From 6ccc33c305c39c1efe474c711bb37d1cfa542b3b Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 4 Jun 2020 00:23:49 +0700 Subject: [PATCH 01/73] Add files via upload --- python/myapp.py | 22 ++++++++++++++++++++++ python/myapp2.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+) create mode 100644 python/myapp.py create mode 100644 python/myapp2.py diff --git a/python/myapp.py b/python/myapp.py new file mode 100644 index 0000000..1f8aa4f --- /dev/null +++ b/python/myapp.py @@ -0,0 +1,22 @@ +import yfinance as yf +import streamlit as st +import pandas as pd + +st.write(""" +# Simple Stock Price App + +Shown are the stock closing price and volume of Google! + +""") + +# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75 +#define the ticker symbol +tickerSymbol = 'GOOGL' +#get data on this ticker +tickerData = yf.Ticker(tickerSymbol) +#get the historical prices for this ticker +tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31') +# Open High Low Close Volume Dividends Stock Splits + +st.line_chart(tickerDf.Close) +st.line_chart(tickerDf.Volume) diff --git a/python/myapp2.py b/python/myapp2.py new file mode 100644 index 0000000..70ba571 --- /dev/null +++ b/python/myapp2.py @@ -0,0 +1,28 @@ +import yfinance as yf +import streamlit as st +import pandas as pd + +st.write(""" +# Simple Stock Price App + +Shown are the stock **closing price** and ***volume*** of Google! + +""") + +# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75 +#define the ticker symbol +tickerSymbol = 'GOOGL' +#get data on this ticker +tickerData = yf.Ticker(tickerSymbol) +#get the historical prices for this ticker +tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31') +# Open High Low Close Volume Dividends Stock Splits + +st.write(""" +## Closing Price +""") +st.line_chart(tickerDf.Close) +st.write(""" +## Volume Price +""") +st.line_chart(tickerDf.Volume) From 500ea8a092f8dfbf5a45e2e5ff30e1731cf824ad Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 7 Jun 2020 10:14:47 +0700 Subject: [PATCH 02/73] Add files via upload --- myapp.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 myapp.py diff --git a/myapp.py b/myapp.py new file mode 100644 index 0000000..1f8aa4f --- /dev/null +++ b/myapp.py @@ -0,0 +1,22 @@ +import yfinance as yf +import streamlit as st +import pandas as pd + +st.write(""" +# Simple Stock Price App + +Shown are the stock closing price and volume of Google! + +""") + +# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75 +#define the ticker symbol +tickerSymbol = 'GOOGL' +#get data on this ticker +tickerData = yf.Ticker(tickerSymbol) +#get the historical prices for this ticker +tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31') +# Open High Low Close Volume Dividends Stock Splits + +st.line_chart(tickerDf.Close) +st.line_chart(tickerDf.Volume) From bb1cf858f140c7e46462d6dfcd588437306b2ebf Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 7 Jun 2020 10:15:08 +0700 Subject: [PATCH 03/73] Rename myapp.py to streamlit/myapp.py --- myapp.py => streamlit/myapp.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename myapp.py => streamlit/myapp.py (100%) diff --git a/myapp.py b/streamlit/myapp.py similarity index 100% rename from myapp.py rename to streamlit/myapp.py From 2661b4d5c9126cdcd6db1b4359d76c9b0994226f Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 7 Jun 2020 10:15:25 +0700 Subject: [PATCH 04/73] Add files via upload --- streamlit/myapp2.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 streamlit/myapp2.py diff --git a/streamlit/myapp2.py b/streamlit/myapp2.py new file mode 100644 index 0000000..70ba571 --- /dev/null +++ b/streamlit/myapp2.py @@ -0,0 +1,28 @@ +import yfinance as yf +import streamlit as st +import pandas as pd + +st.write(""" +# Simple Stock Price App + +Shown are the stock **closing price** and ***volume*** of Google! + +""") + +# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75 +#define the ticker symbol +tickerSymbol = 'GOOGL' +#get data on this ticker +tickerData = yf.Ticker(tickerSymbol) +#get the historical prices for this ticker +tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31') +# Open High Low Close Volume Dividends Stock Splits + +st.write(""" +## Closing Price +""") +st.line_chart(tickerDf.Close) +st.write(""" +## Volume Price +""") +st.line_chart(tickerDf.Volume) From e403ec19d8f9be3e6c010b142f63bcef473fccc0 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 9 Jun 2020 09:27:01 +0700 Subject: [PATCH 05/73] Update myapp.py --- streamlit/myapp.py | 1 - 1 file changed, 1 deletion(-) diff --git a/streamlit/myapp.py b/streamlit/myapp.py index 1f8aa4f..2093c12 100644 --- a/streamlit/myapp.py +++ b/streamlit/myapp.py @@ -1,6 +1,5 @@ import yfinance as yf import streamlit as st -import pandas as pd st.write(""" # Simple Stock Price App From 3ebb6043c2b020cc62dc192819c747b833cda57f Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 9 Jun 2020 09:27:45 +0700 Subject: [PATCH 06/73] Update myapp2.py --- streamlit/myapp2.py | 1 - 1 file changed, 1 deletion(-) diff --git a/streamlit/myapp2.py b/streamlit/myapp2.py index 70ba571..9995946 100644 --- a/streamlit/myapp2.py +++ b/streamlit/myapp2.py @@ -1,6 +1,5 @@ import yfinance as yf import streamlit as st -import pandas as pd st.write(""" # Simple Stock Price App From 4504f157920804a69644aed5c380ec488ba19d3a Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 9 Jun 2020 23:22:15 +0700 Subject: [PATCH 07/73] Add files via upload --- ...inesterase_Bioactivity_Data_Concised.ipynb | 3022 ++++++ ...inesterase_Exploratory_Data_Analysis.ipynb | 3349 +++++++ ...erase_Descriptor_Dataset_Preparation.ipynb | 8786 +++++++++++++++++ 3 files changed, 15157 insertions(+) create mode 100644 python/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb create mode 100644 python/CDD_ML_Part_2_Acetylcholinesterase_Exploratory_Data_Analysis.ipynb create mode 100644 python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb diff --git a/python/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb b/python/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb new file mode 100644 index 0000000..0709fca --- /dev/null +++ b/python/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb @@ -0,0 +1,3022 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "CDD-ML-Part-1-Bioactivity-Data-Concised-Acetylcholinesterase.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "wSFbIMb87cHu", + "colab_type": "text" + }, + "source": [ + "# **Bioinformatics Project - Computational Drug Discovery [Part 1] Download Bioactivity Data (Concised version)**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", + "\n", + "In this Jupyter notebook, we will be building a real-life **data science project** that you can include in your **data science portfolio**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", + "\n", + "In **Part 1**, we will be performing Data Collection and Pre-Processing from the ChEMBL Database.\n", + "\n", + "Note for this Concised Version:\n", + "* Redundant code cells were deleted.\n", + "* Code cells for saving files to Google Drive has been deleted.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3iQiERxumDor", + "colab_type": "text" + }, + "source": [ + "## **ChEMBL Database**\n", + "\n", + "The [*ChEMBL Database*](https://www.ebi.ac.uk/chembl/) is a database that contains curated bioactivity data of more than 2 million compounds. It is compiled from more than 76,000 documents, 1.2 million assays and the data spans 13,000 targets and 1,800 cells and 33,000 indications.\n", + "[Data as of March 25, 2020; ChEMBL version 26]." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iryGAwAIQ4yf", + "colab_type": "text" + }, + "source": [ + "## **Installing libraries**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "toGT1U_B7F2i", + "colab_type": "text" + }, + "source": [ + "Install the ChEMBL web service package so that we can retrieve bioactivity data from the ChEMBL Database." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cJGExHQBfLh7", + "colab_type": "code", + "outputId": "dbeeb7ab-426b-481f-ca15-897f6c09451c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 + } + }, + "source": [ + "! pip install chembl_webresource_client" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting chembl_webresource_client\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/2e/48/0db29040c92726fcc6f99a5bc89e0ea8cf5a9d84753ebaaf53108792da2a/chembl-webresource-client-0.10.2.tar.gz (51kB)\n", + "\r\u001b[K |██████▍ | 10kB 21.5MB/s eta 0:00:01\r\u001b[K |████████████▊ | 20kB 1.7MB/s eta 0:00:01\r\u001b[K |███████████████████ | 30kB 2.5MB/s eta 0:00:01\r\u001b[K |█████████████████████████▍ | 40kB 3.3MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 51kB 2.1MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61kB 2.1MB/s \n", + "\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from chembl_webresource_client) (1.24.3)\n", + "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.6/dist-packages (from chembl_webresource_client) (2.23.0)\n", + "Collecting requests-cache>=0.4.7\n", + " Downloading https://files.pythonhosted.org/packages/7f/55/9b1c40eb83c16d8fc79c5f6c2ffade04208b080670fbfc35e0a5effb5a92/requests_cache-0.5.2-py2.py3-none-any.whl\n", + "Requirement already satisfied: easydict in /usr/local/lib/python3.6/dist-packages (from chembl_webresource_client) (1.9)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.18.4->chembl_webresource_client) (2.9)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.18.4->chembl_webresource_client) (3.0.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.18.4->chembl_webresource_client) (2020.4.5.1)\n", + "Building wheels for collected packages: chembl-webresource-client\n", + " Building wheel for chembl-webresource-client (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for chembl-webresource-client: filename=chembl_webresource_client-0.10.2-cp36-none-any.whl size=55661 sha256=a52d78c7928723755f476d68cdfcd640830e02a8ab2d24d68d28e0d0df1dcf8d\n", + " Stored in directory: /root/.cache/pip/wheels/e6/96/19/3f042bfda7c669bfe24c8278477f57b0fbbf3e488b4c09e3a8\n", + "Successfully built chembl-webresource-client\n", + "Installing collected packages: requests-cache, chembl-webresource-client\n", + "Successfully installed chembl-webresource-client-0.10.2 requests-cache-0.5.2\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J0kJjL8gb5nX", + "colab_type": "text" + }, + "source": [ + "## **Importing libraries**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RXoCvMPPfNrv", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Import necessary libraries\n", + "import pandas as pd\n", + "from chembl_webresource_client.new_client import new_client" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1FgUai1bfigC", + "colab_type": "text" + }, + "source": [ + "## **Search for Target protein**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7lBsDrD0gAqH", + "colab_type": "text" + }, + "source": [ + "### **Target search for Acetylcholinesterase**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vxtp79so4ZjF", + "colab_type": "code", + "outputId": "92e78250-8b71-4912-f0eb-dd48bb563aaa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "# Target search for coronavirus\n", + "target = new_client.target\n", + "target_query = target.search('acetylcholinesterase')\n", + "targets = pd.DataFrame.from_dict(target_query)\n", + "targets" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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cross_referencesorganismpref_namescorespecies_group_flagtarget_chembl_idtarget_componentstarget_typetax_id
0[{'xref_id': 'P22303', 'xref_name': None, 'xre...Homo sapiensAcetylcholinesterase27.0FalseCHEMBL220[{'accession': 'P22303', 'component_descriptio...SINGLE PROTEIN9606
1[]Homo sapiensCholinesterases; ACHE & BCHE27.0FalseCHEMBL2095233[{'accession': 'P06276', 'component_descriptio...SELECTIVITY GROUP9606
2[]Drosophila melanogasterAcetylcholinesterase17.0FalseCHEMBL2242744[{'accession': 'P07140', 'component_descriptio...SINGLE PROTEIN7227
3[]Nephotettix cincticepsAce-orthologous acetylcholinesterase17.0FalseCHEMBL2366514[{'accession': 'Q9NJH6', 'component_descriptio...SINGLE PROTEIN94400
4[{'xref_id': 'P04058', 'xref_name': None, 'xre...Torpedo californicaAcetylcholinesterase15.0FalseCHEMBL4780[{'accession': 'P04058', 'component_descriptio...SINGLE PROTEIN7787
5[{'xref_id': 'P21836', 'xref_name': None, 'xre...Mus musculusAcetylcholinesterase15.0FalseCHEMBL3198[{'accession': 'P21836', 'component_descriptio...SINGLE PROTEIN10090
6[{'xref_id': 'P37136', 'xref_name': None, 'xre...Rattus norvegicusAcetylcholinesterase15.0FalseCHEMBL3199[{'accession': 'P37136', 'component_descriptio...SINGLE PROTEIN10116
7[{'xref_id': 'O42275', 'xref_name': None, 'xre...Electrophorus electricusAcetylcholinesterase15.0FalseCHEMBL4078[{'accession': 'O42275', 'component_descriptio...SINGLE PROTEIN8005
8[{'xref_id': 'P23795', 'xref_name': None, 'xre...Bos taurusAcetylcholinesterase15.0FalseCHEMBL4768[{'accession': 'P23795', 'component_descriptio...SINGLE PROTEIN9913
9[]Anopheles gambiaeAcetylcholinesterase15.0FalseCHEMBL2046266[{'accession': 'Q869C3', 'component_descriptio...SINGLE PROTEIN7165
10[]Leptinotarsa decemlineataAcetylcholinesterase15.0FalseCHEMBL2366490[{'accession': 'Q27677', 'component_descriptio...SINGLE PROTEIN7539
11[]Gallus gallusAcetylcholinesterase15.0FalseCHEMBL3227914[{'accession': 'P36196', 'component_descriptio...SINGLE PROTEIN9031
12[]Danio rerioAcetylcholinesterase15.0FalseCHEMBL3308995[{'accession': 'Q9DDE3', 'component_descriptio...SINGLE PROTEIN7955
13[]Aedes aegyptiAcetylcholinesterase15.0FalseCHEMBL4295607[{'accession': 'Q6A2E2', 'component_descriptio...SINGLE PROTEIN7159
14[{'xref_id': 'Q95P20', 'xref_name': None, 'xre...Musca domesticaAcetylcholinesterase14.0FalseCHEMBL5752[{'accession': 'Q95P20', 'component_descriptio...SINGLE PROTEIN7370
15[]Plutella xylostellaAcetylcholinesterase14.0FalseCHEMBL2242729[{'accession': 'Q8MZM0', 'component_descriptio...SINGLE PROTEIN51655
16[]Musca domesticaAcetylcholinesterase14.0FalseCHEMBL2242743[{'accession': 'Q7YWJ9', 'component_descriptio...SINGLE PROTEIN7370
17[]Pediculus humanus capitisAcetylcholinesterase14.0FalseCHEMBL2362988[{'accession': 'A2PZ98', 'component_descriptio...SINGLE PROTEIN121226
18[]Spodoptera lituraAcetylcholinesterase14.0FalseCHEMBL2366422[{'accession': 'C5IU80', 'component_descriptio...SINGLE PROTEIN69820
19[]Laodelphax striatellaAcetylcholinesterase14.0FalseCHEMBL2366446[{'accession': 'K4JQ36', 'component_descriptio...SINGLE PROTEIN195883
20[]Schizaphis graminumAcetylcholinesterase14.0FalseCHEMBL2366511[{'accession': 'Q9BMJ1', 'component_descriptio...SINGLE PROTEIN13262
21[]Caenorhabditis elegansAcetylcholinesterase 114.0FalseCHEMBL3341583[{'accession': 'P38433', 'component_descriptio...SINGLE PROTEIN6239
22[]Rattus norvegicusAcetylcholinesterase and butyrylcholinesterase...13.0FalseCHEMBL2111475[{'accession': 'P37136', 'component_descriptio...SELECTIVITY GROUP10116
\n", + "
" + ], + "text/plain": [ + " cross_references ... tax_id\n", + "0 [{'xref_id': 'P22303', 'xref_name': None, 'xre... ... 9606\n", + "1 [] ... 9606\n", + "2 [] ... 7227\n", + "3 [] ... 94400\n", + "4 [{'xref_id': 'P04058', 'xref_name': None, 'xre... ... 7787\n", + "5 [{'xref_id': 'P21836', 'xref_name': None, 'xre... ... 10090\n", + "6 [{'xref_id': 'P37136', 'xref_name': None, 'xre... ... 10116\n", + "7 [{'xref_id': 'O42275', 'xref_name': None, 'xre... ... 8005\n", + "8 [{'xref_id': 'P23795', 'xref_name': None, 'xre... ... 9913\n", + "9 [] ... 7165\n", + "10 [] ... 7539\n", + "11 [] ... 9031\n", + "12 [] ... 7955\n", + "13 [] ... 7159\n", + "14 [{'xref_id': 'Q95P20', 'xref_name': None, 'xre... ... 7370\n", + "15 [] ... 51655\n", + "16 [] ... 7370\n", + "17 [] ... 121226\n", + "18 [] ... 69820\n", + "19 [] ... 195883\n", + "20 [] ... 13262\n", + "21 [] ... 6239\n", + "22 [] ... 10116\n", + "\n", + "[23 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y5OPfEALjAfZ", + "colab_type": "text" + }, + "source": [ + "### **Select and retrieve bioactivity data for *Human Acetylcholinesterase* (first entry)**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gSQ3aroOgML7", + "colab_type": "text" + }, + "source": [ + "We will assign the fifth entry (which corresponds to the target protein, *Human Acetylcholinesterase*) to the ***selected_target*** variable " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "StrcHMVLha7u", + "colab_type": "code", + "outputId": "6cd12d1e-46b2-47ce-efcb-7d3c49fa3712", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "selected_target = targets.target_chembl_id[0]\n", + "selected_target" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'CHEMBL220'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GWd2DRalgjzB", + "colab_type": "text" + }, + "source": [ + "Here, we will retrieve only bioactivity data for *Human Acetylcholinesterase* (CHEMBL220) that are reported as pChEMBL values." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LeFbV_CsSP8D", + "colab_type": "code", + "colab": {} + }, + "source": [ + "activity = new_client.activity\n", + "res = activity.filter(target_chembl_id=selected_target).filter(standard_type=\"IC50\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "LrzAcEFnYzBY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df = pd.DataFrame.from_dict(res)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "s9iUAXFdSkoM", + "colab_type": "code", + "outputId": "bb88ea44-9a54-44bf-b178-07558a80edb6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 779 + } + }, + "source": [ + "df" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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activity_commentactivity_idactivity_propertiesassay_chembl_idassay_descriptionassay_typebao_endpointbao_formatbao_labelcanonical_smilesdata_validity_commentdata_validity_descriptiondocument_chembl_iddocument_journaldocument_yearligand_efficiencymolecule_chembl_idmolecule_pref_nameparent_molecule_chembl_idpchembl_valuepotential_duplicatequdt_unitsrecord_idrelationsrc_idstandard_flagstandard_relationstandard_text_valuestandard_typestandard_unitsstandard_upper_valuestandard_valuetarget_chembl_idtarget_organismtarget_pref_nametarget_tax_idtext_valuetoidtypeunitsuo_unitsupper_valuevalue
0None33969[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1NoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '19.61', 'le': '0.36', 'lle': '3.32', ...CHEMBL133897NoneCHEMBL1338976.12Falsehttp://www.openphacts.org/units/Nanomolar252547=1True=NoneIC50nMNone750.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.75
1None37563[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatO=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1NoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '18.57', 'le': '0.38', 'lle': '2.45', ...CHEMBL336398NoneCHEMBL3363987.00Falsehttp://www.openphacts.org/units/Nanomolar252533=1True=NoneIC50nMNone100.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.1
2None37565[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1NoneNoneCHEMBL1148382J. Med. Chem.2004.0NoneCHEMBL131588NoneCHEMBL131588NoneFalsehttp://www.openphacts.org/units/Nanomolar252530>1True>NoneIC50nMNone50000.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None50.0
3None38902[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatO=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)FNoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '16.11', 'le': '0.34', 'lle': '1.81', ...CHEMBL130628NoneCHEMBL1306286.52Falsehttp://www.openphacts.org/units/Nanomolar252534=1True=NoneIC50nMNone300.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.3
4None41170[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)CNoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '17.60', 'le': '0.36', 'lle': '3.00', ...CHEMBL130478NoneCHEMBL1304786.10Falsehttp://www.openphacts.org/units/Nanomolar252552=1True=NoneIC50nMNone800.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.8
....................................................................................................................................
7021None18798886[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '9.75', 'le': '0.21', 'lle': '-1.76', ...CHEMBL4281727NoneCHEMBL42817275.42Falsehttp://www.openphacts.org/units/Nanomolar3124025=1True=NoneIC50nMNone3810.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None3.81
7022None18798887[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '10.47', 'le': '0.20', 'lle': '-0.87',...CHEMBL4292349NoneCHEMBL42923495.46Falsehttp://www.openphacts.org/units/Nanomolar3124026=1True=NoneIC50nMNone3460.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None3.46
7023None18798888[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '11.07', 'le': '0.21', 'lle': '-0.73',...CHEMBL4278260NoneCHEMBL42782605.56Falsehttp://www.openphacts.org/units/Nanomolar3124027=1True=NoneIC50nMNone2780.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.78
7024None18798889[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatO=C(/C=C/c1ccccc1)NCCC1CCN(Cc2ccccc2)CC1NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '18.21', 'le': '0.33', 'lle': '2.23', ...CHEMBL3819548NoneCHEMBL38195486.35Falsehttp://www.openphacts.org/units/Nanomolar3124014=1True=NoneIC50nMNone450.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.45
7025None18798890[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatO=C(/C=C/c1ccc(O)cc1)NCCC1CCN(Cc2ccccc2)CC1NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '15.36', 'le': '0.28', 'lle': '1.78', ...CHEMBL3819320NoneCHEMBL38193205.60Falsehttp://www.openphacts.org/units/Nanomolar3124015=1True=NoneIC50nMNone2510.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.51
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7026 rows × 43 columns

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" + ], + "text/plain": [ + " activity_comment activity_id ... upper_value value\n", + "0 None 33969 ... None 0.75\n", + "1 None 37563 ... None 0.1\n", + "2 None 37565 ... None 50.0\n", + "3 None 38902 ... None 0.3\n", + "4 None 41170 ... None 0.8\n", + "... ... ... ... ... ...\n", + "7021 None 18798886 ... None 3.81\n", + "7022 None 18798887 ... None 3.46\n", + "7023 None 18798888 ... None 2.78\n", + "7024 None 18798889 ... None 0.45\n", + "7025 None 18798890 ... None 2.51\n", + "\n", + "[7026 rows x 43 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fQ78N26Fg15T", + "colab_type": "text" + }, + "source": [ + "Finally we will save the resulting bioactivity data to a CSV file **bioactivity_data.csv**." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZvUUEIVxTOH1", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df.to_csv('acetylcholinesterase_01_bioactivity_data_raw.csv', index=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_GXMpFNUOn_8", + "colab_type": "text" + }, + "source": [ + "## **Handling missing data**\n", + "If any compounds has missing value for the **standard_value** and **canonical_smiles** column then drop it." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hkVOdk6ZR396", + "colab_type": "code", + "outputId": "f8a638fe-75d1-4fae-d4c8-b3b1e6458174", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 813 + } + }, + "source": [ + "df2 = df[df.standard_value.notna()]\n", + "df2 = df2[df.canonical_smiles.notna()]\n", + "df2" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " \n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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activity_commentactivity_idactivity_propertiesassay_chembl_idassay_descriptionassay_typebao_endpointbao_formatbao_labelcanonical_smilesdata_validity_commentdata_validity_descriptiondocument_chembl_iddocument_journaldocument_yearligand_efficiencymolecule_chembl_idmolecule_pref_nameparent_molecule_chembl_idpchembl_valuepotential_duplicatequdt_unitsrecord_idrelationsrc_idstandard_flagstandard_relationstandard_text_valuestandard_typestandard_unitsstandard_upper_valuestandard_valuetarget_chembl_idtarget_organismtarget_pref_nametarget_tax_idtext_valuetoidtypeunitsuo_unitsupper_valuevalue
0None33969[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1NoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '19.61', 'le': '0.36', 'lle': '3.32', ...CHEMBL133897NoneCHEMBL1338976.12Falsehttp://www.openphacts.org/units/Nanomolar252547=1True=NoneIC50nMNone750.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.75
1None37563[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatO=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1NoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '18.57', 'le': '0.38', 'lle': '2.45', ...CHEMBL336398NoneCHEMBL3363987.00Falsehttp://www.openphacts.org/units/Nanomolar252533=1True=NoneIC50nMNone100.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.1
2None37565[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1NoneNoneCHEMBL1148382J. Med. Chem.2004.0NoneCHEMBL131588NoneCHEMBL131588NoneFalsehttp://www.openphacts.org/units/Nanomolar252530>1True>NoneIC50nMNone50000.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None50.0
3None38902[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatO=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)FNoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '16.11', 'le': '0.34', 'lle': '1.81', ...CHEMBL130628NoneCHEMBL1306286.52Falsehttp://www.openphacts.org/units/Nanomolar252534=1True=NoneIC50nMNone300.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.3
4None41170[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)CNoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '17.60', 'le': '0.36', 'lle': '3.00', ...CHEMBL130478NoneCHEMBL1304786.10Falsehttp://www.openphacts.org/units/Nanomolar252552=1True=NoneIC50nMNone800.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.8
....................................................................................................................................
7021None18798886[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '9.75', 'le': '0.21', 'lle': '-1.76', ...CHEMBL4281727NoneCHEMBL42817275.42Falsehttp://www.openphacts.org/units/Nanomolar3124025=1True=NoneIC50nMNone3810.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None3.81
7022None18798887[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '10.47', 'le': '0.20', 'lle': '-0.87',...CHEMBL4292349NoneCHEMBL42923495.46Falsehttp://www.openphacts.org/units/Nanomolar3124026=1True=NoneIC50nMNone3460.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None3.46
7023None18798888[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '11.07', 'le': '0.21', 'lle': '-0.73',...CHEMBL4278260NoneCHEMBL42782605.56Falsehttp://www.openphacts.org/units/Nanomolar3124027=1True=NoneIC50nMNone2780.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.78
7024None18798889[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatO=C(/C=C/c1ccccc1)NCCC1CCN(Cc2ccccc2)CC1NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '18.21', 'le': '0.33', 'lle': '2.23', ...CHEMBL3819548NoneCHEMBL38195486.35Falsehttp://www.openphacts.org/units/Nanomolar3124014=1True=NoneIC50nMNone450.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.45
7025None18798890[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatO=C(/C=C/c1ccc(O)cc1)NCCC1CCN(Cc2ccccc2)CC1NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '15.36', 'le': '0.28', 'lle': '1.78', ...CHEMBL3819320NoneCHEMBL38193205.60Falsehttp://www.openphacts.org/units/Nanomolar3124015=1True=NoneIC50nMNone2510.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.51
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5835 rows × 43 columns

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activity_commentactivity_idactivity_propertiesassay_chembl_idassay_descriptionassay_typebao_endpointbao_formatbao_labelcanonical_smilesdata_validity_commentdata_validity_descriptiondocument_chembl_iddocument_journaldocument_yearligand_efficiencymolecule_chembl_idmolecule_pref_nameparent_molecule_chembl_idpchembl_valuepotential_duplicatequdt_unitsrecord_idrelationsrc_idstandard_flagstandard_relationstandard_text_valuestandard_typestandard_unitsstandard_upper_valuestandard_valuetarget_chembl_idtarget_organismtarget_pref_nametarget_tax_idtext_valuetoidtypeunitsuo_unitsupper_valuevalue
0None33969[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1NoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '19.61', 'le': '0.36', 'lle': '3.32', ...CHEMBL133897NoneCHEMBL1338976.12Falsehttp://www.openphacts.org/units/Nanomolar252547=1True=NoneIC50nMNone750.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.75
1None37563[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatO=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1NoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '18.57', 'le': '0.38', 'lle': '2.45', ...CHEMBL336398NoneCHEMBL3363987.00Falsehttp://www.openphacts.org/units/Nanomolar252533=1True=NoneIC50nMNone100.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.1
2None37565[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1NoneNoneCHEMBL1148382J. Med. Chem.2004.0NoneCHEMBL131588NoneCHEMBL131588NoneFalsehttp://www.openphacts.org/units/Nanomolar252530>1True>NoneIC50nMNone50000.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None50.0
3None38902[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatO=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)FNoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '16.11', 'le': '0.34', 'lle': '1.81', ...CHEMBL130628NoneCHEMBL1306286.52Falsehttp://www.openphacts.org/units/Nanomolar252534=1True=NoneIC50nMNone300.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.3
4None41170[]CHEMBL643384Inhibitory concentration against acetylcholine...BBAO_0000190BAO_0000357single protein formatCSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)CNoneNoneCHEMBL1148382J. Med. Chem.2004.0{'bei': '17.60', 'le': '0.36', 'lle': '3.00', ...CHEMBL130478NoneCHEMBL1304786.10Falsehttp://www.openphacts.org/units/Nanomolar252552=1True=NoneIC50nMNone800.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None0.8
....................................................................................................................................
7019None18798884[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '10.98', 'le': '0.21', 'lle': '-1.46',...CHEMBL4293155NoneCHEMBL42931555.61Falsehttp://www.openphacts.org/units/Nanomolar3124023=1True=NoneIC50nMNone2440.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.44
7020None18798885[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '10.95', 'le': '0.21', 'lle': '-1.47',...CHEMBL4282558NoneCHEMBL42825585.59Falsehttp://www.openphacts.org/units/Nanomolar3124024=1True=NoneIC50nMNone2540.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.54
7021None18798886[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '9.75', 'le': '0.21', 'lle': '-1.76', ...CHEMBL4281727NoneCHEMBL42817275.42Falsehttp://www.openphacts.org/units/Nanomolar3124025=1True=NoneIC50nMNone3810.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None3.81
7022None18798887[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '10.47', 'le': '0.20', 'lle': '-0.87',...CHEMBL4292349NoneCHEMBL42923495.46Falsehttp://www.openphacts.org/units/Nanomolar3124026=1True=NoneIC50nMNone3460.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None3.46
7023None18798888[]CHEMBL4274263Inhibition of human erythrocyte AChE using ace...BBAO_0000190BAO_0000357single protein formatCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...NoneNoneCHEMBL4270597Eur J Med Chem2018.0{'bei': '11.07', 'le': '0.21', 'lle': '-0.73',...CHEMBL4278260NoneCHEMBL42782605.56Falsehttp://www.openphacts.org/units/Nanomolar3124027=1True=NoneIC50nMNone2780.0CHEMBL220Homo sapiensAcetylcholinesterase9606NoneNoneIC50uMUO_0000065None2.78
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4695 rows × 43 columns

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" + ], + "text/plain": [ + " activity_comment activity_id ... upper_value value\n", + "0 None 33969 ... None 0.75\n", + "1 None 37563 ... None 0.1\n", + "2 None 37565 ... None 50.0\n", + "3 None 38902 ... None 0.3\n", + "4 None 41170 ... None 0.8\n", + "... ... ... ... ... ...\n", + "7019 None 18798884 ... None 2.44\n", + "7020 None 18798885 ... None 2.54\n", + "7021 None 18798886 ... None 3.81\n", + "7022 None 18798887 ... None 3.46\n", + "7023 None 18798888 ... None 2.78\n", + "\n", + "[4695 rows x 43 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5H4sSFAWhV9B", + "colab_type": "text" + }, + "source": [ + "## **Data pre-processing of the bioactivity data**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nv2dzid_hzKd", + "colab_type": "text" + }, + "source": [ + "### **Combine the 3 columns (molecule_chembl_id,canonical_smiles,standard_value) and bioactivity_class into a DataFrame**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0NCLYmrASgha", + "colab_type": "code", + "outputId": "d85ee5a6-e7bc-4be6-a621-6df526d065f9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "selection = ['molecule_chembl_id','canonical_smiles','standard_value']\n", + "df3 = df2_nr[selection]\n", + "df3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesstandard_value
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1750.0
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1100.0
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc150000.0
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F300.0
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C800.0
............
7019CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...2440.0
7020CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...2540.0
7021CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...3810.0
7022CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...3460.0
7023CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...2780.0
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4695 rows × 3 columns

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" + ], + "text/plain": [ + " molecule_chembl_id ... standard_value\n", + "0 CHEMBL133897 ... 750.0\n", + "1 CHEMBL336398 ... 100.0\n", + "2 CHEMBL131588 ... 50000.0\n", + "3 CHEMBL130628 ... 300.0\n", + "4 CHEMBL130478 ... 800.0\n", + "... ... ... ...\n", + "7019 CHEMBL4293155 ... 2440.0\n", + "7020 CHEMBL4282558 ... 2540.0\n", + "7021 CHEMBL4281727 ... 3810.0\n", + "7022 CHEMBL4292349 ... 3460.0\n", + "7023 CHEMBL4278260 ... 2780.0\n", + "\n", + "[4695 rows x 3 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d0ObVWGmkj_L", + "colab_type": "text" + }, + "source": [ + "Saves dataframe to CSV file" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pAzJTVu4kMd_", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df3.to_csv('acetylcholinesterase_02_bioactivity_data_preprocessed.csv', index=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tO22XVlzhkXR", + "colab_type": "text" + }, + "source": [ + "### **Labeling compounds as either being active, inactive or intermediate**\n", + "The bioactivity data is in the IC50 unit. Compounds having values of less than 1000 nM will be considered to be **active** while those greater than 10,000 nM will be considered to be **inactive**. As for those values in between 1,000 and 10,000 nM will be referred to as **intermediate**. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ck9Z2M9CkRy_", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df4 = pd.read_csv('acetylcholinesterase_02_bioactivity_data_preprocessed.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1E8rz7oMOd-5", + "colab_type": "code", + "colab": {} + }, + "source": [ + "bioactivity_threshold = []\n", + "for i in df4.standard_value:\n", + " if float(i) >= 10000:\n", + " bioactivity_threshold.append(\"inactive\")\n", + " elif float(i) <= 1000:\n", + " bioactivity_threshold.append(\"active\")\n", + " else:\n", + " bioactivity_threshold.append(\"intermediate\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Li64nUiZQ-y2", + "colab_type": "code", + "outputId": "18546d01-c66f-4f6e-aca8-14562339ba17", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "bioactivity_class = pd.Series(bioactivity_threshold, name='class')\n", + "df5 = pd.concat([df4, bioactivity_class], axis=1)\n", + "df5" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesstandard_valueclass
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1750.0active
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1100.0active
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc150000.0inactive
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F300.0active
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C800.0active
...............
4690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...2440.0intermediate
4691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...2540.0intermediate
4692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...3810.0intermediate
4693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...3460.0intermediate
4694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...2780.0intermediate
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4695 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " molecule_chembl_id ... class\n", + "0 CHEMBL133897 ... active\n", + "1 CHEMBL336398 ... active\n", + "2 CHEMBL131588 ... inactive\n", + "3 CHEMBL130628 ... active\n", + "4 CHEMBL130478 ... active\n", + "... ... ... ...\n", + "4690 CHEMBL4293155 ... intermediate\n", + "4691 CHEMBL4282558 ... intermediate\n", + "4692 CHEMBL4281727 ... intermediate\n", + "4693 CHEMBL4292349 ... intermediate\n", + "4694 CHEMBL4278260 ... intermediate\n", + "\n", + "[4695 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9tlgyexWh7YJ", + "colab_type": "text" + }, + "source": [ + "Saves dataframe to CSV file" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nSNia7suXstR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df5.to_csv('acetylcholinesterase_03_bioactivity_data_curated.csv', index=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0eCQTu9MnzyH", + "colab_type": "code", + "outputId": "bb0dd61b-5a21-44cb-cb12-f356784c7b7e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "source": [ + "! zip acetylcholinesterase.zip *.csv" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " adding: acetylcholinesterase_01_bioactivity_data_raw.csv (deflated 90%)\n", + " adding: acetylcholinesterase_02_bioactivity_data_preprocessed.csv (deflated 81%)\n", + " adding: acetylcholinesterase_03_bioactivity_data_curated.csv (deflated 82%)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UuZf5-MEd-H5", + "colab_type": "code", + "outputId": "d27dac4e-a925-4645-9e0d-ab43127b4eb2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + } + }, + "source": [ + "! ls -l" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "total 4668\n", + "-rw-r--r-- 1 root root 3562870 Jun 1 15:03 acetylcholinesterase_01_bioactivity_data_raw.csv\n", + "-rw-r--r-- 1 root root 342989 Jun 1 15:03 acetylcholinesterase_02_bioactivity_data_preprocessed.csv\n", + "-rw-r--r-- 1 root root 385924 Jun 1 15:03 acetylcholinesterase_03_bioactivity_data_curated.csv\n", + "-rw-r--r-- 1 root root 475843 Jun 1 15:03 acetylcholinesterase.zip\n", + "drwxr-xr-x 1 root root 4096 May 27 16:27 sample_data\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZywB5K_Dlawb", + "colab_type": "text" + }, + "source": [ + "---" + ] + } + ] +} \ No newline at end of file diff --git a/python/CDD_ML_Part_2_Acetylcholinesterase_Exploratory_Data_Analysis.ipynb b/python/CDD_ML_Part_2_Acetylcholinesterase_Exploratory_Data_Analysis.ipynb new file mode 100644 index 0000000..c6f3ea6 --- /dev/null +++ b/python/CDD_ML_Part_2_Acetylcholinesterase_Exploratory_Data_Analysis.ipynb @@ -0,0 +1,3349 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "CDD-ML-Part-2-Exploratory-Data-Analysis-Acetylcholinesterase.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "l0Y7_lgN4jzM", + "colab_type": "text" + }, + "source": [ + "# **Bioinformatics Project - Computational Drug Discovery [Part 2] Exploratory Data Analysis**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", + "\n", + "In this Jupyter notebook, we will be building a real-life **data science project** that you can include in your **data science portfolio**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", + "\n", + "In **Part 2**, we will be performing Descriptor Calculation and Exploratory Data Analysis.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o-4IOizard4P", + "colab_type": "text" + }, + "source": [ + "## **Install conda and rdkit**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H0mjQ2PcrSe5", + "colab_type": "code", + "outputId": "10fb9322-6afb-4873-8c41-0e6bfbe9d0b2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n", + "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n", + "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n", + "! conda install -c rdkit rdkit -y\n", + "import sys\n", + "sys.path.append('/usr/local/lib/python3.7/site-packages/')" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-06-09 15:43:15-- https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n", + "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.130.3, 104.16.131.3, 2606:4700::6810:8203, ...\n", + "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.130.3|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 85055499 (81M) [application/x-sh]\n", + "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’\n", + "\n", + "\r Miniconda 0%[ ] 0 --.-KB/s \r Miniconda3 48%[========> ] 39.18M 196MB/s \r Miniconda3- 96%[==================> ] 78.09M 195MB/s \rMiniconda3-py37_4.8 100%[===================>] 81.12M 195MB/s in 0.4s \n", + "\n", + "2020-06-09 15:43:15 (195 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’ saved [85055499/85055499]\n", + "\n", + "PREFIX=/usr/local\n", + "Unpacking payload ...\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - _libgcc_mutex==0.1=main\n", + " - asn1crypto==1.3.0=py37_0\n", + " - ca-certificates==2020.1.1=0\n", + " - certifi==2019.11.28=py37_0\n", + " - cffi==1.14.0=py37h2e261b9_0\n", + " - chardet==3.0.4=py37_1003\n", + " - conda-package-handling==1.6.0=py37h7b6447c_0\n", + " - conda==4.8.2=py37_0\n", + " - cryptography==2.8=py37h1ba5d50_0\n", + " - idna==2.8=py37_0\n", + " - ld_impl_linux-64==2.33.1=h53a641e_7\n", + " - libedit==3.1.20181209=hc058e9b_0\n", + " - libffi==3.2.1=hd88cf55_4\n", + " - libgcc-ng==9.1.0=hdf63c60_0\n", + " - libstdcxx-ng==9.1.0=hdf63c60_0\n", + " - ncurses==6.2=he6710b0_0\n", + " - openssl==1.1.1d=h7b6447c_4\n", + " - pip==20.0.2=py37_1\n", + " - pycosat==0.6.3=py37h7b6447c_0\n", + " - pycparser==2.19=py37_0\n", + " - pyopenssl==19.1.0=py37_0\n", + " - pysocks==1.7.1=py37_0\n", + " - python==3.7.6=h0371630_2\n", + " - readline==7.0=h7b6447c_5\n", + " - requests==2.22.0=py37_1\n", + " - ruamel_yaml==0.15.87=py37h7b6447c_0\n", + " - setuptools==45.2.0=py37_0\n", + " - six==1.14.0=py37_0\n", + " - sqlite==3.31.1=h7b6447c_0\n", + " - tk==8.6.8=hbc83047_0\n", + " - tqdm==4.42.1=py_0\n", + " - urllib3==1.25.8=py37_0\n", + " - wheel==0.34.2=py37_0\n", + " - xz==5.2.4=h14c3975_4\n", + " - yaml==0.1.7=had09818_2\n", + " - zlib==1.2.11=h7b6447c_3\n", + "\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n", + " asn1crypto pkgs/main/linux-64::asn1crypto-1.3.0-py37_0\n", + " ca-certificates pkgs/main/linux-64::ca-certificates-2020.1.1-0\n", + " certifi pkgs/main/linux-64::certifi-2019.11.28-py37_0\n", + " cffi pkgs/main/linux-64::cffi-1.14.0-py37h2e261b9_0\n", + " chardet pkgs/main/linux-64::chardet-3.0.4-py37_1003\n", + " conda pkgs/main/linux-64::conda-4.8.2-py37_0\n", + " conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n", + " cryptography pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n", + " idna pkgs/main/linux-64::idna-2.8-py37_0\n", + " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n", + " libedit pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n", + " libffi pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n", + " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n", + " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n", + " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_0\n", + " openssl pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_4\n", + " pip pkgs/main/linux-64::pip-20.0.2-py37_1\n", + " pycosat pkgs/main/linux-64::pycosat-0.6.3-py37h7b6447c_0\n", + " pycparser pkgs/main/linux-64::pycparser-2.19-py37_0\n", + " pyopenssl pkgs/main/linux-64::pyopenssl-19.1.0-py37_0\n", + " pysocks pkgs/main/linux-64::pysocks-1.7.1-py37_0\n", + " python pkgs/main/linux-64::python-3.7.6-h0371630_2\n", + " readline pkgs/main/linux-64::readline-7.0-h7b6447c_5\n", + " requests pkgs/main/linux-64::requests-2.22.0-py37_1\n", + " ruamel_yaml pkgs/main/linux-64::ruamel_yaml-0.15.87-py37h7b6447c_0\n", + " setuptools pkgs/main/linux-64::setuptools-45.2.0-py37_0\n", + " six pkgs/main/linux-64::six-1.14.0-py37_0\n", + " sqlite pkgs/main/linux-64::sqlite-3.31.1-h7b6447c_0\n", + " tk pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n", + " tqdm pkgs/main/noarch::tqdm-4.42.1-py_0\n", + " urllib3 pkgs/main/linux-64::urllib3-1.25.8-py37_0\n", + " wheel pkgs/main/linux-64::wheel-0.34.2-py37_0\n", + " xz pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n", + " yaml pkgs/main/linux-64::yaml-0.1.7-had09818_2\n", + " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n", + "\n", + "\n", + "Preparing transaction: | \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "installation finished.\n", + "WARNING:\n", + " You currently have a PYTHONPATH environment variable set. This may cause\n", + " unexpected behavior when running the Python interpreter in Miniconda3.\n", + " For best results, please verify that your PYTHONPATH only points to\n", + " directories of packages that are compatible with the Python interpreter\n", + " in Miniconda3: /usr/local\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n", + "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n", + "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - rdkit\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " blas-1.0 | mkl 6 KB\n", + " bzip2-1.0.8 | h7b6447c_0 78 KB\n", + " cairo-1.14.12 | h8948797_3 906 KB\n", + " certifi-2020.4.5.1 | py37_0 155 KB\n", + " conda-4.8.3 | py37_0 2.8 MB\n", + " fontconfig-2.13.0 | h9420a91_0 227 KB\n", + " freetype-2.9.1 | h8a8886c_1 550 KB\n", + " glib-2.63.1 | h5a9c865_0 2.9 MB\n", + " icu-58.2 | he6710b0_3 10.5 MB\n", + " intel-openmp-2020.1 | 217 780 KB\n", + " jpeg-9b | h024ee3a_2 214 KB\n", + " libboost-1.67.0 | h46d08c1_4 13.0 MB\n", + " libgfortran-ng-7.3.0 | hdf63c60_0 1006 KB\n", + " libpng-1.6.37 | hbc83047_0 278 KB\n", + " libtiff-4.1.0 | h2733197_0 447 KB\n", + " libuuid-1.0.3 | h1bed415_2 15 KB\n", + " libxcb-1.13 | h1bed415_1 421 KB\n", + " libxml2-2.9.9 | hea5a465_1 1.6 MB\n", + " mkl-2020.1 | 217 129.0 MB\n", + " mkl-service-2.3.0 | py37he904b0f_0 218 KB\n", + " mkl_fft-1.0.15 | py37ha843d7b_0 154 KB\n", + " mkl_random-1.1.1 | py37h0573a6f_0 322 KB\n", + " numpy-1.18.1 | py37h4f9e942_0 5 KB\n", + " numpy-base-1.18.1 | py37hde5b4d6_1 4.2 MB\n", + " olefile-0.46 | py37_0 50 KB\n", + " openssl-1.1.1g | h7b6447c_0 2.5 MB\n", + " pandas-1.0.3 | py37h0573a6f_0 8.6 MB\n", + " pcre-8.43 | he6710b0_0 209 KB\n", + " pillow-7.1.2 | py37hb39fc2d_0 603 KB\n", + " pixman-0.38.0 | h7b6447c_0 364 KB\n", + " py-boost-1.67.0 | py37h04863e7_4 278 KB\n", + " python-dateutil-2.8.1 | py_0 215 KB\n", + " pytz-2020.1 | py_0 184 KB\n", + " rdkit-2020.03.2.0 | py37hc20afe1_1 24.8 MB rdkit\n", + " zstd-1.3.7 | h0b5b093_0 401 KB\n", + " ------------------------------------------------------------\n", + " Total: 207.6 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " blas pkgs/main/linux-64::blas-1.0-mkl\n", + " bzip2 pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0\n", + " cairo pkgs/main/linux-64::cairo-1.14.12-h8948797_3\n", + " fontconfig pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0\n", + " freetype pkgs/main/linux-64::freetype-2.9.1-h8a8886c_1\n", + " glib pkgs/main/linux-64::glib-2.63.1-h5a9c865_0\n", + " icu pkgs/main/linux-64::icu-58.2-he6710b0_3\n", + " intel-openmp pkgs/main/linux-64::intel-openmp-2020.1-217\n", + " jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n", + " libboost pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4\n", + " libgfortran-ng pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0\n", + " libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n", + " libtiff pkgs/main/linux-64::libtiff-4.1.0-h2733197_0\n", + " libuuid pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2\n", + " libxcb pkgs/main/linux-64::libxcb-1.13-h1bed415_1\n", + " libxml2 pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1\n", + " mkl pkgs/main/linux-64::mkl-2020.1-217\n", + " mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0\n", + " mkl_fft pkgs/main/linux-64::mkl_fft-1.0.15-py37ha843d7b_0\n", + " mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py37h0573a6f_0\n", + " numpy pkgs/main/linux-64::numpy-1.18.1-py37h4f9e942_0\n", + " numpy-base pkgs/main/linux-64::numpy-base-1.18.1-py37hde5b4d6_1\n", + " olefile pkgs/main/linux-64::olefile-0.46-py37_0\n", + " pandas pkgs/main/linux-64::pandas-1.0.3-py37h0573a6f_0\n", + " pcre pkgs/main/linux-64::pcre-8.43-he6710b0_0\n", + " pillow pkgs/main/linux-64::pillow-7.1.2-py37hb39fc2d_0\n", + " pixman pkgs/main/linux-64::pixman-0.38.0-h7b6447c_0\n", + " py-boost pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4\n", + " python-dateutil pkgs/main/noarch::python-dateutil-2.8.1-py_0\n", + " pytz pkgs/main/noarch::pytz-2020.1-py_0\n", + " rdkit rdkit/linux-64::rdkit-2020.03.2.0-py37hc20afe1_1\n", + " zstd pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " certifi 2019.11.28-py37_0 --> 2020.4.5.1-py37_0\n", + " conda 4.8.2-py37_0 --> 4.8.3-py37_0\n", + " openssl 1.1.1d-h7b6447c_4 --> 1.1.1g-h7b6447c_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "glib-2.63.1 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 1.31it/s] \n", + "libpng-1.6.37 | 278 KB | : 100% 1.0/1 [00:00<00:00, 14.76it/s]\n", + "numpy-base-1.18.1 | 4.2 MB | : 100% 1.0/1 [00:00<00:00, 4.31it/s]\n", + "conda-4.8.3 | 2.8 MB | : 100% 1.0/1 [00:00<00:00, 6.55it/s]\n", + "libxcb-1.13 | 421 KB | : 100% 1.0/1 [00:00<00:00, 12.07it/s]\n", + "certifi-2020.4.5.1 | 155 KB | : 100% 1.0/1 [00:00<00:00, 17.73it/s]\n", + "blas-1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 17.11it/s]\n", + "mkl_random-1.1.1 | 322 KB | : 100% 1.0/1 [00:00<00:00, 9.32it/s]\n", + "libboost-1.67.0 | 13.0 MB | : 100% 1.0/1 [00:01<00:00, 1.99s/it] \n", + "jpeg-9b | 214 KB | : 100% 1.0/1 [00:00<00:00, 17.02it/s]\n", + "cairo-1.14.12 | 906 KB | : 100% 1.0/1 [00:00<00:00, 11.96it/s]\n", + "fontconfig-2.13.0 | 227 KB | : 100% 1.0/1 [00:00<00:00, 13.67it/s]\n", + "intel-openmp-2020.1 | 780 KB | : 100% 1.0/1 [00:00<00:00, 13.54it/s]\n", + "freetype-2.9.1 | 550 KB | : 100% 1.0/1 [00:00<00:00, 11.17it/s]\n", + "pandas-1.0.3 | 8.6 MB | : 100% 1.0/1 [00:00<00:00, 2.07it/s]\n", + 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"base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "! wget https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_03_bioactivity_data_curated.csv" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-06-09 15:45:12-- https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_03_bioactivity_data_curated.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 385924 (377K) [text/plain]\n", + "Saving to: ‘acetylcholinesterase_03_bioactivity_data_curated.csv’\n", + "\n", + "\r acetylcho 0%[ ] 0 --.-KB/s \racetylcholinesteras 100%[===================>] 376.88K --.-KB/s in 0.03s \n", + "\n", + "2020-06-09 15:45:13 (10.7 MB/s) - ‘acetylcholinesterase_03_bioactivity_data_curated.csv’ saved [385924/385924]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Fpu5C7HlwV9s", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GCcE8J5XwjtB", + "colab_type": "code", + "outputId": "40a10f00-3d5f-4835-fdf8-2cb5acd8683c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df = pd.read_csv('acetylcholinesterase_03_bioactivity_data_curated.csv')\n", + "df" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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4690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...2440.0intermediate
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4CHEMBL130478800.0activeCSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C
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4690CHEMBL42931552440.0intermediateCC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...
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4695 rows × 4 columns

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" + ], + "text/plain": [ + " molecule_chembl_id ... canonical_smiles\n", + "0 CHEMBL133897 ... CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1\n", + "1 CHEMBL336398 ... O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1\n", + "2 CHEMBL131588 ... CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1\n", + "3 CHEMBL130628 ... O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F\n", + "4 CHEMBL130478 ... CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C\n", + "... ... ... ...\n", + "4690 CHEMBL4293155 ... CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...\n", + "4691 CHEMBL4282558 ... CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...\n", + "4692 CHEMBL4281727 ... CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...\n", + "4693 CHEMBL4292349 ... CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...\n", + "4694 CHEMBL4278260 ... CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...\n", + "\n", + "[4695 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YzN_S4Quro5S", + "colab_type": "text" + }, + "source": [ + "## **Calculate Lipinski descriptors**\n", + "Christopher Lipinski, a scientist at Pfizer, came up with a set of rule-of-thumb for evaluating the **druglikeness** of compounds. Such druglikeness is based on the Absorption, Distribution, Metabolism and Excretion (ADME) that is also known as the pharmacokinetic profile. Lipinski analyzed all orally active FDA-approved drugs in the formulation of what is to be known as the **Rule-of-Five** or **Lipinski's Rule**.\n", + "\n", + "The Lipinski's Rule stated the following:\n", + "* Molecular weight < 500 Dalton\n", + "* Octanol-water partition coefficient (LogP) < 5\n", + "* Hydrogen bond donors < 5\n", + "* Hydrogen bond acceptors < 10 " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9qn_eQcnxY7C", + "colab_type": "text" + }, + "source": [ + "### **Import libraries**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CgBjIdT-rnRU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "from rdkit import Chem\n", + "from rdkit.Chem import Descriptors, Lipinski" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JsgTV-ByxdMa", + "colab_type": "text" + }, + "source": [ + "### **Calculate descriptors**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bCXEY7a9ugO_", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n", + "\n", + "def lipinski(smiles, verbose=False):\n", + "\n", + " moldata= []\n", + " for elem in smiles:\n", + " mol=Chem.MolFromSmiles(elem) \n", + " moldata.append(mol)\n", + " \n", + " baseData= np.arange(1,1)\n", + " i=0 \n", + " for mol in moldata: \n", + " \n", + " desc_MolWt = Descriptors.MolWt(mol)\n", + " desc_MolLogP = Descriptors.MolLogP(mol)\n", + " desc_NumHDonors = Lipinski.NumHDonors(mol)\n", + " desc_NumHAcceptors = Lipinski.NumHAcceptors(mol)\n", + " \n", + " row = np.array([desc_MolWt,\n", + " desc_MolLogP,\n", + " desc_NumHDonors,\n", + " desc_NumHAcceptors]) \n", + " \n", + " if(i==0):\n", + " baseData=row\n", + " else:\n", + " baseData=np.vstack([baseData, row])\n", + " i=i+1 \n", + " \n", + " columnNames=[\"MW\",\"LogP\",\"NumHDonors\",\"NumHAcceptors\"] \n", + " descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n", + " \n", + " return descriptors" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ThFIFw8IukMY", + "colab_type": "code", + "outputId": "574c050c-44f1-43be-bf6c-bf973d8d0675", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df_lipinski = lipinski(df_clean_smiles.canonical_smiles)\n", + "df_lipinski" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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MWLogPNumHDonorsNumHAcceptors
0312.3252.803200.06.0
1376.9134.554600.05.0
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4695 rows × 4 columns

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" + ], + "text/plain": [ + " MW LogP NumHDonors NumHAcceptors\n", + "0 312.325 2.80320 0.0 6.0\n", + "1 376.913 4.55460 0.0 5.0\n", + "2 426.851 5.35740 0.0 5.0\n", + "3 404.845 4.70690 0.0 5.0\n", + "4 346.334 3.09530 0.0 6.0\n", + "... ... ... ... ...\n", + "4690 511.150 7.07230 2.0 3.0\n", + "4691 511.150 7.07230 2.0 3.0\n", + "4692 555.601 7.18140 2.0 3.0\n", + "4693 521.702 6.32710 2.0 5.0\n", + "4694 501.715 6.29058 2.0 4.0\n", + "\n", + "[4695 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gUMlPfFrxicj", + "colab_type": "text" + }, + "source": [ + "### **Combine DataFrames**\n", + "\n", + "Let's take a look at the 2 DataFrames that will be combined." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DaezyM5vwp9n", + "colab_type": "code", + "outputId": "14dfe606-85e1-43ca-aaec-feb5b59f5404", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df_lipinski" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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MWLogPNumHDonorsNumHAcceptors
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1376.9134.554600.05.0
2426.8515.357400.05.0
3404.8454.706900.05.0
4346.3343.095300.06.0
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4695 rows × 4 columns

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" + ], + "text/plain": [ + " MW LogP NumHDonors NumHAcceptors\n", + "0 312.325 2.80320 0.0 6.0\n", + "1 376.913 4.55460 0.0 5.0\n", + "2 426.851 5.35740 0.0 5.0\n", + "3 404.845 4.70690 0.0 5.0\n", + "4 346.334 3.09530 0.0 6.0\n", + "... ... ... ... ...\n", + "4690 511.150 7.07230 2.0 3.0\n", + "4691 511.150 7.07230 2.0 3.0\n", + "4692 555.601 7.18140 2.0 3.0\n", + "4693 521.702 6.32710 2.0 5.0\n", + "4694 501.715 6.29058 2.0 4.0\n", + "\n", + "[4695 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9-ChzM8_wuq_", + "colab_type": "code", + "outputId": "64a6a0c0-e29a-4cdd-c9ea-9b102b63cfe2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesstandard_valueclass
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1750.0active
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1100.0active
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc150000.0inactive
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F300.0active
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C800.0active
...............
4690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...2440.0intermediate
4691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...2540.0intermediate
4692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...3810.0intermediate
4693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...3460.0intermediate
4694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...2780.0intermediate
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4695 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " molecule_chembl_id ... class\n", + "0 CHEMBL133897 ... active\n", + "1 CHEMBL336398 ... active\n", + "2 CHEMBL131588 ... inactive\n", + "3 CHEMBL130628 ... active\n", + "4 CHEMBL130478 ... active\n", + "... ... ... ...\n", + "4690 CHEMBL4293155 ... intermediate\n", + "4691 CHEMBL4282558 ... intermediate\n", + "4692 CHEMBL4281727 ... intermediate\n", + "4693 CHEMBL4292349 ... intermediate\n", + "4694 CHEMBL4278260 ... intermediate\n", + "\n", + "[4695 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eET6iZ1Aw3oe", + "colab_type": "text" + }, + "source": [ + "Now, let's combine the 2 DataFrame" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "L9nUZC0Ww3gp", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df_combined = pd.concat([df,df_lipinski], axis=1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FRBfBP3QxFJp", + "colab_type": "code", + "outputId": "33528544-8108-4303-a9f2-977c0f6aef91", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df_combined" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesstandard_valueclassMWLogPNumHDonorsNumHAcceptors
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1750.0active312.3252.803200.06.0
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1100.0active376.9134.554600.05.0
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc150000.0inactive426.8515.357400.05.0
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F300.0active404.8454.706900.05.0
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C800.0active346.3343.095300.06.0
...........................
4690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...2440.0intermediate511.1507.072302.03.0
4691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...2540.0intermediate511.1507.072302.03.0
4692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...3810.0intermediate555.6017.181402.03.0
4693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...3460.0intermediate521.7026.327102.05.0
4694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...2780.0intermediate501.7156.290582.04.0
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4695 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " molecule_chembl_id ... NumHAcceptors\n", + "0 CHEMBL133897 ... 6.0\n", + "1 CHEMBL336398 ... 5.0\n", + "2 CHEMBL131588 ... 5.0\n", + "3 CHEMBL130628 ... 5.0\n", + "4 CHEMBL130478 ... 6.0\n", + "... ... ... ...\n", + "4690 CHEMBL4293155 ... 3.0\n", + "4691 CHEMBL4282558 ... 3.0\n", + "4692 CHEMBL4281727 ... 3.0\n", + "4693 CHEMBL4292349 ... 5.0\n", + "4694 CHEMBL4278260 ... 4.0\n", + "\n", + "[4695 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e0MLOedB6j96", + "colab_type": "text" + }, + "source": [ + "### **Convert IC50 to pIC50**\n", + "To allow **IC50** data to be more uniformly distributed, we will convert **IC50** to the negative logarithmic scale which is essentially **-log10(IC50)**.\n", + "\n", + "This custom function pIC50() will accept a DataFrame as input and will:\n", + "* Take the IC50 values from the ``standard_value`` column and converts it from nM to M by multiplying the value by 10$^{-9}$\n", + "* Take the molar value and apply -log10\n", + "* Delete the ``standard_value`` column and create a new ``pIC50`` column" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UXMuFQoQ4pZF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# https://github.com/chaninlab/estrogen-receptor-alpha-qsar/blob/master/02_ER_alpha_RO5.ipynb\n", + "\n", + "import numpy as np\n", + "\n", + "def pIC50(input):\n", + " pIC50 = []\n", + "\n", + " for i in input['standard_value_norm']:\n", + " molar = i*(10**-9) # Converts nM to M\n", + " pIC50.append(-np.log10(molar))\n", + "\n", + " input['pIC50'] = pIC50\n", + " x = input.drop('standard_value_norm', 1)\n", + " \n", + " return x" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WU5Fh1h2OaJJ", + "colab_type": "text" + }, + "source": [ + "Point to note: Values greater than 100,000,000 will be fixed at 100,000,000 otherwise the negative logarithmic value will become negative." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QuUTFUpcR1wU", + "colab_type": "code", + "outputId": "3e3fe22d-8587-42e3-c92e-396d37877589", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + } + }, + "source": [ + "df_combined.standard_value.describe()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 4.695000e+03\n", + "mean 3.210931e+12\n", + "std 1.189580e+14\n", + "min 6.000000e-03\n", + "25% 1.250000e+02\n", + "50% 2.246000e+03\n", + "75% 1.753000e+04\n", + "max 5.888437e+15\n", + "Name: standard_value, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QyiJ0to5N6Z_", + "colab_type": "code", + "outputId": "58b38ec2-8f7d-4f44-cc35-1ef394f57854", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "-np.log10( (10**-9)* 100000000 )" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9S1aJkOYOP6K", + "colab_type": "code", + "outputId": "0d9c6937-7c36-4844-f84b-9a95a22802dc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "-np.log10( (10**-9)* 10000000000 )" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "-1.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iktHDDwtPDwl", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def norm_value(input):\n", + " norm = []\n", + "\n", + " for i in input['standard_value']:\n", + " if i > 100000000:\n", + " i = 100000000\n", + " norm.append(i)\n", + "\n", + " input['standard_value_norm'] = norm\n", + " x = input.drop('standard_value', 1)\n", + " \n", + " return x" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EkrTs7RfPsrH", + "colab_type": "text" + }, + "source": [ + "We will first apply the norm_value() function so that the values in the standard_value column is normalized." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EX2Mj2-ZP1Rj", + "colab_type": "code", + "outputId": "8e07791e-d693-4d1b-9f82-f396d34c6338", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df_norm = norm_value(df_combined)\n", + "df_norm" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesclassMWLogPNumHDonorsNumHAcceptorsstandard_value_norm
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1active312.3252.803200.06.0750.0
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1active376.9134.554600.05.0100.0
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1inactive426.8515.357400.05.050000.0
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)Factive404.8454.706900.05.0300.0
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)Cactive346.3343.095300.06.0800.0
...........................
4690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...intermediate511.1507.072302.03.02440.0
4691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...intermediate511.1507.072302.03.02540.0
4692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...intermediate555.6017.181402.03.03810.0
4693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...intermediate521.7026.327102.05.03460.0
4694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...intermediate501.7156.290582.04.02780.0
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4695 rows × 8 columns

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" + ], + "text/plain": [ + " molecule_chembl_id ... standard_value_norm\n", + "0 CHEMBL133897 ... 750.0\n", + "1 CHEMBL336398 ... 100.0\n", + "2 CHEMBL131588 ... 50000.0\n", + "3 CHEMBL130628 ... 300.0\n", + "4 CHEMBL130478 ... 800.0\n", + "... ... ... ...\n", + "4690 CHEMBL4293155 ... 2440.0\n", + "4691 CHEMBL4282558 ... 2540.0\n", + "4692 CHEMBL4281727 ... 3810.0\n", + "4693 CHEMBL4292349 ... 3460.0\n", + "4694 CHEMBL4278260 ... 2780.0\n", + "\n", + "[4695 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hb1eKrIjRiH9", + "colab_type": "code", + "outputId": "8435d28b-9774-4567-8134-51bfc3fc8271", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + } + }, + "source": [ + "df_norm.standard_value_norm.describe()" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 4.695000e+03\n", + "mean 3.276494e+05\n", + "std 4.718369e+06\n", + "min 6.000000e-03\n", + "25% 1.250000e+02\n", + "50% 2.246000e+03\n", + "75% 1.753000e+04\n", + "max 1.000000e+08\n", + "Name: standard_value_norm, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UDKZzmK57YnS", + "colab_type": "code", + "outputId": "89ec9de3-c71e-44fb-86a5-3b4172cf9e63", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df_final = pIC50(df_norm)\n", + "df_final" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesclassMWLogPNumHDonorsNumHAcceptorspIC50
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1active312.3252.803200.06.06.124939
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1active376.9134.554600.05.07.000000
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1inactive426.8515.357400.05.04.301030
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)Factive404.8454.706900.05.06.522879
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)Cactive346.3343.095300.06.06.096910
...........................
4690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...intermediate511.1507.072302.03.05.612610
4691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...intermediate511.1507.072302.03.05.595166
4692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...intermediate555.6017.181402.03.05.419075
4693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...intermediate521.7026.327102.05.05.460924
4694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...intermediate501.7156.290582.04.05.555955
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4695 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " molecule_chembl_id ... pIC50\n", + "0 CHEMBL133897 ... 6.124939\n", + "1 CHEMBL336398 ... 7.000000\n", + "2 CHEMBL131588 ... 4.301030\n", + "3 CHEMBL130628 ... 6.522879\n", + "4 CHEMBL130478 ... 6.096910\n", + "... ... ... ...\n", + "4690 CHEMBL4293155 ... 5.612610\n", + "4691 CHEMBL4282558 ... 5.595166\n", + "4692 CHEMBL4281727 ... 5.419075\n", + "4693 CHEMBL4292349 ... 5.460924\n", + "4694 CHEMBL4278260 ... 5.555955\n", + "\n", + "[4695 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BoqY53udSTYC", + "colab_type": "code", + "outputId": "13f3b5ff-acf5-4136-b8a3-f494066d4824", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + } + }, + "source": [ + "df_final.pIC50.describe()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 4695.000000\n", + "mean 5.820864\n", + "std 1.552813\n", + "min 1.000000\n", + "25% 4.756219\n", + "50% 5.648590\n", + "75% 6.903090\n", + "max 11.221849\n", + "Name: pIC50, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TTB-xkOcgwRX", + "colab_type": "text" + }, + "source": [ + "Let's write this to CSV file." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sbZfHUSOgykw", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df_final.to_csv('acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "05vHBWvqaQtb", + "colab_type": "text" + }, + "source": [ + "### **Removing the 'intermediate' bioactivity class**\n", + "Here, we will be removing the ``intermediate`` class from our data set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HmrndhDW3c7Z", + "colab_type": "code", + "outputId": "dbc2255b-ed9c-460c-c12c-1795a735c9e7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df_2class = df_final[df_final['class'] != 'intermediate']\n", + "df_2class" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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molecule_chembl_idcanonical_smilesclassMWLogPNumHDonorsNumHAcceptorspIC50
0CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1active312.3252.80320.06.06.124939
1CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1active376.9134.55460.05.07.000000
2CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1inactive426.8515.35740.05.04.301030
3CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)Factive404.8454.70690.05.06.522879
4CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)Cactive346.3343.09530.06.06.096910
...........................
4675CHEMBL4284261CCN(C)Cc1cc(N)ccc1O.Cl.Clinactive180.2511.42612.03.03.015428
4676CHEMBL4276921CN(C)Cc1cc(N)ccc1O.Cl.Clinactive166.2241.03602.03.02.813467
4677CHEMBL4292574CNCc1cc(N)ccc1O.Cl.Clinactive152.1970.69383.03.03.476904
4685CHEMBL4292766CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3F)CC2...active494.6956.55802.03.06.124939
4687CHEMBL4284475CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(F)cc3)C...active494.6956.55802.03.06.008774
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3549 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " molecule_chembl_id ... pIC50\n", + "0 CHEMBL133897 ... 6.124939\n", + "1 CHEMBL336398 ... 7.000000\n", + "2 CHEMBL131588 ... 4.301030\n", + "3 CHEMBL130628 ... 6.522879\n", + "4 CHEMBL130478 ... 6.096910\n", + "... ... ... ...\n", + "4675 CHEMBL4284261 ... 3.015428\n", + "4676 CHEMBL4276921 ... 2.813467\n", + "4677 CHEMBL4292574 ... 3.476904\n", + "4685 CHEMBL4292766 ... 6.124939\n", + "4687 CHEMBL4284475 ... 6.008774\n", + "\n", + "[3549 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GJGRNXXigd1o", + "colab_type": "text" + }, + "source": [ + "Let's write this to CSV file." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XIiYdeL5ghCo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df_2class.to_csv('acetylcholinesterase_05_bioactivity_data_2class_pIC50.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p9vA4-hQQ8sA", + "colab_type": "text" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x0vqbQWfxsZu", + "colab_type": "text" + }, + "source": [ + "## **Exploratory Data Analysis (Chemical Space Analysis) via Lipinski descriptors**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "18heJagiyHoF", + "colab_type": "text" + }, + "source": [ + "### **Import library**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0Egq_rNsxtIj", + "colab_type": "code", + "outputId": "33657ad8-3fcc-41cd-83ab-6f4f8c0a9e84", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + } + }, + "source": [ + "import seaborn as sns\n", + "sns.set(style='ticks')\n", + "import matplotlib.pyplot as plt" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NiarmFbOdG3H", + "colab_type": "text" + }, + "source": [ + "### **Frequency plot of the 2 bioactivity classes**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "w2Ia0iycdMO2", + "colab_type": "code", + "outputId": "fa37bcfd-af06-4e4b-c754-b6fb2b1b4f97", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.countplot(x='class', data=df_2class, edgecolor='black')\n", + "\n", + "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n", + "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n", + "\n", + "plt.savefig('plot_bioactivity_class.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wB68NKVG0j68", + "colab_type": "text" + }, + "source": [ + "### **Scatter plot of MW versus LogP**\n", + "\n", + "It can be seen that the 2 bioactivity classes are spanning similar chemical spaces as evident by the scatter plot of MW vs LogP." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "F79BNwjF0nub", + "colab_type": "code", + "outputId": "a80f9843-848a-4eb0-ff78-e9473d5d2d4b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.scatterplot(x='MW', y='LogP', data=df_2class, hue='class', size='pIC50', edgecolor='black', alpha=0.7)\n", + "\n", + "plt.xlabel('MW', fontsize=14, fontweight='bold')\n", + "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)\n", + "plt.savefig('plot_MW_vs_LogP.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oLAfyRwHyJfX", + "colab_type": "text" + }, + "source": [ + "### **Box plots**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1n1uIAivyOkY", + "colab_type": "text" + }, + "source": [ + "#### **pIC50 value**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EpPviw0hxue6", + "colab_type": "code", + "outputId": "a6386fee-ae04-4bf0-93cb-c95bcf801432", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.boxplot(x = 'class', y = 'pIC50', data = df_2class)\n", + "\n", + "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n", + "plt.ylabel('pIC50 value', fontsize=14, fontweight='bold')\n", + "\n", + "plt.savefig('plot_ic50.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PsOqKyysCZCv", + "colab_type": "text" + }, + "source": [ + "**Statistical analysis | Mann-Whitney U Test**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LPdV1vDnWBsh", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def mannwhitney(descriptor, verbose=False):\n", + " # https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\n", + " from numpy.random import seed\n", + " from numpy.random import randn\n", + " from scipy.stats import mannwhitneyu\n", + "\n", + "# seed the random number generator\n", + " seed(1)\n", + "\n", + "# actives and inactives\n", + " selection = [descriptor, 'class']\n", + " df = df_2class[selection]\n", + " active = df[df['class'] == 'active']\n", + " active = active[descriptor]\n", + "\n", + " selection = [descriptor, 'class']\n", + " df = df_2class[selection]\n", + " inactive = df[df['class'] == 'inactive']\n", + " inactive = inactive[descriptor]\n", + "\n", + "# compare samples\n", + " stat, p = mannwhitneyu(active, inactive)\n", + " #print('Statistics=%.3f, p=%.3f' % (stat, p))\n", + "\n", + "# interpret\n", + " alpha = 0.05\n", + " if p > alpha:\n", + " interpretation = 'Same distribution (fail to reject H0)'\n", + " else:\n", + " interpretation = 'Different distribution (reject H0)'\n", + " \n", + " results = pd.DataFrame({'Descriptor':descriptor,\n", + " 'Statistics':stat,\n", + " 'p':p,\n", + " 'alpha':alpha,\n", + " 'Interpretation':interpretation}, index=[0])\n", + " filename = 'mannwhitneyu_' + descriptor + '.csv'\n", + " results.to_csv(filename)\n", + "\n", + " return results" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HZmUgOmdYVm5", + "colab_type": "code", + "outputId": "3a9ba656-596c-434a-e52f-5696325cd4e7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + } + }, + "source": [ + "mannwhitney('pIC50')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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DescriptorStatisticspalphaInterpretation
0pIC500.00.00.05Different distribution (reject H0)
\n", + "
" + ], + "text/plain": [ + " Descriptor Statistics p alpha Interpretation\n", + "0 pIC50 0.0 0.0 0.05 Different distribution (reject H0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o2UlCwPmyTBq", + "colab_type": "text" + }, + "source": [ + "#### **MW**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZNlEEsDEx3m6", + "colab_type": "code", + "outputId": "819f291c-d893-4ff6-f4ca-28b46351e999", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 370 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.boxplot(x = 'class', y = 'MW', data = df_2class)\n", + "\n", + "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n", + "plt.ylabel('MW', fontsize=14, fontweight='bold')\n", + "\n", + "plt.savefig('plot_MW.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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DescriptorStatisticspalphaInterpretation
0MW1058779.02.072256e-570.05Different distribution (reject H0)
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" + ], + "text/plain": [ + " Descriptor Statistics ... alpha Interpretation\n", + "0 MW 1058779.0 ... 0.05 Different distribution (reject H0)\n", + "\n", + "[1 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 34 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z5hyBhGqyc6J", + "colab_type": "text" + }, + "source": [ + "#### **LogP**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "liEtkpI4yX9t", + "colab_type": "code", + "outputId": "63ed7b14-b112-44d5-8f0d-8fe017ec0d43", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.boxplot(x = 'class', y = 'LogP', data = df_2class)\n", + "\n", + "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n", + "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n", + "\n", + "plt.savefig('plot_LogP.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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DescriptorStatisticspalphaInterpretation
0LogP1041900.02.318667e-610.05Different distribution (reject H0)
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" + ], + "text/plain": [ + " Descriptor Statistics ... alpha Interpretation\n", + "0 LogP 1041900.0 ... 0.05 Different distribution (reject H0)\n", + "\n", + "[1 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4db7LZLRym2k", + "colab_type": "text" + }, + "source": [ + "#### **NumHDonors**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iru1JPM1yg5A", + "colab_type": "code", + "outputId": "838b8622-b34a-4f93-bec1-9df19fc9f571", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.boxplot(x = 'class', y = 'NumHDonors', data = df_2class)\n", + "\n", + "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n", + "plt.ylabel('NumHDonors', fontsize=14, fontweight='bold')\n", + "\n", + "plt.savefig('plot_NumHDonors.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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DescriptorStatisticspalphaInterpretation
0NumHDonors1361005.52.520096e-100.05Different distribution (reject H0)
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" + ], + "text/plain": [ + " Descriptor Statistics ... alpha Interpretation\n", + "0 NumHDonors 1361005.5 ... 0.05 Different distribution (reject H0)\n", + "\n", + "[1 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yOYQ3QiSyu7-", + "colab_type": "text" + }, + "source": [ + "#### **NumHAcceptors**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yCw6tgNCyxHf", + "colab_type": "code", + "outputId": "3de01d96-1e31-4f95-8084-907f15901106", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } + }, + "source": [ + "plt.figure(figsize=(5.5, 5.5))\n", + "\n", + "sns.boxplot(x = 'class', y = 'NumHAcceptors', data = df_2class)\n", + "\n", + "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n", + "plt.ylabel('NumHAcceptors', fontsize=14, fontweight='bold')\n", + "\n", + "plt.savefig('plot_NumHAcceptors.pdf')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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DescriptorStatisticspalphaInterpretation
0NumHAcceptors1407572.50.0000040.05Different distribution (reject H0)
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" + ], + "text/plain": [ + " Descriptor Statistics ... alpha Interpretation\n", + "0 NumHAcceptors 1407572.5 ... 0.05 Different distribution (reject H0)\n", + "\n", + "[1 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p4QjdHVjKYum", + "colab_type": "text" + }, + "source": [ + "#### **Interpretation of Statistical Results**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mSoOBIqfLbDs", + "colab_type": "text" + }, + "source": [ + "##### **Box Plots**\n", + "\n", + "###### **pIC50 values**\n", + "\n", + "Taking a look at pIC50 values, the **actives** and **inactives** displayed ***statistically significant difference***, which is to be expected since threshold values (``IC50 < 1,000 nM = Actives while IC50 > 10,000 nM = Inactives``, corresponding to ``pIC50 > 6 = Actives and pIC50 < 5 = Inactives``) were used to define actives and inactives.\n", + "\n", + "###### **Lipinski's descriptors**\n", + "\n", + "All of the 4 Lipinski's descriptors exhibited ***statistically significant difference*** between the **actives** and **inactives**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U-rK8l0wWnKK", + "colab_type": "text" + }, + "source": [ + "## **Zip files**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GW1ZSsfJWqbM", + "colab_type": "code", + "outputId": "cee2f037-c743-4059-9822-c87eba3f0c51", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + } + }, + "source": [ + "! zip -r results.zip . -i *.csv *.pdf" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " adding: mannwhitneyu_pIC50.csv (deflated 14%)\n", + " adding: plot_LogP.pdf (deflated 37%)\n", + " adding: 05_acetylcholinesterase_bioactivity_data_2class_pIC50.csv (deflated 76%)\n", + " adding: mannwhitneyu_MW.csv (deflated 10%)\n", + " adding: mannwhitneyu_NumHDonors.csv (deflated 11%)\n", + " adding: acetylcholinesterase_03_bioactivity_data_curated.csv (deflated 82%)\n", + " adding: plot_MW_vs_LogP.pdf (deflated 2%)\n", + " adding: plot_NumHAcceptors.pdf (deflated 38%)\n", + " adding: plot_NumHDonors.pdf (deflated 38%)\n", + " adding: mannwhitneyu_LogP.csv (deflated 7%)\n", + " adding: plot_ic50.pdf (deflated 37%)\n", + " adding: plot_MW.pdf (deflated 38%)\n", + " adding: mannwhitneyu_NumHAcceptors.csv (deflated 11%)\n", + " adding: 04_acetylcholinesterase_bioactivity_data_3class_pIC50.csv (deflated 76%)\n", + " adding: plot_bioactivity_class.pdf (deflated 39%)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LMWOG2UIXEg-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb b/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb new file mode 100644 index 0000000..fcb96a1 --- /dev/null +++ b/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb @@ -0,0 +1,8786 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "CDD-ML-Part-3-Model-Building-Acetylcholinesterase.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "l0Y7_lgN4jzM", + "colab_type": "text" + }, + "source": [ + "# **Bioinformatics Project - Computational Drug Discovery [Part 3] Descriptor Calculation and Dataset Preparation**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", + "\n", + "In this Jupyter notebook, we will be building a real-life **data science project** that you can include in your **data science portfolio**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", + "\n", + "In **Part 3**, we will be calculating molecular descriptors that are essentially quantitative description of the compounds in the dataset. Finally, we will be preparing this into a dataset for subsequent model building in Part 4.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o-4IOizard4P", + "colab_type": "text" + }, + "source": [ + "## **Download PaDEL-Descriptor**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H0mjQ2PcrSe5", + "colab_type": "code", + "outputId": "f89487f7-e16e-4c1b-9ae9-22a82e3b4213", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561 + } + }, + "source": [ + "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n", + "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-06-09 14:27:17-- https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n", + "Resolving github.com (github.com)... 140.82.113.4\n", + "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip [following]\n", + "--2020-06-09 14:27:17-- https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 25768637 (25M) [application/zip]\n", + "Saving to: ‘padel.zip’\n", + "\n", + "padel.zip 100%[===================>] 24.57M 33.2MB/s in 0.7s \n", + "\n", + "2020-06-09 14:27:21 (33.2 MB/s) - ‘padel.zip’ saved [25768637/25768637]\n", + "\n", + "--2020-06-09 14:27:23-- https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh\n", + "Resolving github.com (github.com)... 140.82.113.4\n", + "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh [following]\n", + "--2020-06-09 14:27:24-- https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 231 [text/plain]\n", + "Saving to: ‘padel.sh’\n", + "\n", + "padel.sh 100%[===================>] 231 --.-KB/s in 0s \n", + "\n", + "2020-06-09 14:27:24 (12.5 MB/s) - ‘padel.sh’ saved [231/231]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "64HnTL4tS-nA", + "colab_type": "code", + "outputId": "e0b9c445-e681-4328-908d-d0338c0da805", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "! unzip padel.zip" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Archive: padel.zip\n", + " creating: PaDEL-Descriptor/\n", + " inflating: __MACOSX/._PaDEL-Descriptor \n", + " inflating: PaDEL-Descriptor/MACCSFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._MACCSFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/AtomPairs2DFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._AtomPairs2DFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/EStateFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._EStateFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/Fingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._Fingerprinter.xml \n", + " inflating: PaDEL-Descriptor/.DS_Store \n", + " inflating: __MACOSX/PaDEL-Descriptor/._.DS_Store \n", + " creating: PaDEL-Descriptor/license/\n", + " inflating: __MACOSX/PaDEL-Descriptor/._license \n", + " inflating: PaDEL-Descriptor/KlekotaRothFingerprintCount.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._KlekotaRothFingerprintCount.xml \n", + " inflating: PaDEL-Descriptor/config \n", + " inflating: __MACOSX/PaDEL-Descriptor/._config \n", + " inflating: PaDEL-Descriptor/PubchemFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._PubchemFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/ExtendedFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._ExtendedFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/KlekotaRothFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._KlekotaRothFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/GraphOnlyFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._GraphOnlyFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/SubstructureFingerprinter.xml \n", + " inflating: 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inflating: PaDEL-Descriptor/lib/libPaDEL-Descriptor.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Descriptor.jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL-Jobs(4).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Jobs(4).jar \n", + " inflating: PaDEL-Descriptor/lib/cdk-1.4.15.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._cdk-1.4.15.jar \n", + " inflating: PaDEL-Descriptor/lib/ambit2-smarts-2.4.7-SNAPSHOT(5).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-smarts-2.4.7-SNAPSHOT(5).jar \n", + " inflating: PaDEL-Descriptor/lib/ambit2-core-2.4.7-SNAPSHOT(1).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-core-2.4.7-SNAPSHOT(1).jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL-Jobs(8).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Jobs(8).jar \n", + " inflating: PaDEL-Descriptor/lib/jgrapht-0.6.0(6).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jgrapht-0.6.0(6).jar \n", + " inflating: PaDEL-Descriptor/lib/jama(2).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jama(2).jar \n", + " inflating: PaDEL-Descriptor/lib/jama(3).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jama(3).jar \n", + " inflating: PaDEL-Descriptor/lib/commons-cli-1.2(1).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._commons-cli-1.2(1).jar \n", + " inflating: PaDEL-Descriptor/lib/guava-17.0.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._guava-17.0.jar \n", + " inflating: PaDEL-Descriptor/lib/ambit2-smarts-2.4.7-SNAPSHOT(4).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-smarts-2.4.7-SNAPSHOT(4).jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL-Jobs(5).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Jobs(5).jar \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QmxXXFa4wTNG", + "colab_type": "text" + }, + "source": [ + "## **Load bioactivity data**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fcBvxkPWKFRV", + "colab_type": "text" + }, + "source": [ + "Download the curated ChEMBL bioactivity data that has been pre-processed from Parts 1 and 2 of this Bioinformatics Project series. Here we will be using the **bioactivity_data_3class_pIC50.csv** file that essentially contain the pIC50 values that we will be using for building a regression model." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JBs7QdJoubWC", + "colab_type": "code", + "outputId": "51ee5be0-78c8-4815-ecf9-2dfc981a6d8e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "! wget https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-06-09 15:50:01-- https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 655414 (640K) [text/plain]\n", + "Saving to: ‘acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv.1’\n", + "\n", + "\r acetylcho 0%[ ] 0 --.-KB/s \racetylcholinesteras 100%[===================>] 640.05K --.-KB/s in 0.1s \n", + "\n", + "2020-06-09 15:50:02 (6.33 MB/s) - ‘acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv.1’ saved [655414/655414]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Fpu5C7HlwV9s", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GCcE8J5XwjtB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df3 = pd.read_csv('acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "60z_N6egNiSJ", + "colab_type": "code", + "outputId": "b931eadc-81e2-44a9-d63b-6a8a442e1061", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + } + }, + "source": [ + "df3" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Unnamed: 0molecule_chembl_idcanonical_smilesclassMWLogPNumHDonorsNumHAcceptorspIC50
00CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1active312.3252.803200.06.06.124939
11CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1active376.9134.554600.05.07.000000
22CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1inactive426.8515.357400.05.04.301030
33CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)Factive404.8454.706900.05.06.522879
44CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)Cactive346.3343.095300.06.06.096910
..............................
46904690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...intermediate511.1507.072302.03.05.612610
46914691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...intermediate511.1507.072302.03.05.595166
46924692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...intermediate555.6017.181402.03.05.419075
46934693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...intermediate521.7026.327102.05.05.460924
46944694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...intermediate501.7156.290582.04.05.555955
\n", + "

4695 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 molecule_chembl_id ... NumHAcceptors pIC50\n", + "0 0 CHEMBL133897 ... 6.0 6.124939\n", + "1 1 CHEMBL336398 ... 5.0 7.000000\n", + "2 2 CHEMBL131588 ... 5.0 4.301030\n", + "3 3 CHEMBL130628 ... 5.0 6.522879\n", + "4 4 CHEMBL130478 ... 6.0 6.096910\n", + "... ... ... ... ... ...\n", + "4690 4690 CHEMBL4293155 ... 3.0 5.612610\n", + "4691 4691 CHEMBL4282558 ... 3.0 5.595166\n", + "4692 4692 CHEMBL4281727 ... 3.0 5.419075\n", + "4693 4693 CHEMBL4292349 ... 5.0 5.460924\n", + "4694 4694 CHEMBL4278260 ... 4.0 5.555955\n", + "\n", + "[4695 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BJDJkY43R-9F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "selection = ['canonical_smiles','molecule_chembl_id']\n", + "df3_selection = df3[selection]\n", + "df3_selection.to_csv('molecule.smi', sep='\\t', index=False, header=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nRSCoPVDSkf5", + "colab_type": "code", + "outputId": "c9e9f7ed-3cb1-43c5-ae7b-a23456f408d8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + } + }, + "source": [ + "! cat molecule.smi | head -5" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1\tCHEMBL133897\n", + "O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1\tCHEMBL336398\n", + "CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1\tCHEMBL131588\n", + "O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F\tCHEMBL130628\n", + "CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C\tCHEMBL130478\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GlYaJ9pzUGjS", + "colab_type": "code", + "outputId": "0c694214-bcfb-4715-8ff1-231f99ea8351", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "! cat molecule.smi | wc -l" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "text": [ + "4695\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YzN_S4Quro5S", + "colab_type": "text" + }, + "source": [ + "## **Calculate fingerprint descriptors**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JsgTV-ByxdMa", + "colab_type": "text" + }, + "source": [ + "### **Calculate PaDEL descriptors**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6kN9jrGpS5nE", + "colab_type": "code", + "outputId": "c8edf24d-8126-4c21-e686-898838e47350", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "! bash padel.sh" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Processing CHEMBL133897 in molecule.smi (1/4695). \n", + "Processing CHEMBL336398 in molecule.smi (2/4695). \n", + "Processing CHEMBL131588 in molecule.smi (3/4695). Average speed: 5.49 s/mol.\n", + "Processing CHEMBL130628 in molecule.smi (4/4695). Average speed: 2.77 s/mol.\n", + "Processing CHEMBL130478 in molecule.smi (5/4695). Average speed: 2.18 s/mol.\n", + "Processing CHEMBL130112 in molecule.smi (6/4695). Average speed: 1.70 s/mol.\n", + "Processing CHEMBL130098 in molecule.smi (7/4695). Average speed: 1.45 s/mol.\n", + "Processing CHEMBL337486 in molecule.smi (8/4695). Average speed: 1.27 s/mol.\n", + "Processing CHEMBL336538 in molecule.smi (9/4695). Average speed: 1.13 s/mol.\n", + "Processing CHEMBL131051 in molecule.smi (10/4695). Average speed: 1.04 s/mol.\n", + "Processing CHEMBL341437 in molecule.smi (11/4695). Average speed: 0.94 s/mol.\n", + "Processing CHEMBL122983 in molecule.smi (13/4695). Average speed: 0.92 s/mol.\n", + "Processing CHEMBL335033 in molecule.smi (12/4695). Average speed: 0.91 s/mol.\n", + "Processing CHEMBL339995 in molecule.smi (15/4695). Average speed: 0.78 s/mol.\n", + "Processing CHEMBL338720 in molecule.smi (14/4695). Average speed: 0.81 s/mol.\n", + "Processing CHEMBL131536 in molecule.smi (17/4695). Average speed: 0.76 s/mol.\n", + "Processing CHEMBL335158 in molecule.smi (16/4695). Average speed: 0.76 s/mol.\n", + "Processing CHEMBL106126 in molecule.smi (18/4695). Average speed: 0.70 s/mol.\n", + "Processing CHEMBL334971 in molecule.smi (19/4695). Average speed: 0.66 s/mol.\n", + "Processing CHEMBL336625 in molecule.smi (20/4695). Average speed: 0.64 s/mol.\n", + "Processing CHEMBL130666 in molecule.smi (21/4695). Average speed: 0.63 s/mol.\n", + "Processing CHEMBL134061 in molecule.smi (22/4695). Average speed: 0.61 s/mol.\n", + "Processing CHEMBL133388 in molecule.smi (23/4695). Average speed: 0.59 s/mol.\n", + "Processing CHEMBL130645 in molecule.smi (24/4695). Average speed: 0.57 s/mol.\n", + "Processing CHEMBL133580 in molecule.smi (25/4695). Average speed: 0.55 s/mol.\n", + "Processing CHEMBL336524 in molecule.smi (26/4695). Average speed: 0.54 s/mol.\n", + "Processing CHEMBL336276 in molecule.smi (27/4695). Average speed: 0.52 s/mol.\n", + "Processing CHEMBL334395 in molecule.smi (28/4695). Average speed: 0.51 s/mol.\n", + "Processing CHEMBL131320 in molecule.smi (29/4695). Average speed: 0.50 s/mol.\n", + "Processing CHEMBL337714 in molecule.smi (31/4695). Average speed: 0.47 s/mol.\n", + "Processing CHEMBL339297 in molecule.smi (30/4695). Average speed: 0.48 s/mol.\n", + "Processing CHEMBL122575 in molecule.smi (32/4695). Average speed: 0.47 s/mol.\n", + "Processing CHEMBL130704 in molecule.smi (33/4695). Average speed: 0.45 s/mol.\n", + "Processing CHEMBL46151 in molecule.smi (34/4695). Average speed: 0.45 s/mol.\n", + "Processing CHEMBL54126 in molecule.smi (35/4695). Average speed: 0.44 s/mol.\n", + "Processing CHEMBL297316 in molecule.smi (36/4695). Average speed: 0.43 s/mol.\n", + "Processing CHEMBL65667 in molecule.smi (37/4695). Average speed: 0.42 s/mol.\n", + "Processing CHEMBL431519 in molecule.smi (38/4695). Average speed: 0.41 s/mol.\n", + "Processing CHEMBL296429 in molecule.smi (39/4695). Average speed: 0.41 s/mol.\n", + "Processing CHEMBL154972 in molecule.smi (40/4695). Average speed: 0.40 s/mol.\n", + "Processing CHEMBL152722 in molecule.smi (41/4695). Average speed: 0.40 s/mol.\n", + "Processing CHEMBL349127 in molecule.smi (43/4695). Average speed: 0.39 s/mol.\n", + "Processing CHEMBL544022 in molecule.smi (42/4695). Average speed: 0.39 s/mol.\n", + "Processing CHEMBL156659 in molecule.smi (44/4695). Average speed: 0.38 s/mol.\n", + "Processing CHEMBL45118 in molecule.smi (45/4695). Average speed: 0.37 s/mol.\n", + "Processing CHEMBL542609 in molecule.smi (46/4695). Average speed: 0.37 s/mol.\n", + "Processing CHEMBL155322 in molecule.smi (47/4695). Average speed: 0.36 s/mol.\n", + "Processing CHEMBL1203537 in molecule.smi (49/4695). Average speed: 0.35 s/mol.\n", + "Processing CHEMBL154211 in molecule.smi (48/4695). Average speed: 0.36 s/mol.\n", + "Processing CHEMBL47375 in molecule.smi (51/4695). Average speed: 0.34 s/mol.\n", + "Processing CHEMBL86 in molecule.smi (50/4695). Average speed: 0.35 s/mol.\n", + "Processing CHEMBL542360 in molecule.smi (52/4695). Average speed: 0.34 s/mol.\n", + "Processing CHEMBL154689 in molecule.smi (54/4695). Average speed: 0.33 s/mol.\n", + "Processing CHEMBL345849 in molecule.smi (53/4695). Average speed: 0.33 s/mol.\n", + "Processing CHEMBL346733 in molecule.smi (55/4695). Average speed: 0.33 s/mol.\n", + "Processing CHEMBL416464 in molecule.smi (56/4695). Average speed: 0.32 s/mol.\n", + "Processing CHEMBL1203539 in molecule.smi (57/4695). Average speed: 0.32 s/mol.\n", + "Processing CHEMBL347729 in molecule.smi (58/4695). Average speed: 0.31 s/mol.\n", + "Processing CHEMBL296786 in molecule.smi (59/4695). Average speed: 0.31 s/mol.\n", + "Processing CHEMBL63173 in molecule.smi (60/4695). Average speed: 0.30 s/mol.\n", + "Processing CHEMBL433041 in molecule.smi (61/4695). Average speed: 0.30 s/mol.\n", + "Processing CHEMBL33051 in molecule.smi (63/4695). Average speed: 0.30 s/mol.\n", + "Processing CHEMBL292788 in molecule.smi (62/4695). Average speed: 0.30 s/mol.\n", + "Processing CHEMBL155392 in molecule.smi (64/4695). Average speed: 0.30 s/mol.\n", + "Processing CHEMBL543546 in molecule.smi (66/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL153311 in molecule.smi (65/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL1420 in molecule.smi (67/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL556172 in molecule.smi (68/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL544669 in molecule.smi (70/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL154263 in molecule.smi (69/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL62664 in molecule.smi (71/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL155111 in molecule.smi (72/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL1203534 in molecule.smi (73/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL541044 in molecule.smi (74/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL543779 in molecule.smi (75/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL155495 in molecule.smi (76/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL64011 in molecule.smi (77/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL65585 in molecule.smi (78/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL347539 in molecule.smi (79/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL154433 in molecule.smi (80/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL157208 in molecule.smi (81/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL1790041 in molecule.smi (82/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL65356 in molecule.smi (83/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL154366 in molecule.smi (84/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL153821 in molecule.smi (85/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL416644 in molecule.smi (86/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL94 in molecule.smi (87/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL65018 in molecule.smi (89/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL303574 in molecule.smi (88/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL296277 in molecule.smi (90/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL66309 in molecule.smi (91/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1203535 in molecule.smi (92/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL296560 in molecule.smi (93/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL152260 in molecule.smi (94/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL154950 in molecule.smi (95/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL296669 in molecule.smi (96/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1203538 in molecule.smi (97/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL19060 in molecule.smi (98/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL422977 in molecule.smi (99/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL302179 in molecule.smi (101/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL11805 in molecule.smi (100/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL27673 in molecule.smi (102/4695). 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Average speed: 0.24 s/mol.\n", + "Processing CHEMBL50608 in molecule.smi (114/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL29300 in molecule.smi (115/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL27675 in molecule.smi (116/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL435373 in molecule.smi (117/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL60167 in molecule.smi (118/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL89012 in molecule.smi (119/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL553212 in molecule.smi (121/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL314800 in molecule.smi (120/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL156630 in molecule.smi (122/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL315013 in molecule.smi (123/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL155754 in molecule.smi (124/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL87170 in molecule.smi (126/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL314658 in molecule.smi (125/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL87959 in molecule.smi (127/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87295 in molecule.smi (128/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL82810 in molecule.smi (129/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL157182 in molecule.smi (130/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL88049 in molecule.smi (131/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87128 in molecule.smi (132/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL160219 in molecule.smi (133/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL445916 in molecule.smi (134/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL85473 in molecule.smi (135/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL314502 in molecule.smi (136/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87320 in molecule.smi (137/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL348607 in molecule.smi (139/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87280 in molecule.smi (138/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL313809 in molecule.smi (140/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL314398 in molecule.smi (142/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87849 in molecule.smi (141/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL156931 in molecule.smi (144/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87373 in molecule.smi (143/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL83005 in molecule.smi (145/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL315515 in molecule.smi (146/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL441632 in molecule.smi (147/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL441808 in molecule.smi (149/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87937 in molecule.smi (148/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87649 in molecule.smi (150/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL156861 in molecule.smi (152/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL160836 in molecule.smi (151/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL314766 in molecule.smi (153/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL88026 in molecule.smi (154/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL86233 in molecule.smi (155/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL155350 in molecule.smi (156/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL155863 in molecule.smi (157/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87980 in molecule.smi (158/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL87441 in molecule.smi (159/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL158946 in molecule.smi (160/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL609440 in molecule.smi (162/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL87550 in molecule.smi (161/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL350464 in molecule.smi (163/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL313271 in molecule.smi (164/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL423313 in molecule.smi (165/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL309667 in molecule.smi (166/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL80221 in molecule.smi (168/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL419470 in molecule.smi (167/4695). 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Average speed: 0.23 s/mol.\n", + "Processing CHEMBL51934 in molecule.smi (180/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL352500 in molecule.smi (181/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL292314 in molecule.smi (182/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL355740 in molecule.smi (183/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL54727 in molecule.smi (184/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL75919 in molecule.smi (185/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL309351 in molecule.smi (186/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL76135 in molecule.smi (188/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL75743 in molecule.smi (187/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL440497 in molecule.smi (189/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL62084 in molecule.smi (191/4695). 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Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74926 in molecule.smi (268/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340542 in molecule.smi (269/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128894 in molecule.smi (270/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL337002 in molecule.smi (272/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340866 in molecule.smi (271/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL422971 in molecule.smi (273/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340599 in molecule.smi (275/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340854 in molecule.smi (274/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128292 in molecule.smi (276/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL348318 in molecule.smi (277/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL263322 in molecule.smi (278/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340216 in molecule.smi (279/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL131286 in molecule.smi (280/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL131318 in molecule.smi (281/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340929 in molecule.smi (282/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL156157 in molecule.smi (283/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL129684 in molecule.smi (284/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL155121 in molecule.smi (285/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL153865 in molecule.smi (286/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL157410 in molecule.smi (287/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL158232 in molecule.smi (289/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL155945 in molecule.smi (288/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340409 in molecule.smi (290/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL335329 in molecule.smi (291/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL155768 in molecule.smi (292/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL130831 in molecule.smi (293/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL155746 in molecule.smi (294/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL347354 in molecule.smi (295/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL130738 in molecule.smi (296/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL158174 in molecule.smi (297/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL345241 in molecule.smi (298/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL156763 in molecule.smi (299/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL343237 in molecule.smi (300/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL345905 in molecule.smi (301/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128172 in molecule.smi (302/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL157017 in molecule.smi (303/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL155128 in molecule.smi (304/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL345847 in molecule.smi (305/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL156874 in molecule.smi (307/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL158252 in molecule.smi (306/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL445124 in molecule.smi (309/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL341251 in molecule.smi (308/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL553187 in molecule.smi (311/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL92142 in molecule.smi (310/4695). 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Average speed: 0.21 s/mol.\n", + "Processing CHEMBL433379 in molecule.smi (345/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL129837 in molecule.smi (346/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL328102 in molecule.smi (347/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL130458 in molecule.smi (348/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL92775 in molecule.smi (349/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL92736 in molecule.smi (350/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL91291 in molecule.smi (351/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL413765 in molecule.smi (352/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL359198 in molecule.smi (353/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL609671 in molecule.smi (354/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL609672 in molecule.smi (355/4695). 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Average speed: 0.20 s/mol.\n", + "Processing CHEMBL340427 in molecule.smi (368/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL127465 in molecule.smi (367/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL544592 in molecule.smi (369/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL542220 in molecule.smi (371/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL124933 in molecule.smi (370/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL127857 in molecule.smi (372/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL557405 in molecule.smi (373/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL340228 in molecule.smi (374/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL544126 in molecule.smi (375/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL543655 in molecule.smi (376/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL124156 in molecule.smi (377/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340171 in molecule.smi (378/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL539045 in molecule.smi (379/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL553803 in molecule.smi (380/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL544823 in molecule.smi (381/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL99581 in molecule.smi (382/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL174228 in molecule.smi (383/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL173935 in molecule.smi (384/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL176521 in molecule.smi (385/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL319541 in molecule.smi (386/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL79012 in molecule.smi (387/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL99655 in molecule.smi (388/4695). 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Average speed: 0.21 s/mol.\n", + "Processing CHEMBL284938 in molecule.smi (522/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL33098 in molecule.smi (521/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL32966 in molecule.smi (523/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL287691 in molecule.smi (525/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL33173 in molecule.smi (524/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL286690 in molecule.smi (527/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL32543 in molecule.smi (526/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL32453 in molecule.smi (529/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL33727 in molecule.smi (528/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL33161 in molecule.smi (531/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL62373 in molecule.smi (530/4695). 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Average speed: 0.18 s/mol.\n", + "Processing CHEMBL106936 in molecule.smi (751/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL314989 in molecule.smi (754/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL75013 in molecule.smi (753/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL106344 in molecule.smi (755/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL542297 in molecule.smi (756/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL87273 in molecule.smi (757/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL86031 in molecule.smi (758/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL316973 in molecule.smi (759/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL313582 in molecule.smi (760/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL108163 in molecule.smi (761/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL611808 in molecule.smi (762/4695). 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Average speed: 0.18 s/mol.\n", + "Processing CHEMBL544396 in molecule.smi (796/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1788306 in molecule.smi (797/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL151225 in molecule.smi (798/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL356385 in molecule.smi (799/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL148771 in molecule.smi (800/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1788298 in molecule.smi (801/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL347503 in molecule.smi (802/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL543218 in molecule.smi (803/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL542016 in molecule.smi (804/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL545566 in molecule.smi (805/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL543217 in molecule.smi (806/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL146873 in molecule.smi (807/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL554179 in molecule.smi (808/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL556147 in molecule.smi (809/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL544398 in molecule.smi (810/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL555641 in molecule.smi (811/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL538071 in molecule.smi (812/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL541363 in molecule.smi (813/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL540352 in molecule.smi (814/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL2093015 in molecule.smi (815/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL347527 in molecule.smi (816/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL542265 in molecule.smi (817/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL543220 in molecule.smi (818/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL2093014 in molecule.smi (819/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL556161 in molecule.smi (820/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL151368 in molecule.smi (821/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL2093013 in molecule.smi (822/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL541885 in molecule.smi (823/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL147524 in molecule.smi (824/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL147031 in molecule.smi (825/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL544166 in molecule.smi (826/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL359288 in molecule.smi (827/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL357953 in molecule.smi (828/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL539571 in molecule.smi (829/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL450931 in molecule.smi (830/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL358026 in molecule.smi (831/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL2093017 in molecule.smi (832/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL148879 in molecule.smi (833/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL543216 in molecule.smi (835/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL151194 in molecule.smi (834/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL151388 in molecule.smi (836/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL554056 in molecule.smi (837/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL544165 in molecule.smi (838/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL542981 in molecule.smi (839/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL554453 in molecule.smi (840/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL2093019 in molecule.smi (841/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL545798 in molecule.smi (842/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1788296 in molecule.smi (843/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL541620 in molecule.smi (844/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL554055 in molecule.smi (845/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL555592 in molecule.smi (846/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL556028 in molecule.smi (847/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL147033 in molecule.smi (848/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL151861 in molecule.smi (849/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL545797 in molecule.smi (850/4695). 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Average speed: 0.18 s/mol.\n", + "Processing CHEMBL343365 in molecule.smi (885/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL292421 in molecule.smi (884/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL335133 in molecule.smi (886/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL12858 in molecule.smi (887/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL424100 in molecule.smi (888/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL13166 in molecule.smi (889/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL13122 in molecule.smi (890/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL137721 in molecule.smi (891/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL139685 in molecule.smi (892/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL342245 in molecule.smi (893/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL275537 in molecule.smi (894/4695). 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Average speed: 0.18 s/mol.\n", + "Processing CHEMBL434042 in molecule.smi (906/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL136052 in molecule.smi (907/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL413549 in molecule.smi (908/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL12472 in molecule.smi (909/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL421920 in molecule.smi (910/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL136317 in molecule.smi (911/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL302168 in molecule.smi (912/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1202417 in molecule.smi (913/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL61274 in molecule.smi (914/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL136686 in molecule.smi (915/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL342394 in molecule.smi (916/4695). 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Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161674 in molecule.smi (939/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161714 in molecule.smi (940/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL143812 in molecule.smi (942/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161715 in molecule.smi (941/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161719 in molecule.smi (943/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161720 in molecule.smi (944/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161718 in molecule.smi (946/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161717 in molecule.smi (945/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188823 in molecule.smi (948/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL1161716 in molecule.smi (947/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL185799 in molecule.smi (950/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL426441 in molecule.smi (949/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL186144 in molecule.smi (951/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL361869 in molecule.smi (952/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL362711 in molecule.smi (954/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189283 in molecule.smi (953/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL185976 in molecule.smi (955/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188325 in molecule.smi (956/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187812 in molecule.smi (957/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191729 in molecule.smi (958/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL364125 in molecule.smi (959/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL610243 in molecule.smi (960/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL372491 in molecule.smi (961/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL364585 in molecule.smi (962/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188917 in molecule.smi (963/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188338 in molecule.smi (964/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL366114 in molecule.smi (965/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188753 in molecule.smi (966/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189122 in molecule.smi (967/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189517 in molecule.smi (968/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL365904 in molecule.smi (969/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL186264 in molecule.smi (970/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187818 in molecule.smi (971/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188011 in molecule.smi (972/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL362752 in molecule.smi (973/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL610242 in molecule.smi (974/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL362350 in molecule.smi (975/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL359570 in molecule.smi (976/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187603 in molecule.smi (977/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL360832 in molecule.smi (978/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL186090 in molecule.smi (979/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187591 in molecule.smi (980/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188714 in molecule.smi (981/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187330 in molecule.smi (982/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189774 in molecule.smi (983/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL186042 in molecule.smi (984/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL363455 in molecule.smi (985/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189906 in molecule.smi (987/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL188274 in molecule.smi (986/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL190322 in molecule.smi (989/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189907 in molecule.smi (988/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187870 in molecule.smi (991/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL371787 in molecule.smi (990/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL187385 in molecule.smi (992/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189957 in molecule.smi (994/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL363557 in molecule.smi (993/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL190060 in molecule.smi (996/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191416 in molecule.smi (995/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL190528 in molecule.smi (998/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191386 in molecule.smi (997/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL365678 in molecule.smi (1000/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL364545 in molecule.smi (999/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL370961 in molecule.smi (1002/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191314 in molecule.smi (1001/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL372710 in molecule.smi (1003/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191461 in molecule.smi (1005/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL364678 in molecule.smi (1004/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL365136 in molecule.smi (1007/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL363588 in molecule.smi (1006/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL189797 in molecule.smi (1009/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL371798 in molecule.smi (1008/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL372071 in molecule.smi (1010/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL372279 in molecule.smi (1012/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL192953 in molecule.smi (1011/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191930 in molecule.smi (1014/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191315 in molecule.smi (1013/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL434948 in molecule.smi (1015/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL372262 in molecule.smi (1017/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL190152 in molecule.smi (1016/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191084 in molecule.smi (1018/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL195418 in molecule.smi (1020/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL32823 in molecule.smi (1019/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL192682 in molecule.smi (1022/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL195241 in molecule.smi (1021/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL195179 in molecule.smi (1023/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL425739 in molecule.smi (1025/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL424833 in molecule.smi (1024/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL195067 in molecule.smi (1027/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL278963 in molecule.smi (1026/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL363391 in molecule.smi (1029/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL194823 in molecule.smi (1028/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL134342 in molecule.smi (1031/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL191758 in molecule.smi (1030/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL555666 in molecule.smi (1032/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL557155 in molecule.smi (1033/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL534932 in molecule.smi (1034/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL2113368 in molecule.smi (1035/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL555214 in molecule.smi (1036/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL538573 in molecule.smi (1037/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL539599 in molecule.smi (1038/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL2113367 in molecule.smi (1039/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL536047 in molecule.smi (1040/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL371276 in molecule.smi (1041/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL196401 in molecule.smi (1042/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL196116 in molecule.smi (1043/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL371006 in molecule.smi (1044/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL196177 in molecule.smi (1045/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL196699 in molecule.smi (1046/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL371651 in molecule.smi (1047/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL370561 in molecule.smi (1048/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL1385 in molecule.smi (1049/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL194372 in molecule.smi (1050/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL197696 in molecule.smi (1051/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL199670 in molecule.smi (1052/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL370807 in molecule.smi (1053/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL372202 in molecule.smi (1054/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL382260 in molecule.smi (1055/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL199585 in molecule.smi (1056/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL202661 in molecule.smi (1057/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL381499 in molecule.smi (1058/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL204895 in molecule.smi (1059/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL382207 in molecule.smi (1060/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205335 in molecule.smi (1061/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205895 in molecule.smi (1062/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL381926 in molecule.smi (1063/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205940 in molecule.smi (1064/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205231 in molecule.smi (1065/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL381782 in molecule.smi (1066/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205340 in molecule.smi (1067/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL382351 in molecule.smi (1068/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205321 in molecule.smi (1069/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL207777 in molecule.smi (1070/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL378404 in molecule.smi (1071/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL205967 in molecule.smi (1072/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL380830 in molecule.smi (1073/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL206237 in molecule.smi (1074/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL206093 in molecule.smi (1075/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL377283 in molecule.smi (1077/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL426674 in molecule.smi (1076/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL494887 in molecule.smi (1078/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL464538 in molecule.smi (1079/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL19224 in molecule.smi (1081/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL383242 in molecule.smi (1080/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL208132 in molecule.smi (1083/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL207931 in molecule.smi (1082/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211500 in molecule.smi (1085/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211383 in molecule.smi (1084/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212526 in molecule.smi (1087/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211450 in molecule.smi (1086/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL378763 in molecule.smi (1088/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL378762 in molecule.smi (1089/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212374 in molecule.smi (1091/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL379950 in molecule.smi (1090/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL379065 in molecule.smi (1093/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL213281 in molecule.smi (1092/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212001 in molecule.smi (1094/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212220 in molecule.smi (1095/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL439508 in molecule.smi (1096/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL213861 in molecule.smi (1097/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL377041 in molecule.smi (1098/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212105 in molecule.smi (1099/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL213240 in molecule.smi (1100/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211529 in molecule.smi (1101/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL213404 in molecule.smi (1102/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL377618 in molecule.smi (1103/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212377 in molecule.smi (1104/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL379935 in molecule.smi (1105/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL213964 in molecule.smi (1106/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL379563 in molecule.smi (1107/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL214160 in molecule.smi (1108/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL213944 in molecule.smi (1109/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212771 in molecule.smi (1110/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211652 in molecule.smi (1111/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211596 in molecule.smi (1112/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211943 in molecule.smi (1113/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211897 in molecule.smi (1114/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL208888 in molecule.smi (1115/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL377431 in molecule.smi (1116/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212572 in molecule.smi (1117/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL376425 in molecule.smi (1118/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL377037 in molecule.smi (1119/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL378050 in molecule.smi (1120/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212065 in molecule.smi (1121/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL209493 in molecule.smi (1122/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL386053 in molecule.smi (1123/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211471 in molecule.smi (1124/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL209392 in molecule.smi (1125/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL209924 in molecule.smi (1126/4695). Average speed: 0.18 s/mol.\n", + "Processing CHEMBL212162 in molecule.smi (1127/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL209863 in molecule.smi (1129/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL212111 in molecule.smi (1128/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL211520 in molecule.smi (1131/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL427232 in molecule.smi (1130/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL384563 in molecule.smi (1133/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL380050 in molecule.smi (1132/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL386541 in molecule.smi (1135/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL415799 in molecule.smi (1134/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219262 in molecule.smi (1136/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219264 in molecule.smi (1137/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219316 in molecule.smi (1138/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL384886 in molecule.smi (1139/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL220141 in molecule.smi (1140/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL445846 in molecule.smi (1141/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219911 in molecule.smi (1142/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL374981 in molecule.smi (1143/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL216159 in molecule.smi (1144/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219569 in molecule.smi (1145/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL434378 in molecule.smi (1146/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219550 in molecule.smi (1147/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219063 in molecule.smi (1148/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL266045 in molecule.smi (1149/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL385269 in molecule.smi (1150/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL374184 in molecule.smi (1151/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL218940 in molecule.smi (1152/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL436302 in molecule.smi (1153/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219406 in molecule.smi (1154/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219405 in molecule.smi (1155/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL220294 in molecule.smi (1156/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219172 in molecule.smi (1158/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL219400 in molecule.smi (1157/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL266084 in molecule.smi (1160/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL218939 in molecule.smi (1159/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL221811 in molecule.smi (1161/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL80619 in molecule.smi (1162/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL221794 in molecule.smi (1163/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL278193 in molecule.smi (1164/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL376011 in molecule.smi (1165/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL373674 in molecule.smi (1166/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL376012 in molecule.smi (1167/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL220849 in molecule.smi (1168/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL220959 in molecule.smi (1169/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL441868 in molecule.smi (1170/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL375862 in molecule.smi (1171/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL373651 in molecule.smi (1172/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL220905 in molecule.smi (1173/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL374729 in molecule.smi (1174/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL385778 in molecule.smi (1175/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL376434 in molecule.smi (1176/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL375077 in molecule.smi (1177/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL71851 in molecule.smi (1178/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL500996 in molecule.smi (1179/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL611732 in molecule.smi (1180/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222037 in molecule.smi (1181/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222044 in molecule.smi (1182/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL221992 in molecule.smi (1184/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222043 in molecule.smi (1183/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222875 in molecule.smi (1186/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL19949 in molecule.smi (1185/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222340 in molecule.smi (1188/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222818 in molecule.smi (1187/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222378 in molecule.smi (1189/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL387394 in molecule.smi (1190/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222946 in molecule.smi (1191/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222904 in molecule.smi (1192/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL222856 in molecule.smi (1194/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL385130 in molecule.smi (1193/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225198 in molecule.smi (1196/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225567 in molecule.smi (1195/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL223443 in molecule.smi (1197/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225326 in molecule.smi (1198/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225021 in molecule.smi (1199/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225610 in molecule.smi (1200/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224553 in molecule.smi (1201/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL387849 in molecule.smi (1202/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225615 in molecule.smi (1203/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL389668 in molecule.smi (1204/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224811 in molecule.smi (1205/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225638 in molecule.smi (1206/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL223652 in molecule.smi (1207/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL375976 in molecule.smi (1208/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225453 in molecule.smi (1209/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225396 in molecule.smi (1210/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225451 in molecule.smi (1211/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL376186 in molecule.smi (1212/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224981 in molecule.smi (1214/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL390083 in molecule.smi (1213/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224555 in molecule.smi (1216/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225366 in molecule.smi (1215/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224347 in molecule.smi (1217/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL390598 in molecule.smi (1218/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL427090 in molecule.smi (1219/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224469 in molecule.smi (1220/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL224433 in molecule.smi (1221/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL390737 in molecule.smi (1222/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL427249 in molecule.smi (1223/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL225611 in molecule.smi (1224/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL376327 in molecule.smi (1225/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL229334 in molecule.smi (1226/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL396199 in molecule.smi (1227/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL241830 in molecule.smi (1228/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL241828 in molecule.smi (1229/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL241827 in molecule.smi (1230/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL245055 in molecule.smi (1231/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL388714 in molecule.smi (1232/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL245054 in molecule.smi (1233/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL244624 in molecule.smi (1235/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL244838 in molecule.smi (1234/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL396247 in molecule.smi (1237/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL244619 in molecule.smi (1236/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL244415 in molecule.smi (1239/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL244416 in molecule.smi (1238/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL389825 in molecule.smi (1241/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL245047 in molecule.smi (1240/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL427865 in molecule.smi (1243/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL245046 in molecule.smi (1242/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL242906 in molecule.smi (1245/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243130 in molecule.smi (1244/4695). Average speed: 0.19 s/mol.\n", + "Processing CHEMBL265416 in molecule.smi (1247/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242905 in molecule.smi (1246/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL413794 in molecule.smi (1249/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243532 in molecule.smi (1248/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243311 in molecule.smi (1251/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL413793 in molecule.smi (1250/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243310 in molecule.smi (1252/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL244837 in molecule.smi (1253/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL557586 in molecule.smi (1254/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241960 in molecule.smi (1255/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241974 in molecule.smi (1256/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL244762 in molecule.smi (1257/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL390247 in molecule.smi (1258/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL276444 in molecule.smi (1259/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL395815 in molecule.smi (1260/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243057 in molecule.smi (1261/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243274 in molecule.smi (1262/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL394865 in molecule.smi (1263/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242661 in molecule.smi (1264/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL218443 in molecule.smi (1265/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242836 in molecule.smi (1266/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242191 in molecule.smi (1267/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243044 in molecule.smi (1268/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242190 in molecule.smi (1269/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL394348 in molecule.smi (1270/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243273 in molecule.smi (1271/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL230702 in molecule.smi (1272/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243272 in molecule.smi (1273/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243271 in molecule.smi (1274/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243270 in molecule.smi (1275/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL230434 in molecule.smi (1276/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL396050 in molecule.smi (1277/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243269 in molecule.smi (1278/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243062 in molecule.smi (1279/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243056 in molecule.smi (1280/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL243055 in molecule.smi (1281/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242989 in molecule.smi (1282/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL242345 in molecule.smi (1283/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL332051 in molecule.smi (1284/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL392324 in molecule.smi (1285/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL115835 in molecule.smi (1286/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL325502 in molecule.smi (1287/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL397891 in molecule.smi (1288/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL415675 in molecule.smi (1289/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241011 in molecule.smi (1290/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL240793 in molecule.smi (1291/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL240792 in molecule.smi (1292/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL398406 in molecule.smi (1293/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238063 in molecule.smi (1295/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL235548 in molecule.smi (1294/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238062 in molecule.smi (1296/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL237870 in molecule.smi (1297/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL396190 in molecule.smi (1298/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL393212 in molecule.smi (1299/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241536 in molecule.smi (1300/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL391918 in molecule.smi (1301/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238994 in molecule.smi (1303/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL393621 in molecule.smi (1302/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238785 in molecule.smi (1304/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238573 in molecule.smi (1305/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238572 in molecule.smi (1306/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238571 in molecule.smi (1307/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241762 in molecule.smi (1308/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241761 in molecule.smi (1309/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL392385 in molecule.smi (1310/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241760 in molecule.smi (1311/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241554 in molecule.smi (1312/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241553 in molecule.smi (1313/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241552 in molecule.smi (1314/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL393758 in molecule.smi (1315/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL239633 in molecule.smi (1316/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241548 in molecule.smi (1317/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL397168 in molecule.smi (1318/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL237189 in molecule.smi (1319/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL392100 in molecule.smi (1320/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL392099 in molecule.smi (1321/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL393149 in molecule.smi (1323/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238293 in molecule.smi (1322/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL438324 in molecule.smi (1325/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL235761 in molecule.smi (1324/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL235759 in molecule.smi (1326/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238257 in molecule.smi (1327/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238256 in molecule.smi (1328/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238085 in molecule.smi (1329/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL235751 in molecule.smi (1330/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238067 in molecule.smi (1331/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL391356 in molecule.smi (1332/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238764 in molecule.smi (1333/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL236173 in molecule.smi (1334/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL239418 in molecule.smi (1335/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL397160 in molecule.smi (1336/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL239207 in molecule.smi (1337/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1203985 in molecule.smi (1339/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL241127 in molecule.smi (1338/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL360055 in molecule.smi (1340/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1203984 in molecule.smi (1342/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL555247 in molecule.smi (1341/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL540132 in molecule.smi (1343/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1203986 in molecule.smi (1344/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL540131 in molecule.smi (1345/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL235014 in molecule.smi (1346/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL440983 in molecule.smi (1347/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL238230 in molecule.smi (1348/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL251077 in molecule.smi (1349/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL402484 in molecule.smi (1350/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1524 in molecule.smi (1351/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL409610 in molecule.smi (1353/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL251076 in molecule.smi (1352/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL400092 in molecule.smi (1355/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL401114 in molecule.smi (1354/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL252723 in molecule.smi (1357/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL250688 in molecule.smi (1356/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1523 in molecule.smi (1358/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL479291 in molecule.smi (1359/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL15056 in molecule.smi (1360/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL41 in molecule.smi (1361/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL255231 in molecule.smi (1362/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL255230 in molecule.smi (1363/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL109018 in molecule.smi (1364/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL257540 in molecule.smi (1365/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL403260 in molecule.smi (1366/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL254016 in molecule.smi (1367/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL231160 in molecule.smi (1368/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL32329 in molecule.smi (1369/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL590603 in molecule.smi (1370/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL270248 in molecule.smi (1371/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL271710 in molecule.smi (1372/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL271709 in molecule.smi (1373/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL26125 in molecule.smi (1374/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL407904 in molecule.smi (1375/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL248922 in molecule.smi (1376/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL239046 in molecule.smi (1377/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL272700 in molecule.smi (1378/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL269865 in molecule.smi (1379/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL271385 in molecule.smi (1380/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL271746 in molecule.smi (1381/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL270349 in molecule.smi (1382/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL260964 in molecule.smi (1383/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL273051 in molecule.smi (1384/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL260308 in molecule.smi (1385/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL407320 in molecule.smi (1386/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL261384 in molecule.smi (1388/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL277984 in molecule.smi (1387/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL272229 in molecule.smi (1390/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL270491 in molecule.smi (1389/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL273078 in molecule.smi (1392/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL272230 in molecule.smi (1391/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL129542 in molecule.smi (1394/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL269863 in molecule.smi (1393/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1190 in molecule.smi (1395/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL259482 in molecule.smi (1397/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL259264 in molecule.smi (1396/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL259523 in molecule.smi (1399/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL129306 in molecule.smi (1398/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL497276 in molecule.smi (1401/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL406645 in molecule.smi (1400/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL442813 in molecule.smi (1403/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL481657 in molecule.smi (1402/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL502689 in molecule.smi (1405/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL416 in molecule.smi (1404/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL448060 in molecule.smi (1407/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL51628 in molecule.smi (1406/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL303846 in molecule.smi (1408/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL513279 in molecule.smi (1409/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL481658 in molecule.smi (1410/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL461949 in molecule.smi (1411/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL459825 in molecule.smi (1412/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL459826 in molecule.smi (1413/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL496487 in molecule.smi (1414/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL504924 in molecule.smi (1415/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL496259 in molecule.smi (1416/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL496255 in molecule.smi (1417/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL498645 in molecule.smi (1418/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL496488 in molecule.smi (1419/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL1678 in molecule.smi (1420/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL486698 in molecule.smi (1421/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3217103 in molecule.smi (1422/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3216210 in molecule.smi (1423/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3216208 in molecule.smi (1425/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3216209 in molecule.smi (1424/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3216422 in molecule.smi (1427/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3216655 in molecule.smi (1426/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL497755 in molecule.smi (1429/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL3216886 in molecule.smi (1428/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL504674 in molecule.smi (1430/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL444427 in molecule.smi (1431/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL447857 in molecule.smi (1432/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL455790 in molecule.smi (1433/4695). Average speed: 0.20 s/mol.\n", + "Processing CHEMBL506588 in molecule.smi (1434/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL511575 in molecule.smi (1435/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL511948 in molecule.smi (1436/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL523830 in molecule.smi (1437/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL513281 in molecule.smi (1438/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL518500 in molecule.smi (1439/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL512569 in molecule.smi (1440/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL503392 in molecule.smi (1441/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL461946 in molecule.smi (1442/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL512758 in molecule.smi (1443/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL461945 in molecule.smi (1444/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL459684 in molecule.smi (1445/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL459683 in molecule.smi (1446/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL450553 in molecule.smi (1447/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL507903 in molecule.smi (1448/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL507304 in molecule.smi (1449/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL449941 in molecule.smi (1450/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL452339 in molecule.smi (1451/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL449467 in molecule.smi (1452/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL450990 in molecule.smi (1453/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL507885 in molecule.smi (1454/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL499722 in molecule.smi (1455/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL450506 in molecule.smi (1456/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL499825 in molecule.smi (1457/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL502877 in molecule.smi (1458/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL488590 in molecule.smi (1459/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL456881 in molecule.smi (1460/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL470907 in molecule.smi (1462/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL490507 in molecule.smi (1461/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL511468 in molecule.smi (1463/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL474837 in molecule.smi (1464/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL471581 in molecule.smi (1465/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL514966 in molecule.smi (1466/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL474836 in molecule.smi (1467/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491526 in molecule.smi (1468/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL513236 in molecule.smi (1469/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL490360 in molecule.smi (1470/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL492098 in molecule.smi (1471/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491562 in molecule.smi (1472/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491527 in molecule.smi (1473/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL523311 in molecule.smi (1474/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491528 in molecule.smi (1475/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491346 in molecule.smi (1476/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL490359 in molecule.smi (1477/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491360 in molecule.smi (1478/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491359 in molecule.smi (1479/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491358 in molecule.smi (1480/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL490338 in molecule.smi (1481/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL491561 in molecule.smi (1482/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL470715 in molecule.smi (1483/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL470866 in molecule.smi (1484/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL470867 in molecule.smi (1485/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL469423 in molecule.smi (1486/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL478666 in molecule.smi (1487/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL474268 in molecule.smi (1488/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL473866 in molecule.smi (1489/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL478667 in molecule.smi (1490/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL497686 in molecule.smi (1491/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL498539 in molecule.smi (1492/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL526476 in molecule.smi (1493/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL456156 in molecule.smi (1494/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL508510 in molecule.smi (1495/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL498536 in molecule.smi (1496/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL468002 in molecule.smi (1498/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL512096 in molecule.smi (1497/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL513161 in molecule.smi (1500/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL468001 in molecule.smi (1499/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL556939 in molecule.smi (1502/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL468000 in molecule.smi (1501/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL550989 in molecule.smi (1504/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL561846 in molecule.smi (1503/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3216221 in molecule.smi (1506/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3215574 in molecule.smi (1505/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3215573 in molecule.smi (1507/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL2448076 in molecule.smi (1508/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3215770 in molecule.smi (1510/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3216018 in molecule.smi (1509/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3215768 in molecule.smi (1512/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3215769 in molecule.smi (1511/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3216429 in molecule.smi (1514/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL3215572 in molecule.smi (1513/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL556581 in molecule.smi (1516/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL555300 in molecule.smi (1515/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574267 in molecule.smi (1518/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL540178 in molecule.smi (1517/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL573094 in molecule.smi (1519/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575614 in molecule.smi (1521/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL584603 in molecule.smi (1520/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL583776 in molecule.smi (1523/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574746 in molecule.smi (1522/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574747 in molecule.smi (1524/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL584003 in molecule.smi (1525/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL576189 in molecule.smi (1526/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574261 in molecule.smi (1527/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL576384 in molecule.smi (1528/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL572858 in molecule.smi (1529/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574948 in molecule.smi (1530/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL576004 in molecule.smi (1531/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575166 in molecule.smi (1532/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL572865 in molecule.smi (1533/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL572866 in molecule.smi (1534/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL583920 in molecule.smi (1535/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL576385 in molecule.smi (1537/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL573095 in molecule.smi (1536/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574974 in molecule.smi (1539/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574973 in molecule.smi (1538/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575674 in molecule.smi (1541/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575613 in molecule.smi (1540/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575802 in molecule.smi (1542/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575167 in molecule.smi (1543/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575810 in molecule.smi (1544/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575619 in molecule.smi (1546/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL576005 in molecule.smi (1545/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575621 in molecule.smi (1548/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575620 in molecule.smi (1547/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL582883 in molecule.smi (1550/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL573102 in molecule.smi (1549/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574474 in molecule.smi (1552/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL575813 in molecule.smi (1551/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL572480 in molecule.smi (1554/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574487 in molecule.smi (1553/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL572453 in molecule.smi (1556/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL572532 in molecule.smi (1555/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL574847 in molecule.smi (1557/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1794936 in molecule.smi (1559/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL569978 in molecule.smi (1558/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL567532 in molecule.smi (1561/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL558598 in molecule.smi (1560/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL570159 in molecule.smi (1563/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL570158 in molecule.smi (1562/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL583855 in molecule.smi (1565/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL571730 in molecule.smi (1564/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL604478 in molecule.smi (1566/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL595115 in molecule.smi (1567/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL594870 in molecule.smi (1568/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL605303 in molecule.smi (1569/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL606348 in molecule.smi (1570/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL593933 in molecule.smi (1571/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL596236 in molecule.smi (1572/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL595114 in molecule.smi (1573/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL594186 in molecule.smi (1574/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL594187 in molecule.smi (1575/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL596457 in molecule.smi (1577/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL606372 in molecule.smi (1576/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL606538 in molecule.smi (1578/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL590031 in molecule.smi (1579/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL592433 in molecule.smi (1580/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL606034 in molecule.smi (1581/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL604255 in molecule.smi (1582/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL592624 in molecule.smi (1583/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL592856 in molecule.smi (1584/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL592857 in molecule.smi (1585/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL591469 in molecule.smi (1586/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL591470 in molecule.smi (1587/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1025 in molecule.smi (1588/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL603622 in molecule.smi (1589/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL599644 in molecule.smi (1590/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597188 in molecule.smi (1591/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597795 in molecule.smi (1593/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL604096 in molecule.smi (1592/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL598824 in molecule.smi (1595/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597794 in molecule.smi (1594/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL599058 in molecule.smi (1596/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL603256 in molecule.smi (1597/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL599057 in molecule.smi (1598/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597822 in molecule.smi (1599/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597821 in molecule.smi (1600/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL598645 in molecule.smi (1601/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL599253 in molecule.smi (1603/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL599254 in molecule.smi (1602/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597001 in molecule.smi (1605/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL599252 in molecule.smi (1604/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597406 in molecule.smi (1607/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL604715 in molecule.smi (1606/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL610779 in molecule.smi (1609/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL597405 in molecule.smi (1608/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL610219 in molecule.smi (1610/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL598632 in molecule.smi (1611/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL812 in molecule.smi (1612/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1088018 in molecule.smi (1613/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1088019 in molecule.smi (1614/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL389283 in molecule.smi (1615/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1088155 in molecule.smi (1616/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL105742 in molecule.smi (1618/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL225688 in molecule.smi (1617/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL430886 in molecule.smi (1620/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL225689 in molecule.smi (1619/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081448 in molecule.smi (1621/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081613 in molecule.smi (1623/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081449 in molecule.smi (1622/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081124 in molecule.smi (1625/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1076550 in molecule.smi (1624/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081126 in molecule.smi (1627/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081125 in molecule.smi (1626/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082081 in molecule.smi (1629/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1081810 in molecule.smi (1628/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082082 in molecule.smi (1630/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1076243 in molecule.smi (1631/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1080631 in molecule.smi (1632/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1080813 in molecule.smi (1633/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1080814 in molecule.smi (1634/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1076259 in molecule.smi (1635/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1076260 in molecule.smi (1636/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1080274 in molecule.smi (1637/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1087194 in molecule.smi (1638/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083973 in molecule.smi (1639/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084250 in molecule.smi (1640/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084518 in molecule.smi (1642/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084519 in molecule.smi (1641/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083972 in molecule.smi (1644/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084517 in molecule.smi (1643/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086184 in molecule.smi (1645/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084784 in molecule.smi (1646/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085759 in molecule.smi (1647/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085758 in molecule.smi (1648/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084785 in molecule.smi (1649/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085026 in molecule.smi (1650/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085027 in molecule.smi (1651/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084167 in molecule.smi (1652/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085028 in molecule.smi (1654/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082427 in molecule.smi (1653/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085029 in molecule.smi (1655/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085022 in molecule.smi (1657/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085284 in molecule.smi (1656/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082428 in molecule.smi (1659/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084442 in molecule.smi (1658/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082747 in molecule.smi (1661/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082746 in molecule.smi (1660/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085279 in molecule.smi (1663/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082748 in molecule.smi (1662/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082750 in molecule.smi (1665/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082749 in molecule.smi (1664/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083063 in molecule.smi (1666/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083064 in molecule.smi (1667/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084709 in molecule.smi (1669/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083373 in molecule.smi (1668/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085280 in molecule.smi (1670/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75110 in molecule.smi (1672/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085281 in molecule.smi (1671/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL308793 in molecule.smi (1674/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL311017 in molecule.smi (1673/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74922 in molecule.smi (1676/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74560 in molecule.smi (1675/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085858 in molecule.smi (1678/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL73339 in molecule.smi (1677/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL306776 in molecule.smi (1679/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL76108 in molecule.smi (1681/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL73447 in molecule.smi (1680/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL308807 in molecule.smi (1683/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL73974 in molecule.smi (1682/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL306175 in molecule.smi (1685/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74832 in molecule.smi (1684/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75649 in molecule.smi (1687/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74809 in molecule.smi (1686/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL310918 in molecule.smi (1689/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL306179 in molecule.smi (1688/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74359 in molecule.smi (1690/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083661 in molecule.smi (1692/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL307693 in molecule.smi (1691/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083660 in molecule.smi (1693/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL73593 in molecule.smi (1694/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083662 in molecule.smi (1695/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL76470 in molecule.smi (1696/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084254 in molecule.smi (1697/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75157 in molecule.smi (1698/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74747 in molecule.smi (1699/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL310467 in molecule.smi (1700/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL307513 in molecule.smi (1701/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74962 in molecule.smi (1702/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL72967 in molecule.smi (1704/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL309173 in molecule.smi (1703/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74533 in molecule.smi (1705/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75127 in molecule.smi (1706/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL308093 in molecule.smi (1707/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL76384 in molecule.smi (1708/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL420378 in molecule.smi (1709/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74340 in molecule.smi (1710/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL307832 in molecule.smi (1711/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74698 in molecule.smi (1712/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084703 in molecule.smi (1713/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085285 in molecule.smi (1714/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083065 in molecule.smi (1715/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083066 in molecule.smi (1716/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083067 in molecule.smi (1717/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083375 in molecule.smi (1718/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085125 in molecule.smi (1719/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085126 in molecule.smi (1720/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085127 in molecule.smi (1721/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084635 in molecule.smi (1722/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084880 in molecule.smi (1723/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085382 in molecule.smi (1725/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085128 in molecule.smi (1724/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084881 in molecule.smi (1727/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085383 in molecule.smi (1726/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086090 in molecule.smi (1729/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084882 in molecule.smi (1728/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085385 in molecule.smi (1731/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085384 in molecule.smi (1730/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085629 in molecule.smi (1733/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085386 in molecule.smi (1732/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085630 in molecule.smi (1734/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085011 in molecule.smi (1735/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL56470 in molecule.smi (1737/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL55014 in molecule.smi (1736/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL55312 in molecule.smi (1738/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140737 in molecule.smi (1740/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141873 in molecule.smi (1739/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340621 in molecule.smi (1742/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL126354 in molecule.smi (1741/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340672 in molecule.smi (1744/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140769 in molecule.smi (1743/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL127527 in molecule.smi (1746/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141853 in molecule.smi (1745/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141167 in molecule.smi (1748/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141801 in molecule.smi (1747/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140936 in molecule.smi (1750/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340609 in molecule.smi (1749/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL125578 in molecule.smi (1752/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141352 in molecule.smi (1751/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL333909 in molecule.smi (1753/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL106891 in molecule.smi (1754/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL326868 in molecule.smi (1756/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL419184 in molecule.smi (1755/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL336686 in molecule.smi (1758/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL105874 in molecule.smi (1757/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140948 in molecule.smi (1760/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL123909 in molecule.smi (1759/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL139353 in molecule.smi (1761/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL138107 in molecule.smi (1763/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL433678 in molecule.smi (1762/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140770 in molecule.smi (1764/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084256 in molecule.smi (1765/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140999 in molecule.smi (1766/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141001 in molecule.smi (1767/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL138442 in molecule.smi (1768/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141622 in molecule.smi (1769/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140106 in molecule.smi (1770/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141203 in molecule.smi (1771/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL142014 in molecule.smi (1772/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140990 in molecule.smi (1773/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL142013 in molecule.smi (1774/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141042 in molecule.smi (1775/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL140328 in molecule.smi (1776/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141810 in molecule.smi (1777/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL342413 in molecule.smi (1778/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141276 in molecule.smi (1779/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL138552 in molecule.smi (1780/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL141515 in molecule.smi (1781/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL129753 in molecule.smi (1783/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083069 in molecule.smi (1782/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL337999 in molecule.smi (1785/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083070 in molecule.smi (1784/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128863 in molecule.smi (1787/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083379 in molecule.smi (1786/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL336297 in molecule.smi (1788/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128260 in molecule.smi (1790/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL130847 in molecule.smi (1789/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL131827 in molecule.smi (1791/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084090 in molecule.smi (1793/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL130918 in molecule.smi (1792/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL340625 in molecule.smi (1794/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084883 in molecule.smi (1796/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128551 in molecule.smi (1795/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084091 in molecule.smi (1798/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128618 in molecule.smi (1797/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128471 in molecule.smi (1800/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085129 in molecule.smi (1799/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128043 in molecule.smi (1801/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084366 in molecule.smi (1802/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128202 in molecule.smi (1803/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL339039 in molecule.smi (1804/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082419 in molecule.smi (1805/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL131219 in molecule.smi (1806/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL128452 in molecule.smi (1807/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083976 in molecule.smi (1808/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL131579 in molecule.smi (1809/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL131411 in molecule.smi (1810/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083977 in molecule.smi (1811/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL151417 in molecule.smi (1812/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL281588 in molecule.smi (1813/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL278819 in molecule.smi (1814/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085019 in molecule.smi (1815/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL150985 in molecule.smi (1816/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085020 in molecule.smi (1817/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085021 in molecule.smi (1818/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083676 in molecule.smi (1819/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL150365 in molecule.smi (1820/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082740 in molecule.smi (1821/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082741 in molecule.smi (1822/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL356224 in molecule.smi (1823/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085937 in molecule.smi (1824/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085274 in molecule.smi (1825/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085275 in molecule.smi (1826/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084255 in molecule.smi (1827/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL25264 in molecule.smi (1828/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083054 in molecule.smi (1829/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086185 in molecule.smi (1830/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083055 in molecule.smi (1831/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084257 in molecule.smi (1832/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083057 in molecule.smi (1833/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083058 in molecule.smi (1834/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083367 in molecule.smi (1835/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL25412 in molecule.smi (1836/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084542 in molecule.smi (1837/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084543 in molecule.smi (1838/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084786 in molecule.smi (1839/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084788 in molecule.smi (1841/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084787 in molecule.smi (1840/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084789 in molecule.smi (1842/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082434 in molecule.smi (1844/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082433 in molecule.smi (1843/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084275 in molecule.smi (1846/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082435 in molecule.smi (1845/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084790 in molecule.smi (1847/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL100334 in molecule.smi (1849/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082436 in molecule.smi (1848/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL102142 in molecule.smi (1850/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL431058 in molecule.smi (1851/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL430679 in molecule.smi (1853/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL101518 in molecule.smi (1852/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL101940 in molecule.smi (1854/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL100510 in molecule.smi (1856/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL101809 in molecule.smi (1855/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085031 in molecule.smi (1858/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085030 in molecule.smi (1857/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL101155 in molecule.smi (1859/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL317541 in molecule.smi (1860/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL420827 in molecule.smi (1862/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL101988 in molecule.smi (1861/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL329518 in molecule.smi (1863/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL101028 in molecule.smi (1864/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085032 in molecule.smi (1865/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL99605 in molecule.smi (1866/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL329733 in molecule.smi (1867/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL98497 in molecule.smi (1868/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083789 in molecule.smi (1869/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083790 in molecule.smi (1871/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL258928 in molecule.smi (1870/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084093 in molecule.smi (1873/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084092 in molecule.smi (1872/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084094 in molecule.smi (1875/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083791 in molecule.smi (1874/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084095 in molecule.smi (1876/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084367 in molecule.smi (1877/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084368 in molecule.smi (1878/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084369 in molecule.smi (1879/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084096 in molecule.smi (1880/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084530 in molecule.smi (1881/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084531 in molecule.smi (1882/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084774 in molecule.smi (1883/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084775 in molecule.smi (1884/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082738 in molecule.smi (1885/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1082739 in molecule.smi (1886/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL118752 in molecule.smi (1887/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL330883 in molecule.smi (1888/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL118968 in molecule.smi (1889/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL331637 in molecule.smi (1890/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL118033 in molecule.smi (1891/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084952 in molecule.smi (1892/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL332454 in molecule.smi (1893/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL119634 in molecule.smi (1894/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL118139 in molecule.smi (1895/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL117826 in molecule.smi (1896/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL115697 in molecule.smi (1897/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL325203 in molecule.smi (1898/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL115580 in molecule.smi (1899/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL119528 in molecule.smi (1900/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL119390 in molecule.smi (1901/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL119389 in molecule.smi (1902/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085276 in molecule.smi (1903/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083677 in molecule.smi (1904/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083678 in molecule.smi (1905/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083982 in molecule.smi (1906/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083056 in molecule.smi (1907/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085277 in molecule.smi (1908/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085278 in molecule.smi (1909/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085526 in molecule.smi (1910/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085527 in molecule.smi (1911/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086245 in molecule.smi (1912/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086246 in molecule.smi (1913/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086247 in molecule.smi (1914/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1086248 in molecule.smi (1915/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084276 in molecule.smi (1916/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL94054 in molecule.smi (1917/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL90241 in molecule.smi (1918/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL91240 in molecule.smi (1919/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL90264 in molecule.smi (1920/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL91432 in molecule.smi (1921/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL91774 in molecule.smi (1922/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL421501 in molecule.smi (1923/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL76014 in molecule.smi (1924/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74827 in molecule.smi (1925/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL73800 in molecule.smi (1926/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74940 in molecule.smi (1928/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75274 in molecule.smi (1927/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL312761 in molecule.smi (1930/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL74927 in molecule.smi (1929/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL307004 in molecule.smi (1932/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL73160 in molecule.smi (1931/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75305 in molecule.smi (1934/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL312310 in molecule.smi (1933/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL75985 in molecule.smi (1936/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL76173 in molecule.smi (1935/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL307908 in molecule.smi (1938/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083488 in molecule.smi (1937/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084155 in molecule.smi (1940/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL306492 in molecule.smi (1939/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084777 in molecule.smi (1942/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084776 in molecule.smi (1941/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085018 in molecule.smi (1944/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085017 in molecule.smi (1943/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085932 in molecule.smi (1946/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083374 in molecule.smi (1945/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085938 in molecule.smi (1948/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085933 in molecule.smi (1947/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085939 in molecule.smi (1949/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL224158 in molecule.smi (1951/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1085940 in molecule.smi (1950/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083983 in molecule.smi (1953/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL224335 in molecule.smi (1952/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL389192 in molecule.smi (1955/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1083984 in molecule.smi (1954/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084263 in molecule.smi (1957/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL1084262 in molecule.smi (1956/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL388551 in molecule.smi (1959/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL224336 in molecule.smi (1958/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL223170 in molecule.smi (1960/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL224202 in molecule.smi (1961/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL224998 in molecule.smi (1963/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL388575 in molecule.smi (1962/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL223714 in molecule.smi (1965/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL388627 in molecule.smi (1964/4695). Average speed: 0.21 s/mol.\n", + "Processing CHEMBL426015 in molecule.smi (1967/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL427082 in molecule.smi (1966/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1086007 in molecule.smi (1969/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL388615 in molecule.smi (1968/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084545 in molecule.smi (1971/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084544 in molecule.smi (1970/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL427342 in molecule.smi (1972/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL325109 in molecule.smi (1974/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL185896 in molecule.smi (1973/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL189870 in molecule.smi (1976/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL362579 in molecule.smi (1975/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL360186 in molecule.smi (1977/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL188019 in molecule.smi (1979/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL187030 in molecule.smi (1978/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1086358 in molecule.smi (1980/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL189764 in molecule.smi (1982/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL186896 in molecule.smi (1981/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL189875 in molecule.smi (1984/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1085465 in molecule.smi (1983/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL186349 in molecule.smi (1986/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL187017 in molecule.smi (1985/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL186400 in molecule.smi (1988/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL187370 in molecule.smi (1987/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL328085 in molecule.smi (1989/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1085702 in molecule.smi (1990/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1085703 in molecule.smi (1991/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL3215602 in molecule.smi (1992/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL3215816 in molecule.smi (1993/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL3216469 in molecule.smi (1994/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL310555 in molecule.smi (1995/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1171854 in molecule.smi (1996/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1172830 in molecule.smi (1997/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1173543 in molecule.smi (1998/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1173603 in molecule.smi (1999/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1173604 in molecule.smi (2000/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1173605 in molecule.smi (2001/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1172913 in molecule.smi (2002/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1173624 in molecule.smi (2003/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1173090 in molecule.smi (2004/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1213295 in molecule.smi (2005/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243360 in molecule.smi (2006/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243329 in molecule.smi (2007/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243359 in molecule.smi (2008/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1240668 in molecule.smi (2009/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243297 in molecule.smi (2010/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243269 in molecule.smi (2011/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243268 in molecule.smi (2012/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243388 in molecule.smi (2014/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243234 in molecule.smi (2013/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243170 in molecule.smi (2015/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1241457 in molecule.smi (2017/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243140 in molecule.smi (2016/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243203 in molecule.smi (2018/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243076 in molecule.smi (2020/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243111 in molecule.smi (2019/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243298 in molecule.smi (2021/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL519475 in molecule.smi (2022/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL295462 in molecule.smi (2023/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1241427 in molecule.smi (2024/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1243012 in molecule.smi (2025/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257997 in molecule.smi (2026/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257996 in molecule.smi (2027/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257887 in molecule.smi (2028/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257886 in molecule.smi (2029/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257885 in molecule.smi (2030/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257767 in molecule.smi (2031/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257766 in molecule.smi (2032/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257644 in molecule.smi (2033/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257413 in molecule.smi (2034/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257525 in molecule.smi (2035/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1257412 in molecule.smi (2036/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1256415 in molecule.smi (2037/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1276594 in molecule.smi (2038/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1276534 in molecule.smi (2039/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1276567 in molecule.smi (2040/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1275924 in molecule.smi (2041/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289045 in molecule.smi (2042/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289159 in molecule.smi (2043/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289160 in molecule.smi (2044/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289273 in molecule.smi (2045/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289274 in molecule.smi (2046/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289391 in molecule.smi (2047/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289392 in molecule.smi (2048/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289507 in molecule.smi (2050/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289506 in molecule.smi (2049/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288821 in molecule.smi (2051/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289615 in molecule.smi (2052/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289719 in molecule.smi (2053/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289825 in molecule.smi (2054/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288822 in molecule.smi (2055/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1289942 in molecule.smi (2056/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1290043 in molecule.smi (2057/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288823 in molecule.smi (2058/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288824 in molecule.smi (2059/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1290152 in molecule.smi (2060/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288825 in molecule.smi (2061/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288826 in molecule.smi (2062/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288827 in molecule.smi (2063/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288828 in molecule.smi (2064/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1288829 in molecule.smi (2065/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642977 in molecule.smi (2066/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642979 in molecule.smi (2068/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL594629 in molecule.smi (2067/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642976 in molecule.smi (2069/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642980 in molecule.smi (2071/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642988 in molecule.smi (2070/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642978 in molecule.smi (2073/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642975 in molecule.smi (2072/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642987 in molecule.smi (2074/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642986 in molecule.smi (2075/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642984 in molecule.smi (2076/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642973 in molecule.smi (2078/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642983 in molecule.smi (2077/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642981 in molecule.smi (2080/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642985 in molecule.smi (2079/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642972 in molecule.smi (2081/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642982 in molecule.smi (2083/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642971 in molecule.smi (2082/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642974 in molecule.smi (2085/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1642970 in molecule.smi (2084/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL291233 in molecule.smi (2086/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL426113 in molecule.smi (2088/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1200970 in molecule.smi (2087/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL451825 in molecule.smi (2089/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL397871 in molecule.smi (2090/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL238977 in molecule.smi (2091/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1644323 in molecule.smi (2092/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1644325 in molecule.smi (2093/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1644328 in molecule.smi (2094/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651042 in molecule.smi (2095/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651043 in molecule.smi (2096/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651044 in molecule.smi (2097/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651045 in molecule.smi (2098/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651046 in molecule.smi (2099/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651040 in molecule.smi (2101/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651041 in molecule.smi (2100/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651047 in molecule.smi (2102/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651048 in molecule.smi (2103/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651038 in molecule.smi (2104/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1196495 in molecule.smi (2106/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651125 in molecule.smi (2105/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651126 in molecule.smi (2107/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651128 in molecule.smi (2109/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651127 in molecule.smi (2108/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651130 in molecule.smi (2111/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651129 in molecule.smi (2110/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651132 in molecule.smi (2113/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651131 in molecule.smi (2112/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651134 in molecule.smi (2115/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651133 in molecule.smi (2114/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651136 in molecule.smi (2117/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651135 in molecule.smi (2116/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651137 in molecule.smi (2118/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651138 in molecule.smi (2119/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651139 in molecule.smi (2120/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651140 in molecule.smi (2121/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651243 in molecule.smi (2123/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651141 in molecule.smi (2122/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651244 in molecule.smi (2124/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651245 in molecule.smi (2125/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1179697 in molecule.smi (2126/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651246 in molecule.smi (2127/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651247 in molecule.smi (2128/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651248 in molecule.smi (2129/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651249 in molecule.smi (2130/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1651250 in molecule.smi (2131/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669479 in molecule.smi (2132/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669480 in molecule.smi (2133/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669482 in molecule.smi (2135/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669481 in molecule.smi (2134/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669484 in molecule.smi (2137/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669483 in molecule.smi (2136/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669485 in molecule.smi (2138/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669486 in molecule.smi (2139/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL104553 in molecule.smi (2140/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1643944 in molecule.smi (2141/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL105537 in molecule.smi (2143/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL104613 in molecule.smi (2142/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669488 in molecule.smi (2145/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669487 in molecule.smi (2144/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669489 in molecule.smi (2146/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669490 in molecule.smi (2147/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669491 in molecule.smi (2148/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669492 in molecule.smi (2149/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669493 in molecule.smi (2150/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1669494 in molecule.smi (2151/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1668333 in molecule.smi (2152/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1668334 in molecule.smi (2153/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761991 in molecule.smi (2154/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761992 in molecule.smi (2155/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761993 in molecule.smi (2156/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL601410 in molecule.smi (2157/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761994 in molecule.smi (2158/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761995 in molecule.smi (2159/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL105017 in molecule.smi (2160/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL225577 in molecule.smi (2161/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL104387 in molecule.smi (2163/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL105257 in molecule.smi (2162/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761996 in molecule.smi (2164/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761997 in molecule.smi (2165/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761998 in molecule.smi (2166/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1761999 in molecule.smi (2167/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1762000 in molecule.smi (2168/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1762001 in molecule.smi (2169/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1762003 in molecule.smi (2170/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1555 in molecule.smi (2171/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766151 in molecule.smi (2172/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084213 in molecule.smi (2173/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084212 in molecule.smi (2174/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084211 in molecule.smi (2175/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084210 in molecule.smi (2176/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766150 in molecule.smi (2177/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1082981 in molecule.smi (2178/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1082979 in molecule.smi (2180/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1082980 in molecule.smi (2179/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1082978 in molecule.smi (2182/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1085505 in molecule.smi (2181/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766148 in molecule.smi (2184/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766149 in molecule.smi (2183/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766146 in molecule.smi (2186/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766147 in molecule.smi (2185/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766145 in molecule.smi (2187/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766144 in molecule.smi (2188/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766143 in molecule.smi (2189/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766142 in molecule.smi (2190/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766141 in molecule.smi (2191/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766140 in molecule.smi (2192/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766139 in molecule.smi (2193/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766020 in molecule.smi (2194/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766019 in molecule.smi (2195/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766018 in molecule.smi (2196/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766017 in molecule.smi (2197/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1082977 in molecule.smi (2199/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1766016 in molecule.smi (2198/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1086014 in molecule.smi (2201/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1084808 in molecule.smi (2200/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1085780 in molecule.smi (2203/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1085781 in molecule.smi (2202/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770549 in molecule.smi (2205/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770548 in molecule.smi (2204/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770551 in molecule.smi (2207/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770550 in molecule.smi (2206/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770552 in molecule.smi (2208/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770553 in molecule.smi (2209/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770554 in molecule.smi (2210/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1770555 in molecule.smi (2211/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL188821 in molecule.smi (2212/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL187404 in molecule.smi (2213/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1771549 in molecule.smi (2214/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1771556 in molecule.smi (2215/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773478 in molecule.smi (2216/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773479 in molecule.smi (2217/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773480 in molecule.smi (2218/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773481 in molecule.smi (2219/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773482 in molecule.smi (2220/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773483 in molecule.smi (2221/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773484 in molecule.smi (2222/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773485 in molecule.smi (2223/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773486 in molecule.smi (2224/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773487 in molecule.smi (2225/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773488 in molecule.smi (2226/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773489 in molecule.smi (2227/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773490 in molecule.smi (2228/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773491 in molecule.smi (2229/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1773492 in molecule.smi (2230/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1080386 in molecule.smi (2231/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1782707 in molecule.smi (2232/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1206 in molecule.smi (2233/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1782730 in molecule.smi (2234/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1784957 in molecule.smi (2235/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1784956 in molecule.smi (2236/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1784955 in molecule.smi (2237/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1784958 in molecule.smi (2238/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783529 in molecule.smi (2239/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783530 in molecule.smi (2240/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783531 in molecule.smi (2241/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783532 in molecule.smi (2242/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783533 in molecule.smi (2244/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1782071 in molecule.smi (2243/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783534 in molecule.smi (2245/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783535 in molecule.smi (2246/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783520 in molecule.smi (2247/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783521 in molecule.smi (2248/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783522 in molecule.smi (2249/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783523 in molecule.smi (2250/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783524 in molecule.smi (2251/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783525 in molecule.smi (2252/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783526 in molecule.smi (2253/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783527 in molecule.smi (2254/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783528 in molecule.smi (2255/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783514 in molecule.smi (2256/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783515 in molecule.smi (2257/4695). Average speed: 0.22 s/mol.\n", + "Processing CHEMBL1783516 in molecule.smi (2258/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1783517 in molecule.smi (2259/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1783518 in molecule.smi (2260/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1783519 in molecule.smi (2261/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1783499 in molecule.smi (2262/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL589390 in molecule.smi (2263/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1814729 in molecule.smi (2264/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1814730 in molecule.smi (2265/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819169 in molecule.smi (2267/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1814731 in molecule.smi (2266/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819171 in molecule.smi (2269/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819170 in molecule.smi (2268/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819172 in molecule.smi (2270/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819173 in molecule.smi (2271/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819174 in molecule.smi (2272/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819175 in molecule.smi (2273/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819176 in molecule.smi (2274/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819177 in molecule.smi (2275/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819178 in molecule.smi (2276/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819179 in molecule.smi (2277/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1819180 in molecule.smi (2278/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834076 in molecule.smi (2279/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834060 in molecule.smi (2280/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834061 in molecule.smi (2281/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834062 in molecule.smi (2282/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834063 in molecule.smi (2283/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834064 in molecule.smi (2284/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834065 in molecule.smi (2285/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834066 in molecule.smi (2286/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834067 in molecule.smi (2287/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834068 in molecule.smi (2288/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834069 in molecule.smi (2289/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834070 in molecule.smi (2290/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834071 in molecule.smi (2291/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834072 in molecule.smi (2292/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834073 in molecule.smi (2293/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834074 in molecule.smi (2294/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834075 in molecule.smi (2295/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834077 in molecule.smi (2296/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834078 in molecule.smi (2297/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834079 in molecule.smi (2298/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834806 in molecule.smi (2299/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834808 in molecule.smi (2300/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834807 in molecule.smi (2301/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834809 in molecule.smi (2302/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834877 in molecule.smi (2303/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834810 in molecule.smi (2304/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1834811 in molecule.smi (2305/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL221753 in molecule.smi (2306/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL31184 in molecule.smi (2308/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL12089 in molecule.smi (2307/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL990 in molecule.smi (2309/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL195437 in molecule.smi (2311/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL40583 in molecule.smi (2310/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL305660 in molecule.smi (2312/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL64894 in molecule.smi (2313/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL808 in molecule.smi (2314/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL496 in molecule.smi (2315/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1200623 in molecule.smi (2316/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL672 in molecule.smi (2317/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL7002 in molecule.smi (2318/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL12131 in molecule.smi (2319/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL104 in molecule.smi (2320/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL411 in molecule.smi (2321/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL334255 in molecule.smi (2322/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3183075 in molecule.smi (2323/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1262 in molecule.smi (2324/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL7568 in molecule.smi (2325/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL91 in molecule.smi (2326/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL19215 in molecule.smi (2327/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL481 in molecule.smi (2328/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1306 in molecule.smi (2329/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL81 in molecule.smi (2330/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912062 in molecule.smi (2331/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912061 in molecule.smi (2332/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912060 in molecule.smi (2333/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912059 in molecule.smi (2334/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912058 in molecule.smi (2335/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912063 in molecule.smi (2336/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1912064 in molecule.smi (2337/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1917825 in molecule.smi (2338/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1917826 in molecule.smi (2339/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1618217 in molecule.smi (2340/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1618106 in molecule.smi (2341/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1914495 in molecule.smi (2342/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915758 in molecule.smi (2343/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915759 in molecule.smi (2344/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1086927 in molecule.smi (2345/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1087180 in molecule.smi (2346/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1087131 in molecule.smi (2348/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL465514 in molecule.smi (2347/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915760 in molecule.smi (2350/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL240972 in molecule.smi (2349/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915762 in molecule.smi (2352/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915761 in molecule.smi (2351/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915763 in molecule.smi (2354/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1098479 in molecule.smi (2353/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915764 in molecule.smi (2355/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL465487 in molecule.smi (2356/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL512830 in molecule.smi (2358/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL460969 in molecule.smi (2357/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915765 in molecule.smi (2359/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1915766 in molecule.smi (2360/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1916475 in molecule.smi (2361/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1916766 in molecule.smi (2362/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1916768 in molecule.smi (2363/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1916770 in molecule.smi (2364/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1916927 in molecule.smi (2365/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1916929 in molecule.smi (2366/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1922540 in molecule.smi (2367/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1922541 in molecule.smi (2368/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL180483 in molecule.smi (2369/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1929421 in molecule.smi (2370/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940736 in molecule.smi (2371/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940737 in molecule.smi (2372/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940738 in molecule.smi (2373/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940612 in molecule.smi (2374/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940613 in molecule.smi (2375/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940614 in molecule.smi (2376/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940615 in molecule.smi (2377/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940616 in molecule.smi (2378/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940617 in molecule.smi (2379/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940618 in molecule.smi (2380/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1940619 in molecule.smi (2381/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952057 in molecule.smi (2382/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952058 in molecule.smi (2383/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952059 in molecule.smi (2384/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952060 in molecule.smi (2385/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952061 in molecule.smi (2386/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952062 in molecule.smi (2387/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952063 in molecule.smi (2388/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952064 in molecule.smi (2389/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952065 in molecule.smi (2390/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952066 in molecule.smi (2391/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952067 in molecule.smi (2392/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952047 in molecule.smi (2393/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952048 in molecule.smi (2394/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952049 in molecule.smi (2395/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952050 in molecule.smi (2396/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952051 in molecule.smi (2397/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952052 in molecule.smi (2398/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952053 in molecule.smi (2399/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952054 in molecule.smi (2400/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952055 in molecule.smi (2401/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952056 in molecule.smi (2402/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011402 in molecule.smi (2403/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL459258 in molecule.smi (2404/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011403 in molecule.smi (2405/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011404 in molecule.smi (2406/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL253152 in molecule.smi (2407/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL374130 in molecule.smi (2409/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011405 in molecule.smi (2408/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011406 in molecule.smi (2411/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL223256 in molecule.smi (2410/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL399121 in molecule.smi (2412/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL8996 in molecule.smi (2414/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL9352 in molecule.smi (2413/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3215882 in molecule.smi (2416/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011483 in molecule.smi (2415/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3217208 in molecule.smi (2418/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216777 in molecule.smi (2417/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011488 in molecule.smi (2420/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011487 in molecule.smi (2419/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216554 in molecule.smi (2422/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216116 in molecule.smi (2421/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216555 in molecule.smi (2424/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011491 in molecule.smi (2423/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216556 in molecule.smi (2425/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216557 in molecule.smi (2426/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011495 in molecule.smi (2427/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011496 in molecule.smi (2428/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216778 in molecule.smi (2429/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3216779 in molecule.smi (2430/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1994202 in molecule.smi (2431/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2011499 in molecule.smi (2432/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL340807 in molecule.smi (2433/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2018160 in molecule.smi (2434/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2018161 in molecule.smi (2435/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL376478 in molecule.smi (2436/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2018162 in molecule.smi (2437/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019052 in molecule.smi (2439/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019053 in molecule.smi (2438/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019048 in molecule.smi (2441/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019049 in molecule.smi (2440/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019046 in molecule.smi (2443/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019047 in molecule.smi (2442/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019043 in molecule.smi (2445/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019044 in molecule.smi (2444/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019040 in molecule.smi (2447/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019041 in molecule.smi (2446/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019038 in molecule.smi (2448/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019037 in molecule.smi (2449/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019035 in molecule.smi (2450/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019034 in molecule.smi (2451/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019032 in molecule.smi (2452/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2019030 in molecule.smi (2453/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2022933 in molecule.smi (2454/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047529 in molecule.smi (2456/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047528 in molecule.smi (2455/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047530 in molecule.smi (2457/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047531 in molecule.smi (2458/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047231 in molecule.smi (2460/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047375 in molecule.smi (2459/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047229 in molecule.smi (2462/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047230 in molecule.smi (2461/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047228 in molecule.smi (2463/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047227 in molecule.smi (2464/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047226 in molecule.smi (2465/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047225 in molecule.smi (2466/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047224 in molecule.smi (2467/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2047223 in molecule.smi (2468/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064471 in molecule.smi (2469/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064470 in molecule.smi (2470/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064469 in molecule.smi (2471/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064466 in molecule.smi (2473/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064468 in molecule.smi (2472/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064464 in molecule.smi (2475/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064465 in molecule.smi (2474/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2071424 in molecule.smi (2477/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2064404 in molecule.smi (2476/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2071426 in molecule.smi (2479/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2071425 in molecule.smi (2478/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2071427 in molecule.smi (2480/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL463686 in molecule.smi (2481/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2071428 in molecule.smi (2482/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL1952289 in molecule.smi (2483/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088687 in molecule.smi (2484/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088780 in molecule.smi (2485/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088781 in molecule.smi (2486/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088782 in molecule.smi (2487/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088783 in molecule.smi (2488/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088784 in molecule.smi (2489/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088785 in molecule.smi (2490/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2088786 in molecule.smi (2491/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2147460 in molecule.smi (2492/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2147462 in molecule.smi (2493/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2147461 in molecule.smi (2494/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160227 in molecule.smi (2495/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160226 in molecule.smi (2496/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160223 in molecule.smi (2497/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160225 in molecule.smi (2498/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160221 in molecule.smi (2499/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160220 in molecule.smi (2500/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160224 in molecule.smi (2501/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160222 in molecule.smi (2502/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160217 in molecule.smi (2503/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160228 in molecule.smi (2504/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2160213 in molecule.smi (2505/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2163795 in molecule.smi (2507/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL258893 in molecule.smi (2506/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181440 in molecule.smi (2508/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181438 in molecule.smi (2509/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181435 in molecule.smi (2511/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181437 in molecule.smi (2510/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181433 in molecule.smi (2512/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181432 in molecule.smi (2513/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181430 in molecule.smi (2515/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2181431 in molecule.smi (2514/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206900 in molecule.smi (2516/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206899 in molecule.smi (2517/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206897 in molecule.smi (2519/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206898 in molecule.smi (2518/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206896 in molecule.smi (2520/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206895 in molecule.smi (2521/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206894 in molecule.smi (2522/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206893 in molecule.smi (2523/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206892 in molecule.smi (2524/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2206891 in molecule.smi (2525/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238007 in molecule.smi (2526/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238006 in molecule.smi (2527/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238005 in molecule.smi (2528/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238004 in molecule.smi (2529/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238003 in molecule.smi (2530/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238002 in molecule.smi (2531/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238001 in molecule.smi (2532/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2238000 in molecule.smi (2533/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237999 in molecule.smi (2534/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237998 in molecule.smi (2535/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237996 in molecule.smi (2537/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237997 in molecule.smi (2536/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237994 in molecule.smi (2539/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237995 in molecule.smi (2538/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237992 in molecule.smi (2541/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237993 in molecule.smi (2540/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2237991 in molecule.smi (2542/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL400257 in molecule.smi (2543/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL607537 in molecule.smi (2544/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL256497 in molecule.smi (2545/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL21396 in molecule.smi (2546/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL128392 in molecule.smi (2547/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL128780 in molecule.smi (2548/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL129577 in molecule.smi (2549/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL128390 in molecule.smi (2550/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2236393 in molecule.smi (2552/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2236394 in molecule.smi (2551/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234536 in molecule.smi (2554/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234537 in molecule.smi (2553/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234534 in molecule.smi (2556/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234535 in molecule.smi (2555/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234532 in molecule.smi (2558/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234533 in molecule.smi (2557/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234531 in molecule.smi (2559/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234530 in molecule.smi (2560/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234529 in molecule.smi (2561/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234528 in molecule.smi (2562/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234527 in molecule.smi (2563/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234526 in molecule.smi (2564/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234525 in molecule.smi (2565/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234524 in molecule.smi (2566/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234523 in molecule.smi (2567/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234522 in molecule.smi (2568/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234521 in molecule.smi (2569/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234520 in molecule.smi (2570/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234519 in molecule.smi (2571/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234518 in molecule.smi (2572/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234517 in molecule.smi (2573/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234516 in molecule.smi (2574/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234515 in molecule.smi (2575/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234513 in molecule.smi (2577/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234514 in molecule.smi (2576/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234511 in molecule.smi (2579/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234512 in molecule.smi (2578/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234844 in molecule.smi (2581/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234845 in molecule.smi (2580/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234842 in molecule.smi (2583/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234843 in molecule.smi (2582/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234840 in molecule.smi (2585/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234841 in molecule.smi (2584/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234838 in molecule.smi (2587/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234839 in molecule.smi (2586/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234836 in molecule.smi (2589/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234837 in molecule.smi (2588/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234834 in molecule.smi (2591/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234835 in molecule.smi (2590/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234833 in molecule.smi (2592/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234831 in molecule.smi (2594/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234832 in molecule.smi (2593/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234829 in molecule.smi (2596/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234830 in molecule.smi (2595/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234546 in molecule.smi (2598/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234547 in molecule.smi (2597/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234545 in molecule.smi (2599/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252378 in molecule.smi (2601/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234544 in molecule.smi (2600/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL253603 in molecule.smi (2602/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL398250 in molecule.smi (2603/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL253602 in molecule.smi (2604/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL398249 in molecule.smi (2605/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252781 in molecule.smi (2606/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234543 in molecule.smi (2607/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234542 in molecule.smi (2608/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234541 in molecule.smi (2609/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234540 in molecule.smi (2610/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252578 in molecule.smi (2611/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252580 in molecule.smi (2612/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234539 in molecule.smi (2613/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2234538 in molecule.smi (2614/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL400675 in molecule.smi (2615/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252579 in molecule.smi (2616/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252380 in molecule.smi (2617/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL252379 in molecule.smi (2618/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL399040 in molecule.smi (2619/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL404407 in molecule.smi (2620/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL253783 in molecule.smi (2621/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL253782 in molecule.smi (2622/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL253386 in molecule.smi (2623/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2297278 in molecule.smi (2625/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2297277 in molecule.smi (2624/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL31574 in molecule.smi (2627/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL28 in molecule.smi (2626/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL309490 in molecule.smi (2629/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL164 in molecule.smi (2628/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL226335 in molecule.smi (2631/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2297279 in molecule.smi (2630/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2297280 in molecule.smi (2633/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL594229 in molecule.smi (2632/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL50 in molecule.smi (2634/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2314726 in molecule.smi (2635/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2323356 in molecule.smi (2636/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2323355 in molecule.smi (2637/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2323354 in molecule.smi (2638/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2323353 in molecule.smi (2639/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2323352 in molecule.smi (2640/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2323351 in molecule.smi (2641/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332981 in molecule.smi (2642/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332540 in molecule.smi (2643/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332541 in molecule.smi (2644/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332530 in molecule.smi (2645/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332531 in molecule.smi (2646/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332532 in molecule.smi (2647/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332533 in molecule.smi (2648/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332542 in molecule.smi (2650/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332534 in molecule.smi (2649/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2332543 in molecule.smi (2651/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332544 in molecule.smi (2652/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332545 in molecule.smi (2653/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332980 in molecule.smi (2654/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332979 in molecule.smi (2655/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332978 in molecule.smi (2656/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332977 in molecule.smi (2657/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332976 in molecule.smi (2658/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332975 in molecule.smi (2659/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332974 in molecule.smi (2660/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332550 in molecule.smi (2661/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332549 in molecule.smi (2662/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332548 in molecule.smi (2663/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332547 in molecule.smi (2664/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL152675 in molecule.smi (2666/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL350032 in molecule.smi (2665/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332546 in molecule.smi (2668/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL454594 in molecule.smi (2667/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332536 in molecule.smi (2670/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332537 in molecule.smi (2669/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332539 in molecule.smi (2672/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332535 in molecule.smi (2671/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL156016 in molecule.smi (2674/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2332538 in molecule.smi (2673/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2377874 in molecule.smi (2675/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2377878 in molecule.smi (2676/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2377877 in molecule.smi (2677/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2377876 in molecule.smi (2678/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2377875 in molecule.smi (2679/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2375480 in molecule.smi (2680/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2377873 in molecule.smi (2681/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2375483 in molecule.smi (2682/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2375482 in molecule.smi (2683/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2375481 in molecule.smi (2684/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380667 in molecule.smi (2685/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380677 in molecule.smi (2686/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380676 in molecule.smi (2687/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380675 in molecule.smi (2688/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380674 in molecule.smi (2689/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380672 in molecule.smi (2691/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380673 in molecule.smi (2690/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380670 in molecule.smi (2693/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380671 in molecule.smi (2692/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380668 in molecule.smi (2695/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2380669 in molecule.smi (2694/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2386558 in molecule.smi (2697/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2381401 in molecule.smi (2696/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396914 in molecule.smi (2698/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396913 in molecule.smi (2699/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1429841 in molecule.smi (2701/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396912 in molecule.smi (2700/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396910 in molecule.smi (2703/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396911 in molecule.smi (2702/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396908 in molecule.smi (2705/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396909 in molecule.smi (2704/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396907 in molecule.smi (2707/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1572608 in molecule.smi (2706/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396906 in molecule.smi (2708/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396904 in molecule.smi (2710/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396905 in molecule.smi (2709/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1376358 in molecule.smi (2712/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2396903 in molecule.smi (2711/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL145 in molecule.smi (2713/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2402007 in molecule.smi (2715/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1443379 in molecule.smi (2714/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL164660 in molecule.smi (2716/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1079905 in molecule.smi (2717/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL113 in molecule.smi (2719/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL628 in molecule.smi (2718/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2409142 in molecule.smi (2721/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1424 in molecule.smi (2720/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2409141 in molecule.smi (2723/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL448799 in molecule.smi (2722/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413744 in molecule.smi (2725/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2409140 in molecule.smi (2724/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413742 in molecule.smi (2727/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413743 in molecule.smi (2726/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413740 in molecule.smi (2729/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413741 in molecule.smi (2728/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413738 in molecule.smi (2731/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413739 in molecule.smi (2730/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413736 in molecule.smi (2733/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413737 in molecule.smi (2732/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413734 in molecule.smi (2735/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413735 in molecule.smi (2734/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2413732 in molecule.smi (2737/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413733 in molecule.smi (2736/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413561 in molecule.smi (2738/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413560 in molecule.smi (2739/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413559 in molecule.smi (2740/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413558 in molecule.smi (2741/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413745 in molecule.smi (2742/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2413746 in molecule.smi (2743/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL377740 in molecule.smi (2744/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419690 in molecule.smi (2745/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419688 in molecule.smi (2747/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419689 in molecule.smi (2746/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419687 in molecule.smi (2748/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419686 in molecule.smi (2749/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419684 in molecule.smi (2751/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419685 in molecule.smi (2750/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419683 in molecule.smi (2752/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419681 in molecule.smi (2754/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2419682 in molecule.smi (2753/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425415 in molecule.smi (2755/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425414 in molecule.smi (2756/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425413 in molecule.smi (2757/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425412 in molecule.smi (2758/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425410 in molecule.smi (2759/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425409 in molecule.smi (2760/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425408 in molecule.smi (2761/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425407 in molecule.smi (2762/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425418 in molecule.smi (2763/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2425417 in molecule.smi (2764/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441695 in molecule.smi (2765/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441694 in molecule.smi (2766/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441693 in molecule.smi (2767/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441692 in molecule.smi (2768/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441691 in molecule.smi (2769/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441690 in molecule.smi (2770/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441689 in molecule.smi (2771/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441688 in molecule.smi (2772/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441687 in molecule.smi (2773/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441686 in molecule.smi (2774/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441685 in molecule.smi (2775/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441684 in molecule.smi (2776/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2440134 in molecule.smi (2777/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441683 in molecule.smi (2778/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441682 in molecule.smi (2779/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441681 in molecule.smi (2780/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441680 in molecule.smi (2781/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441679 in molecule.smi (2782/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441678 in molecule.smi (2783/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441676 in molecule.smi (2785/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441677 in molecule.smi (2784/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441674 in molecule.smi (2787/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441675 in molecule.smi (2786/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441672 in molecule.smi (2789/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441673 in molecule.smi (2788/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441670 in molecule.smi (2791/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441671 in molecule.smi (2790/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441669 in molecule.smi (2792/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441668 in molecule.smi (2793/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441667 in molecule.smi (2794/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441666 in molecule.smi (2795/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL2441665 in molecule.smi (2796/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087683 in molecule.smi (2797/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087682 in molecule.smi (2798/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087681 in molecule.smi (2799/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087680 in molecule.smi (2800/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087679 in molecule.smi (2801/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087677 in molecule.smi (2802/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087676 in molecule.smi (2803/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087675 in molecule.smi (2804/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3087674 in molecule.smi (2805/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3085881 in molecule.smi (2806/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL398628 in molecule.smi (2808/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL430403 in molecule.smi (2807/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3086278 in molecule.smi (2810/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3086279 in molecule.smi (2809/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3086277 in molecule.smi (2811/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093180 in molecule.smi (2813/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3086276 in molecule.smi (2812/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093174 in molecule.smi (2815/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093175 in molecule.smi (2814/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093791 in molecule.smi (2817/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093173 in molecule.smi (2816/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093804 in molecule.smi (2819/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093787 in molecule.smi (2818/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093802 in molecule.smi (2821/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093803 in molecule.smi (2820/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093801 in molecule.smi (2822/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093800 in molecule.smi (2823/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093799 in molecule.smi (2824/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093798 in molecule.smi (2825/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093797 in molecule.smi (2826/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093796 in molecule.smi (2827/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093795 in molecule.smi (2828/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3093794 in molecule.smi (2829/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL187266 in molecule.smi (2830/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL363535 in molecule.smi (2831/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL227075 in molecule.smi (2832/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3099500 in molecule.smi (2833/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL51085 in molecule.smi (2834/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115026 in molecule.smi (2835/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3112614 in molecule.smi (2836/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115025 in molecule.smi (2837/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115024 in molecule.smi (2838/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115023 in molecule.smi (2839/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115022 in molecule.smi (2840/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115021 in molecule.smi (2841/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115020 in molecule.smi (2842/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115029 in molecule.smi (2843/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115028 in molecule.smi (2844/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115027 in molecule.smi (2845/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115038 in molecule.smi (2846/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115037 in molecule.smi (2847/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115036 in molecule.smi (2848/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL3115035 in molecule.smi (2849/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115034 in molecule.smi (2850/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115033 in molecule.smi (2851/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115032 in molecule.smi (2852/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115031 in molecule.smi (2853/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115030 in molecule.smi (2854/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115198 in molecule.smi (2855/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115197 in molecule.smi (2856/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115196 in molecule.smi (2857/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115048 in molecule.smi (2858/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115046 in molecule.smi (2860/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115047 in molecule.smi (2859/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115045 in molecule.smi (2861/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115044 in molecule.smi (2862/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115043 in molecule.smi (2863/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115042 in molecule.smi (2864/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115041 in molecule.smi (2865/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115040 in molecule.smi (2866/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3115039 in molecule.smi (2867/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116301 in molecule.smi (2868/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116300 in molecule.smi (2869/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116299 in molecule.smi (2870/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116298 in molecule.smi (2871/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116297 in molecule.smi (2872/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116296 in molecule.smi (2873/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116295 in molecule.smi (2874/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116294 in molecule.smi (2875/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116293 in molecule.smi (2876/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116292 in molecule.smi (2877/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116291 in molecule.smi (2878/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116290 in molecule.smi (2879/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116289 in molecule.smi (2880/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1973869 in molecule.smi (2881/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116288 in molecule.smi (2882/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116287 in molecule.smi (2883/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116286 in molecule.smi (2884/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116285 in molecule.smi (2885/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116284 in molecule.smi (2886/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116283 in molecule.smi (2887/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116282 in molecule.smi (2888/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116281 in molecule.smi (2889/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116280 in molecule.smi (2890/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116278 in molecule.smi (2892/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3116279 in molecule.smi (2891/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122169 in molecule.smi (2893/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122167 in molecule.smi (2895/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122168 in molecule.smi (2894/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122150 in molecule.smi (2897/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122166 in molecule.smi (2896/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122148 in molecule.smi (2899/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122149 in molecule.smi (2898/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122147 in molecule.smi (2900/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122145 in molecule.smi (2902/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122146 in molecule.smi (2901/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122172 in molecule.smi (2903/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3120185 in molecule.smi (2904/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122171 in molecule.smi (2905/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3122170 in molecule.smi (2906/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3127003 in molecule.smi (2907/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3126996 in molecule.smi (2908/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3127006 in molecule.smi (2909/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133436 in molecule.smi (2910/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133433 in molecule.smi (2912/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133434 in molecule.smi (2911/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133432 in molecule.smi (2913/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133430 in molecule.smi (2914/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133427 in molecule.smi (2915/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133444 in molecule.smi (2916/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133443 in molecule.smi (2917/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133442 in molecule.smi (2918/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133440 in molecule.smi (2919/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3133439 in molecule.smi (2920/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220507 in molecule.smi (2921/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1325943 in molecule.smi (2922/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220508 in molecule.smi (2923/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220509 in molecule.smi (2924/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220510 in molecule.smi (2925/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220801 in molecule.smi (2927/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220513 in molecule.smi (2926/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220803 in molecule.smi (2929/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220802 in molecule.smi (2928/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220804 in molecule.smi (2930/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220805 in molecule.smi (2931/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220806 in molecule.smi (2932/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220807 in molecule.smi (2933/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3220808 in molecule.smi (2934/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237628 in molecule.smi (2935/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237629 in molecule.smi (2936/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237630 in molecule.smi (2937/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237631 in molecule.smi (2938/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237632 in molecule.smi (2939/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237633 in molecule.smi (2940/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237634 in molecule.smi (2941/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237520 in molecule.smi (2942/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1377579 in molecule.smi (2944/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3237527 in molecule.smi (2943/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1344483 in molecule.smi (2945/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233990 in molecule.smi (2946/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233991 in molecule.smi (2947/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233022 in molecule.smi (2948/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233992 in molecule.smi (2949/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1191355 in molecule.smi (2951/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233993 in molecule.smi (2950/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233994 in molecule.smi (2953/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1194868 in molecule.smi (2952/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233995 in molecule.smi (2954/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1190535 in molecule.smi (2956/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1192998 in molecule.smi (2955/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233996 in molecule.smi (2957/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233997 in molecule.smi (2958/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233999 in molecule.smi (2960/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233998 in molecule.smi (2959/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234000 in molecule.smi (2962/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1556015 in molecule.smi (2961/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1611608 in molecule.smi (2963/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL388560 in molecule.smi (2965/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234001 in molecule.smi (2964/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3235503 in molecule.smi (2967/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3235502 in molecule.smi (2966/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2171309 in molecule.smi (2968/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234591 in molecule.smi (2969/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234587 in molecule.smi (2971/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234592 in molecule.smi (2970/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234589 in molecule.smi (2973/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234588 in molecule.smi (2972/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234590 in molecule.smi (2974/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234593 in molecule.smi (2975/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234594 in molecule.smi (2976/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233826 in molecule.smi (2977/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233827 in molecule.smi (2978/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233828 in molecule.smi (2979/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233829 in molecule.smi (2980/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233830 in molecule.smi (2981/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233831 in molecule.smi (2982/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233832 in molecule.smi (2983/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234038 in molecule.smi (2984/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234039 in molecule.smi (2985/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3234040 in molecule.smi (2986/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL418068 in molecule.smi (2987/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2007996 in molecule.smi (2988/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233935 in molecule.smi (2990/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3233934 in molecule.smi (2989/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261985 in molecule.smi (2992/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261984 in molecule.smi (2991/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261987 in molecule.smi (2994/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261986 in molecule.smi (2993/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261989 in molecule.smi (2996/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261988 in molecule.smi (2995/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261990 in molecule.smi (2997/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261991 in molecule.smi (2998/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3261992 in molecule.smi (2999/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262658 in molecule.smi (3000/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262661 in molecule.smi (3001/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262662 in molecule.smi (3002/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262663 in molecule.smi (3003/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262664 in molecule.smi (3004/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262665 in molecule.smi (3005/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262666 in molecule.smi (3006/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262668 in molecule.smi (3008/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262667 in molecule.smi (3007/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262670 in molecule.smi (3010/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262669 in molecule.smi (3009/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262671 in molecule.smi (3011/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262673 in molecule.smi (3013/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3262672 in molecule.smi (3012/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263725 in molecule.smi (3015/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL510363 in molecule.smi (3014/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263726 in molecule.smi (3017/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263724 in molecule.smi (3016/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263728 in molecule.smi (3019/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263727 in molecule.smi (3018/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263730 in molecule.smi (3021/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263729 in molecule.smi (3020/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263732 in molecule.smi (3023/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263731 in molecule.smi (3022/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263733 in molecule.smi (3024/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263734 in molecule.smi (3025/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263735 in molecule.smi (3026/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263736 in molecule.smi (3027/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3263737 in molecule.smi (3028/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289924 in molecule.smi (3029/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289925 in molecule.smi (3030/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289926 in molecule.smi (3031/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289927 in molecule.smi (3032/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289928 in molecule.smi (3033/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289929 in molecule.smi (3034/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289931 in molecule.smi (3036/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289930 in molecule.smi (3035/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289932 in molecule.smi (3037/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289933 in molecule.smi (3038/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289934 in molecule.smi (3039/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289936 in molecule.smi (3041/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289935 in molecule.smi (3040/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289938 in molecule.smi (3043/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289937 in molecule.smi (3042/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289939 in molecule.smi (3044/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289941 in molecule.smi (3046/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289940 in molecule.smi (3045/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289942 in molecule.smi (3047/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3289943 in molecule.smi (3048/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290187 in molecule.smi (3049/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290188 in molecule.smi (3050/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290189 in molecule.smi (3051/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290190 in molecule.smi (3052/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290192 in molecule.smi (3054/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290191 in molecule.smi (3053/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290193 in molecule.smi (3055/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290195 in molecule.smi (3057/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290194 in molecule.smi (3056/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3290196 in molecule.smi (3058/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL40275 in molecule.smi (3059/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL385181 in molecule.smi (3060/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL32778 in molecule.smi (3061/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3360486 in molecule.smi (3062/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3360487 in molecule.smi (3063/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353039 in molecule.smi (3064/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353040 in molecule.smi (3065/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353041 in molecule.smi (3066/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353042 in molecule.smi (3067/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353566 in molecule.smi (3068/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353567 in molecule.smi (3069/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3353568 in molecule.smi (3070/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323068 in molecule.smi (3072/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3361270 in molecule.smi (3071/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323065 in molecule.smi (3073/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323062 in molecule.smi (3074/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323059 in molecule.smi (3075/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323056 in molecule.smi (3076/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323053 in molecule.smi (3077/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323051 in molecule.smi (3078/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323049 in molecule.smi (3079/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323046 in molecule.smi (3080/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323043 in molecule.smi (3081/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323042 in molecule.smi (3082/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323039 in molecule.smi (3083/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323036 in molecule.smi (3084/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3323035 in molecule.smi (3085/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322157 in molecule.smi (3086/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322140 in molecule.smi (3087/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322141 in molecule.smi (3088/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322142 in molecule.smi (3089/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322158 in molecule.smi (3090/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322159 in molecule.smi (3091/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322160 in molecule.smi (3092/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322161 in molecule.smi (3093/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322162 in molecule.smi (3094/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322155 in molecule.smi (3095/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322156 in molecule.smi (3096/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3313966 in molecule.smi (3097/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1134 in molecule.smi (3098/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322231 in molecule.smi (3100/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1196204 in molecule.smi (3099/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322233 in molecule.smi (3102/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322234 in molecule.smi (3101/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3322232 in molecule.smi (3103/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL278020 in molecule.smi (3104/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3310703 in molecule.smi (3105/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3360758 in molecule.smi (3106/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3310705 in molecule.smi (3107/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3360757 in molecule.smi (3109/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3310704 in molecule.smi (3108/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3310697 in molecule.smi (3111/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3310706 in molecule.smi (3110/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3318018 in molecule.smi (3113/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3310699 in molecule.smi (3112/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2391486 in molecule.smi (3115/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3318017 in molecule.smi (3114/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL276473 in molecule.smi (3116/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2158994 in molecule.smi (3117/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338992 in molecule.smi (3119/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338991 in molecule.smi (3118/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338994 in molecule.smi (3121/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338993 in molecule.smi (3120/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338995 in molecule.smi (3123/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3337471 in molecule.smi (3122/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338997 in molecule.smi (3125/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338996 in molecule.smi (3124/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338998 in molecule.smi (3126/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338999 in molecule.smi (3127/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339000 in molecule.smi (3128/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339001 in molecule.smi (3129/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339002 in molecule.smi (3130/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339003 in molecule.smi (3131/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339004 in molecule.smi (3132/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339005 in molecule.smi (3133/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339006 in molecule.smi (3134/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339007 in molecule.smi (3135/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3339008 in molecule.smi (3136/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3355581 in molecule.smi (3137/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3355580 in molecule.smi (3138/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3355579 in molecule.smi (3139/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL151763 in molecule.smi (3140/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335072 in molecule.smi (3141/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335071 in molecule.smi (3142/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335070 in molecule.smi (3143/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3334506 in molecule.smi (3144/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335068 in molecule.smi (3146/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335069 in molecule.smi (3145/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335066 in molecule.smi (3148/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335067 in molecule.smi (3147/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335064 in molecule.smi (3150/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335065 in molecule.smi (3149/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335062 in molecule.smi (3152/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335063 in molecule.smi (3151/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335061 in molecule.smi (3153/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335060 in molecule.smi (3154/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335059 in molecule.smi (3155/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335058 in molecule.smi (3156/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335057 in molecule.smi (3157/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335056 in molecule.smi (3158/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335055 in molecule.smi (3159/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335054 in molecule.smi (3160/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335053 in molecule.smi (3161/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335052 in molecule.smi (3162/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335051 in molecule.smi (3163/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335050 in molecule.smi (3164/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335049 in molecule.smi (3165/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335048 in molecule.smi (3166/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335047 in molecule.smi (3167/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335046 in molecule.smi (3168/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335032 in molecule.smi (3169/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335031 in molecule.smi (3170/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335030 in molecule.smi (3171/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335029 in molecule.smi (3172/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335028 in molecule.smi (3173/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335027 in molecule.smi (3174/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335026 in molecule.smi (3175/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335025 in molecule.smi (3176/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335024 in molecule.smi (3177/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335023 in molecule.smi (3178/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335022 in molecule.smi (3179/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335021 in molecule.smi (3180/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335020 in molecule.smi (3181/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335019 in molecule.smi (3182/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335017 in molecule.smi (3184/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3335018 in molecule.smi (3183/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343708 in molecule.smi (3186/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343705 in molecule.smi (3185/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343712 in molecule.smi (3188/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343711 in molecule.smi (3187/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343714 in molecule.smi (3190/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343713 in molecule.smi (3189/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3355594 in molecule.smi (3191/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338389 in molecule.smi (3192/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338388 in molecule.smi (3193/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338387 in molecule.smi (3194/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3338386 in molecule.smi (3195/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343884 in molecule.smi (3196/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343885 in molecule.smi (3197/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343920 in molecule.smi (3198/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343922 in molecule.smi (3199/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343882 in molecule.smi (3200/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343923 in molecule.smi (3201/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343925 in molecule.smi (3203/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343924 in molecule.smi (3202/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343927 in molecule.smi (3205/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343926 in molecule.smi (3204/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343929 in molecule.smi (3207/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343928 in molecule.smi (3206/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343931 in molecule.smi (3209/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343930 in molecule.smi (3208/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356528 in molecule.smi (3211/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3343932 in molecule.smi (3210/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356529 in molecule.smi (3212/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356531 in molecule.smi (3214/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356530 in molecule.smi (3213/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356533 in molecule.smi (3216/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356532 in molecule.smi (3215/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356535 in molecule.smi (3218/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356534 in molecule.smi (3217/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356537 in molecule.smi (3220/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356536 in molecule.smi (3219/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356539 in molecule.smi (3222/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356538 in molecule.smi (3221/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356950 in molecule.smi (3224/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356540 in molecule.smi (3223/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356951 in molecule.smi (3225/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356953 in molecule.smi (3227/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356952 in molecule.smi (3226/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3356954 in molecule.smi (3228/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3393675 in molecule.smi (3229/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3393674 in molecule.smi (3230/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3393673 in molecule.smi (3231/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403878 in molecule.smi (3232/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403877 in molecule.smi (3233/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3400187 in molecule.smi (3234/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403874 in molecule.smi (3235/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402707 in molecule.smi (3236/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402708 in molecule.smi (3237/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402709 in molecule.smi (3238/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402710 in molecule.smi (3239/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402712 in molecule.smi (3240/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402713 in molecule.smi (3241/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402714 in molecule.smi (3242/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402715 in molecule.smi (3243/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402717 in molecule.smi (3244/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402718 in molecule.smi (3245/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402719 in molecule.smi (3246/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3402720 in molecule.smi (3247/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403305 in molecule.smi (3248/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403307 in molecule.smi (3250/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403306 in molecule.smi (3249/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403309 in molecule.smi (3252/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403308 in molecule.smi (3251/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403311 in molecule.smi (3254/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403310 in molecule.smi (3253/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403312 in molecule.smi (3255/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403314 in molecule.smi (3257/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403313 in molecule.smi (3256/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403315 in molecule.smi (3258/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3403316 in molecule.smi (3259/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407587 in molecule.smi (3260/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407586 in molecule.smi (3261/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407585 in molecule.smi (3262/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407584 in molecule.smi (3263/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407583 in molecule.smi (3264/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407582 in molecule.smi (3265/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407581 in molecule.smi (3266/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3407591 in molecule.smi (3267/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410957 in molecule.smi (3268/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410956 in molecule.smi (3269/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410955 in molecule.smi (3270/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410954 in molecule.smi (3271/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410953 in molecule.smi (3272/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410952 in molecule.smi (3273/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3410951 in molecule.smi (3274/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417009 in molecule.smi (3275/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL85251 in molecule.smi (3276/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3414597 in molecule.smi (3277/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3416996 in molecule.smi (3279/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3416995 in molecule.smi (3278/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3416998 in molecule.smi (3281/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3416997 in molecule.smi (3280/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417000 in molecule.smi (3283/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3416999 in molecule.smi (3282/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417002 in molecule.smi (3285/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417001 in molecule.smi (3284/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417004 in molecule.smi (3287/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417003 in molecule.smi (3286/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417006 in molecule.smi (3288/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417008 in molecule.smi (3289/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415643 in molecule.smi (3290/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415558 in molecule.smi (3291/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415559 in molecule.smi (3292/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415560 in molecule.smi (3293/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415563 in molecule.smi (3295/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415561 in molecule.smi (3294/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415564 in molecule.smi (3296/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415566 in molecule.smi (3298/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415565 in molecule.smi (3297/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415568 in molecule.smi (3299/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415570 in molecule.smi (3301/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415569 in molecule.smi (3300/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415573 in molecule.smi (3302/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415574 in molecule.smi (3303/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415575 in molecule.smi (3304/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415576 in molecule.smi (3305/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415578 in molecule.smi (3306/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415579 in molecule.smi (3307/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415580 in molecule.smi (3308/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3415581 in molecule.smi (3309/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417300 in molecule.smi (3310/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3417299 in molecule.smi (3311/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577505 in molecule.smi (3312/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577508 in molecule.smi (3313/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577509 in molecule.smi (3314/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577510 in molecule.smi (3315/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577511 in molecule.smi (3316/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577517 in molecule.smi (3317/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577520 in molecule.smi (3318/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577522 in molecule.smi (3319/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3359895 in molecule.smi (3321/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3577524 in molecule.smi (3320/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582200 in molecule.smi (3322/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582202 in molecule.smi (3324/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582201 in molecule.smi (3323/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582204 in molecule.smi (3326/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582203 in molecule.smi (3325/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582206 in molecule.smi (3328/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582205 in molecule.smi (3327/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582208 in molecule.smi (3330/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582207 in molecule.smi (3329/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582210 in molecule.smi (3332/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582209 in molecule.smi (3331/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582212 in molecule.smi (3334/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582211 in molecule.smi (3333/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582213 in molecule.smi (3335/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582214 in molecule.smi (3336/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582215 in molecule.smi (3337/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582218 in molecule.smi (3339/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582216 in molecule.smi (3338/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582224 in molecule.smi (3341/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582223 in molecule.smi (3340/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582226 in molecule.smi (3343/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582225 in molecule.smi (3342/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582228 in molecule.smi (3345/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582227 in molecule.smi (3344/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582230 in molecule.smi (3347/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582229 in molecule.smi (3346/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582231 in molecule.smi (3348/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3582232 in molecule.smi (3349/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3581580 in molecule.smi (3350/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3581576 in molecule.smi (3351/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3581573 in molecule.smi (3352/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3581572 in molecule.smi (3353/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3585374 in molecule.smi (3354/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586574 in molecule.smi (3356/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3585375 in molecule.smi (3355/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586576 in molecule.smi (3357/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586577 in molecule.smi (3358/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586582 in molecule.smi (3359/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586583 in molecule.smi (3360/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586592 in molecule.smi (3361/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586593 in molecule.smi (3362/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586594 in molecule.smi (3363/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586595 in molecule.smi (3364/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586600 in molecule.smi (3365/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586602 in molecule.smi (3366/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586604 in molecule.smi (3367/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586605 in molecule.smi (3368/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586608 in molecule.smi (3369/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586610 in molecule.smi (3370/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586614 in molecule.smi (3371/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586196 in molecule.smi (3372/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586197 in molecule.smi (3373/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586198 in molecule.smi (3374/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586199 in molecule.smi (3375/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586201 in molecule.smi (3377/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586200 in molecule.smi (3376/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586202 in molecule.smi (3378/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL506258 in molecule.smi (3380/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586203 in molecule.smi (3379/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586205 in molecule.smi (3382/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3586204 in molecule.smi (3381/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3586206 in molecule.smi (3383/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3586207 in molecule.smi (3384/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL295124 in molecule.smi (3385/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585777 in molecule.smi (3386/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585776 in molecule.smi (3387/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585775 in molecule.smi (3388/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585778 in molecule.smi (3389/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585779 in molecule.smi (3390/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585780 in molecule.smi (3391/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585781 in molecule.smi (3392/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585782 in molecule.smi (3393/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585783 in molecule.smi (3394/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3585784 in molecule.smi (3395/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597939 in molecule.smi (3396/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597941 in molecule.smi (3397/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597942 in molecule.smi (3398/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597946 in molecule.smi (3400/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597944 in molecule.smi (3399/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597948 in molecule.smi (3401/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597548 in molecule.smi (3403/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597556 in molecule.smi (3402/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597068 in molecule.smi (3404/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597067 in molecule.smi (3405/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597064 in molecule.smi (3407/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3597065 in molecule.smi (3406/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597063 in molecule.smi (3408/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597062 in molecule.smi (3409/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597061 in molecule.smi (3410/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597060 in molecule.smi (3411/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597059 in molecule.smi (3412/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597058 in molecule.smi (3413/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597057 in molecule.smi (3414/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597056 in molecule.smi (3415/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597055 in molecule.smi (3416/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597054 in molecule.smi (3417/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597010 in molecule.smi (3418/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597009 in molecule.smi (3419/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597008 in molecule.smi (3420/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597007 in molecule.smi (3421/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597005 in molecule.smi (3423/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597006 in molecule.smi (3422/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597003 in molecule.smi (3425/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597004 in molecule.smi (3424/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597001 in molecule.smi (3427/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597002 in molecule.smi (3426/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3600551 in molecule.smi (3429/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3597000 in molecule.smi (3428/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3600553 in molecule.smi (3431/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3600552 in molecule.smi (3430/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3600554 in molecule.smi (3432/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3600555 in molecule.smi (3433/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3600556 in molecule.smi (3434/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605437 in molecule.smi (3435/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605436 in molecule.smi (3436/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605434 in molecule.smi (3437/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605433 in molecule.smi (3438/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605432 in molecule.smi (3439/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605431 in molecule.smi (3440/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605430 in molecule.smi (3441/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605428 in molecule.smi (3442/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605426 in molecule.smi (3443/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605425 in molecule.smi (3444/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605424 in molecule.smi (3445/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605423 in molecule.smi (3446/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605422 in molecule.smi (3447/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605419 in molecule.smi (3448/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605418 in molecule.smi (3449/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605316 in molecule.smi (3450/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605314 in molecule.smi (3451/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605313 in molecule.smi (3452/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605310 in molecule.smi (3453/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605309 in molecule.smi (3454/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3605308 in molecule.smi (3455/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604192 in molecule.smi (3456/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604193 in molecule.smi (3457/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604194 in molecule.smi (3458/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604195 in molecule.smi (3459/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604196 in molecule.smi (3460/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604197 in molecule.smi (3461/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604198 in molecule.smi (3462/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3604199 in molecule.smi (3463/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3621335 in molecule.smi (3464/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3621334 in molecule.smi (3465/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3621333 in molecule.smi (3466/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3621332 in molecule.smi (3467/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623558 in molecule.smi (3469/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3621322 in molecule.smi (3468/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623557 in molecule.smi (3470/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623556 in molecule.smi (3471/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623555 in molecule.smi (3472/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623554 in molecule.smi (3473/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623553 in molecule.smi (3474/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623552 in molecule.smi (3475/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623551 in molecule.smi (3476/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623550 in molecule.smi (3477/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623549 in molecule.smi (3478/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623548 in molecule.smi (3479/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL89354 in molecule.smi (3480/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3137700 in molecule.smi (3481/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL167911 in molecule.smi (3482/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL446060 in molecule.smi (3483/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623563 in molecule.smi (3484/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623562 in molecule.smi (3485/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623561 in molecule.smi (3486/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623560 in molecule.smi (3487/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623559 in molecule.smi (3488/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623567 in molecule.smi (3489/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623566 in molecule.smi (3490/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623565 in molecule.smi (3491/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3623564 in molecule.smi (3492/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628053 in molecule.smi (3493/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628054 in molecule.smi (3494/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628055 in molecule.smi (3495/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628056 in molecule.smi (3496/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628057 in molecule.smi (3497/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628058 in molecule.smi (3498/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628059 in molecule.smi (3499/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628061 in molecule.smi (3501/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628060 in molecule.smi (3500/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628063 in molecule.smi (3503/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628062 in molecule.smi (3502/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628065 in molecule.smi (3505/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628064 in molecule.smi (3504/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628067 in molecule.smi (3507/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628066 in molecule.smi (3506/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628183 in molecule.smi (3508/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628184 in molecule.smi (3509/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628185 in molecule.smi (3510/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3628186 in molecule.smi (3511/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632858 in molecule.smi (3512/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632857 in molecule.smi (3513/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632856 in molecule.smi (3514/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632855 in molecule.smi (3515/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632853 in molecule.smi (3517/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632854 in molecule.smi (3516/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632846 in molecule.smi (3519/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632852 in molecule.smi (3518/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632847 in molecule.smi (3520/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632626 in molecule.smi (3521/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632848 in molecule.smi (3522/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632849 in molecule.smi (3523/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632850 in molecule.smi (3524/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632851 in molecule.smi (3525/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632987 in molecule.smi (3526/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632988 in molecule.smi (3527/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632989 in molecule.smi (3528/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632991 in molecule.smi (3530/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632990 in molecule.smi (3529/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632992 in molecule.smi (3531/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632993 in molecule.smi (3532/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL153 in molecule.smi (3534/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3632994 in molecule.smi (3533/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642057 in molecule.smi (3535/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642053 in molecule.smi (3537/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642042 in molecule.smi (3536/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642043 in molecule.smi (3538/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642045 in molecule.smi (3539/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642054 in molecule.smi (3540/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642047 in molecule.smi (3541/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642049 in molecule.smi (3542/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642041 in molecule.smi (3543/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642046 in molecule.smi (3544/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642050 in molecule.smi (3545/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642052 in molecule.smi (3546/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642055 in molecule.smi (3547/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642044 in molecule.smi (3548/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642051 in molecule.smi (3549/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642040 in molecule.smi (3550/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642048 in molecule.smi (3551/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3642056 in molecule.smi (3552/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3736339 in molecule.smi (3553/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3735606 in molecule.smi (3554/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3734787 in molecule.smi (3555/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3735042 in molecule.smi (3556/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL2179375 in molecule.smi (3557/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3401159 in molecule.smi (3558/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752974 in molecule.smi (3559/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752232 in molecule.smi (3560/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754631 in molecule.smi (3561/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754448 in molecule.smi (3562/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752908 in molecule.smi (3563/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754585 in molecule.smi (3564/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754622 in molecule.smi (3565/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752710 in molecule.smi (3566/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL189886 in molecule.smi (3567/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754480 in molecule.smi (3568/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754739 in molecule.smi (3569/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754146 in molecule.smi (3571/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752926 in molecule.smi (3570/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752356 in molecule.smi (3573/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3751941 in molecule.smi (3572/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752543 in molecule.smi (3575/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752969 in molecule.smi (3574/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3753405 in molecule.smi (3577/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752467 in molecule.smi (3576/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754694 in molecule.smi (3579/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754341 in molecule.smi (3578/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754778 in molecule.smi (3581/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3753957 in molecule.smi (3580/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3753360 in molecule.smi (3583/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752384 in molecule.smi (3582/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754005 in molecule.smi (3585/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754672 in molecule.smi (3584/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3751866 in molecule.smi (3587/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752685 in molecule.smi (3586/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3754291 in molecule.smi (3588/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3753850 in molecule.smi (3590/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752119 in molecule.smi (3589/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3753706 in molecule.smi (3592/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3753508 in molecule.smi (3591/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752555 in molecule.smi (3594/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752451 in molecule.smi (3593/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3752227 in molecule.smi (3595/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3758282 in molecule.smi (3597/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3759090 in molecule.smi (3596/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL395280 in molecule.smi (3599/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3759013 in molecule.smi (3598/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764433 in molecule.smi (3600/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764714 in molecule.smi (3602/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764900 in molecule.smi (3601/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764065 in molecule.smi (3604/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763417 in molecule.smi (3603/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764488 in molecule.smi (3605/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765098 in molecule.smi (3607/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763832 in molecule.smi (3606/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763973 in molecule.smi (3608/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764376 in molecule.smi (3610/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763232 in molecule.smi (3609/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765476 in molecule.smi (3612/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763609 in molecule.smi (3611/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763820 in molecule.smi (3613/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765697 in molecule.smi (3614/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763482 in molecule.smi (3616/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765569 in molecule.smi (3615/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765093 in molecule.smi (3617/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764100 in molecule.smi (3618/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765496 in molecule.smi (3620/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764679 in molecule.smi (3619/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763778 in molecule.smi (3621/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763203 in molecule.smi (3622/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764305 in molecule.smi (3624/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765162 in molecule.smi (3623/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764609 in molecule.smi (3626/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3765293 in molecule.smi (3625/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3763535 in molecule.smi (3628/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764762 in molecule.smi (3627/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769555 in molecule.smi (3630/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3764211 in molecule.smi (3629/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770203 in molecule.smi (3632/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769537 in molecule.smi (3631/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770803 in molecule.smi (3634/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770577 in molecule.smi (3633/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769862 in molecule.smi (3636/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770479 in molecule.smi (3635/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769833 in molecule.smi (3638/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770978 in molecule.smi (3637/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769576 in molecule.smi (3639/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769811 in molecule.smi (3641/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3771233 in molecule.smi (3640/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3769834 in molecule.smi (3643/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770569 in molecule.smi (3642/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770071 in molecule.smi (3644/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770206 in molecule.smi (3645/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1801816 in molecule.smi (3646/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770866 in molecule.smi (3647/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770516 in molecule.smi (3648/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1801815 in molecule.smi (3649/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1411769 in molecule.smi (3650/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3770356 in molecule.smi (3651/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1327885 in molecule.smi (3652/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL239618 in molecule.smi (3653/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL240047 in molecule.smi (3654/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL394753 in molecule.smi (3655/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL239400 in molecule.smi (3656/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL828 in molecule.smi (3657/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3781564 in molecule.smi (3658/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3780448 in molecule.smi (3659/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3780131 in molecule.smi (3660/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3787116 in molecule.smi (3661/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785861 in molecule.smi (3662/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786544 in molecule.smi (3663/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3787307 in molecule.smi (3664/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786666 in molecule.smi (3665/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785481 in molecule.smi (3666/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786655 in molecule.smi (3667/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785526 in molecule.smi (3668/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786843 in molecule.smi (3669/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786447 in molecule.smi (3670/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3787666 in molecule.smi (3671/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785229 in molecule.smi (3672/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785187 in molecule.smi (3673/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785803 in molecule.smi (3674/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786323 in molecule.smi (3675/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786505 in molecule.smi (3677/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786589 in molecule.smi (3676/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785808 in molecule.smi (3679/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785644 in molecule.smi (3678/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786651 in molecule.smi (3680/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3787502 in molecule.smi (3682/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786516 in molecule.smi (3681/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3787223 in molecule.smi (3684/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785269 in molecule.smi (3683/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786937 in molecule.smi (3686/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786448 in molecule.smi (3685/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785391 in molecule.smi (3687/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3785207 in molecule.smi (3688/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786873 in molecule.smi (3689/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786442 in molecule.smi (3690/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3786719 in molecule.smi (3691/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3809337 in molecule.smi (3692/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1929043 in molecule.smi (3693/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL355160 in molecule.smi (3694/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818415 in molecule.smi (3695/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819155 in molecule.smi (3696/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818230 in molecule.smi (3697/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818616 in molecule.smi (3698/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819646 in molecule.smi (3699/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL168938 in molecule.smi (3700/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819220 in molecule.smi (3701/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818167 in molecule.smi (3702/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818801 in molecule.smi (3704/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818084 in molecule.smi (3703/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819113 in molecule.smi (3706/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819531 in molecule.smi (3705/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819574 in molecule.smi (3707/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819247 in molecule.smi (3708/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818918 in molecule.smi (3709/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819024 in molecule.smi (3710/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818374 in molecule.smi (3711/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL524100 in molecule.smi (3712/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL489354 in molecule.smi (3713/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1254269 in molecule.smi (3714/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819036 in molecule.smi (3715/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL535 in molecule.smi (3716/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL477772 in molecule.smi (3717/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818089 in molecule.smi (3718/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818086 in molecule.smi (3719/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819231 in molecule.smi (3720/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819320 in molecule.smi (3721/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818992 in molecule.smi (3722/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3817981 in molecule.smi (3723/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819548 in molecule.smi (3724/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818689 in molecule.smi (3726/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819383 in molecule.smi (3725/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3819083 in molecule.smi (3728/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818512 in molecule.smi (3727/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818990 in molecule.smi (3730/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818332 in molecule.smi (3729/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818250 in molecule.smi (3732/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3818782 in molecule.smi (3731/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3824239 in molecule.smi (3734/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3822559 in molecule.smi (3733/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3823464 in molecule.smi (3736/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3823317 in molecule.smi (3735/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3822670 in molecule.smi (3738/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3823538 in molecule.smi (3737/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3824057 in molecule.smi (3740/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3822790 in molecule.smi (3739/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3822890 in molecule.smi (3742/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3824362 in molecule.smi (3741/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3823197 in molecule.smi (3744/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3823578 in molecule.smi (3743/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3824248 in molecule.smi (3745/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828259 in molecule.smi (3746/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827734 in molecule.smi (3747/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827064 in molecule.smi (3748/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828631 in molecule.smi (3749/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827589 in molecule.smi (3750/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828679 in molecule.smi (3751/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828028 in molecule.smi (3753/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827011 in molecule.smi (3752/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827624 in molecule.smi (3754/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827449 in molecule.smi (3756/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827347 in molecule.smi (3755/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828273 in molecule.smi (3757/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828739 in molecule.smi (3758/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828644 in molecule.smi (3759/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827856 in molecule.smi (3760/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827058 in molecule.smi (3761/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827189 in molecule.smi (3762/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL440526 in molecule.smi (3763/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827288 in molecule.smi (3764/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827432 in molecule.smi (3765/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828293 in molecule.smi (3766/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828693 in molecule.smi (3767/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827610 in molecule.smi (3768/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827044 in molecule.smi (3769/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828054 in molecule.smi (3770/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827220 in molecule.smi (3771/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828696 in molecule.smi (3772/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827284 in molecule.smi (3773/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827950 in molecule.smi (3774/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3826998 in molecule.smi (3776/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828281 in molecule.smi (3775/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827765 in molecule.smi (3778/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828443 in molecule.smi (3777/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828586 in molecule.smi (3779/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828385 in molecule.smi (3780/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3828381 in molecule.smi (3781/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3827575 in molecule.smi (3782/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3978533 in molecule.smi (3784/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3949439 in molecule.smi (3783/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3945872 in molecule.smi (3786/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3919086 in molecule.smi (3785/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3936989 in molecule.smi (3788/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3917990 in molecule.smi (3787/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3892044 in molecule.smi (3790/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3965783 in molecule.smi (3789/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3905695 in molecule.smi (3792/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3916769 in molecule.smi (3791/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3892118 in molecule.smi (3794/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3931599 in molecule.smi (3793/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3901082 in molecule.smi (3796/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3961103 in molecule.smi (3795/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3910142 in molecule.smi (3798/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3972214 in molecule.smi (3797/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL972 in molecule.smi (3800/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL8706 in molecule.smi (3799/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3960962 in molecule.smi (3802/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3291019 in molecule.smi (3801/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3931517 in molecule.smi (3804/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1903032 in molecule.smi (3803/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3969241 in molecule.smi (3805/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1485276 in molecule.smi (3807/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3939698 in molecule.smi (3806/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3926757 in molecule.smi (3809/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL577016 in molecule.smi (3808/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1459580 in molecule.smi (3811/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3965046 in molecule.smi (3810/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3950130 in molecule.smi (3813/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3957099 in molecule.smi (3812/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3921061 in molecule.smi (3815/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL611941 in molecule.smi (3814/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL353468 in molecule.smi (3817/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3948367 in molecule.smi (3816/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL354848 in molecule.smi (3819/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL170946 in molecule.smi (3818/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3927363 in molecule.smi (3821/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3943450 in molecule.smi (3820/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3933395 in molecule.smi (3823/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3985059 in molecule.smi (3822/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3960861 in molecule.smi (3824/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3926911 in molecule.smi (3825/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3890758 in molecule.smi (3826/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3961494 in molecule.smi (3827/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3892502 in molecule.smi (3828/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL145361 in molecule.smi (3829/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL435071 in molecule.smi (3830/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL171048 in molecule.smi (3831/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3910513 in molecule.smi (3832/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3891369 in molecule.smi (3833/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3919441 in molecule.smi (3834/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3900401 in molecule.smi (3835/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3928343 in molecule.smi (3836/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3944652 in molecule.smi (3837/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3973337 in molecule.smi (3838/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3889713 in molecule.smi (3839/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3889625 in molecule.smi (3840/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3934564 in molecule.smi (3841/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3950063 in molecule.smi (3842/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3984034 in molecule.smi (3843/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3955393 in molecule.smi (3844/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3976003 in molecule.smi (3845/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3928840 in molecule.smi (3846/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3983017 in molecule.smi (3847/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3901794 in molecule.smi (3848/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3972865 in molecule.smi (3849/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3910830 in molecule.smi (3850/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3945423 in molecule.smi (3851/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3930722 in molecule.smi (3852/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3958208 in molecule.smi (3853/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3921785 in molecule.smi (3854/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3948489 in molecule.smi (3855/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3884618 in molecule.smi (3857/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3884988 in molecule.smi (3856/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3885186 in molecule.smi (3859/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3884858 in molecule.smi (3858/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3883432 in molecule.smi (3861/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3884690 in molecule.smi (3860/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3919289 in molecule.smi (3863/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3971112 in molecule.smi (3862/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3304357 in molecule.smi (3865/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3922423 in molecule.smi (3864/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3304306 in molecule.smi (3866/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4113560 in molecule.smi (3867/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4109726 in molecule.smi (3868/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3303094 in molecule.smi (3869/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3304241 in molecule.smi (3870/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3304178 in molecule.smi (3871/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4112162 in molecule.smi (3872/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3892148 in molecule.smi (3873/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3937964 in molecule.smi (3874/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3910025 in molecule.smi (3875/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3952144 in molecule.smi (3876/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3900968 in molecule.smi (3877/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3947803 in molecule.smi (3878/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3985136 in molecule.smi (3879/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3959939 in molecule.smi (3880/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3960964 in molecule.smi (3881/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3933508 in molecule.smi (3882/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3985662 in molecule.smi (3883/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3939015 in molecule.smi (3884/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3965431 in molecule.smi (3885/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3983878 in molecule.smi (3887/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3981946 in molecule.smi (3886/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3972317 in molecule.smi (3889/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3928459 in molecule.smi (3888/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3941497 in molecule.smi (3891/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3949770 in molecule.smi (3890/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3922471 in molecule.smi (3892/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3937974 in molecule.smi (3893/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3910033 in molecule.smi (3894/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3900977 in molecule.smi (3895/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3895528 in molecule.smi (3896/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3891315 in molecule.smi (3897/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3986650 in molecule.smi (3898/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3944088 in molecule.smi (3899/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3919972 in molecule.smi (3900/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3938676 in molecule.smi (3901/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3896153 in molecule.smi (3903/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3968230 in molecule.smi (3902/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3981703 in molecule.smi (3905/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3920662 in molecule.smi (3904/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4088116 in molecule.smi (3907/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3911730 in molecule.smi (3906/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093408 in molecule.smi (3908/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085671 in molecule.smi (3910/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4065799 in molecule.smi (3909/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4103604 in molecule.smi (3911/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4063045 in molecule.smi (3912/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4084714 in molecule.smi (3913/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4086323 in molecule.smi (3914/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064846 in molecule.smi (3915/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4078463 in molecule.smi (3916/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4067562 in molecule.smi (3918/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4102718 in molecule.smi (3917/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4105158 in molecule.smi (3920/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094981 in molecule.smi (3919/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4087300 in molecule.smi (3921/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072405 in molecule.smi (3922/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099340 in molecule.smi (3923/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4087978 in molecule.smi (3924/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4059684 in molecule.smi (3925/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095695 in molecule.smi (3926/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4068340 in molecule.smi (3927/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069385 in molecule.smi (3928/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4090413 in molecule.smi (3929/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4080105 in molecule.smi (3930/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098132 in molecule.smi (3931/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4074258 in molecule.smi (3932/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094761 in molecule.smi (3933/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075021 in molecule.smi (3934/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085283 in molecule.smi (3936/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4101703 in molecule.smi (3935/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093019 in molecule.smi (3938/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4063663 in molecule.smi (3937/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100736 in molecule.smi (3940/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072258 in molecule.smi (3939/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4062593 in molecule.smi (3942/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4082739 in molecule.smi (3941/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4090559 in molecule.smi (3943/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085738 in molecule.smi (3945/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4103664 in molecule.smi (3944/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104551 in molecule.smi (3947/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095908 in molecule.smi (3946/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4089082 in molecule.smi (3949/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4067497 in molecule.smi (3948/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4090583 in molecule.smi (3950/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075571 in molecule.smi (3951/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100760 in molecule.smi (3953/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094805 in molecule.smi (3952/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081805 in molecule.smi (3955/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4059887 in molecule.smi (3954/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4102562 in molecule.smi (3957/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098196 in molecule.smi (3956/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072914 in molecule.smi (3958/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069184 in molecule.smi (3959/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060687 in molecule.smi (3960/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081013 in molecule.smi (3961/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099046 in molecule.smi (3962/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070380 in molecule.smi (3963/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091317 in molecule.smi (3964/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4061746 in molecule.smi (3965/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4097374 in molecule.smi (3967/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083524 in molecule.smi (3966/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4105088 in molecule.smi (3969/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4079345 in molecule.smi (3968/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4067481 in molecule.smi (3971/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3262251 in molecule.smi (3970/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4078382 in molecule.smi (3973/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094911 in molecule.smi (3972/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4086248 in molecule.smi (3975/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4102655 in molecule.smi (3974/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064773 in molecule.smi (3976/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4061837 in molecule.smi (3977/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083601 in molecule.smi (3978/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070462 in molecule.smi (3979/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091385 in molecule.smi (3980/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100046 in molecule.smi (3981/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4096673 in molecule.smi (3983/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4073193 in molecule.smi (3982/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060016 in molecule.smi (3985/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069444 in molecule.smi (3984/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085114 in molecule.smi (3986/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098421 in molecule.smi (3987/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4077088 in molecule.smi (3988/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099790 in molecule.smi (3989/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1626455 in molecule.smi (3990/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095445 in molecule.smi (3991/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL2337271 in molecule.smi (3992/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL215645 in molecule.smi (3993/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060617 in molecule.smi (3994/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081795 in molecule.smi (3995/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066309 in molecule.smi (3996/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093419 in molecule.smi (3997/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083174 in molecule.smi (3998/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL521589 in molecule.smi (3999/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4096024 in molecule.smi (4000/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098455 in molecule.smi (4001/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099598 in molecule.smi (4002/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4062146 in molecule.smi (4003/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083875 in molecule.smi (4004/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070769 in molecule.smi (4005/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091651 in molecule.smi (4006/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4092602 in molecule.smi (4007/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064904 in molecule.smi (4008/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100338 in molecule.smi (4009/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4073501 in molecule.smi (4010/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4074566 in molecule.smi (4011/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095032 in molecule.smi (4012/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060800 in molecule.smi (4013/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085222 in molecule.smi (4014/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4092048 in molecule.smi (4015/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4061527 in molecule.smi (4016/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081776 in molecule.smi (4017/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4105085 in molecule.smi (4019/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099794 in molecule.smi (4018/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083445 in molecule.smi (4020/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093567 in molecule.smi (4021/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072865 in molecule.smi (4022/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4101303 in molecule.smi (4023/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4076930 in molecule.smi (4024/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095535 in molecule.smi (4025/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4068149 in molecule.smi (4026/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4092123 in molecule.smi (4027/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064413 in molecule.smi (4028/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099873 in molecule.smi (4029/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4074062 in molecule.smi (4031/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072999 in molecule.smi (4030/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094591 in molecule.smi (4032/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4084371 in molecule.smi (4033/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4102333 in molecule.smi (4034/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066548 in molecule.smi (4035/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094081 in molecule.smi (4036/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081915 in molecule.smi (4037/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4071377 in molecule.smi (4039/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060011 in molecule.smi (4038/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098418 in molecule.smi (4040/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4077952 in molecule.smi (4042/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083306 in molecule.smi (4041/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070182 in molecule.smi (4044/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4076034 in molecule.smi (4043/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091851 in molecule.smi (4045/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075061 in molecule.smi (4046/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066365 in molecule.smi (4047/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093928 in molecule.smi (4048/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4097266 in molecule.smi (4049/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072457 in molecule.smi (4050/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL23838 in molecule.smi (4051/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060832 in molecule.smi (4052/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4089033 in molecule.smi (4053/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4088091 in molecule.smi (4054/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066805 in molecule.smi (4055/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4080216 in molecule.smi (4056/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104459 in molecule.smi (4057/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093532 in molecule.smi (4059/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072825 in molecule.smi (4058/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4090159 in molecule.smi (4061/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069096 in molecule.smi (4060/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4097877 in molecule.smi (4063/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4079853 in molecule.smi (4062/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4071803 in molecule.smi (4065/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098808 in molecule.smi (4064/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4082785 in molecule.smi (4067/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085482 in molecule.smi (4066/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4101886 in molecule.smi (4069/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072478 in molecule.smi (4068/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4068410 in molecule.smi (4070/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4090842 in molecule.smi (4072/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095755 in molecule.smi (4071/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069847 in molecule.smi (4073/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091748 in molecule.smi (4075/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4063984 in molecule.smi (4074/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4072579 in molecule.smi (4076/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4068829 in molecule.smi (4078/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099486 in molecule.smi (4077/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4096126 in molecule.smi (4079/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081509 in molecule.smi (4081/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4059560 in molecule.smi (4080/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4098703 in molecule.smi (4082/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095035 in molecule.smi (4084/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4076580 in molecule.smi (4083/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4073287 in molecule.smi (4085/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099391 in molecule.smi (4086/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4084842 in molecule.smi (4087/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4076986 in molecule.smi (4088/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4063583 in molecule.smi (4089/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4082048 in molecule.smi (4090/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083039 in molecule.smi (4091/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4101018 in molecule.smi (4092/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070471 in molecule.smi (4093/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1340607 in molecule.smi (4094/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4081932 in molecule.smi (4096/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1482486 in molecule.smi (4095/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4071394 in molecule.smi (4098/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099934 in molecule.smi (4097/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1543212 in molecule.smi (4100/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4092186 in molecule.smi (4099/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4095547 in molecule.smi (4102/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1416246 in molecule.smi (4101/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4087841 in molecule.smi (4104/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075136 in molecule.smi (4103/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4079963 in molecule.smi (4106/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1301463 in molecule.smi (4105/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4092142 in molecule.smi (4108/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104206 in molecule.smi (4107/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064806 in molecule.smi (4110/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL179218 in molecule.smi (4109/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4073401 in molecule.smi (4112/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100253 in molecule.smi (4111/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL14577 in molecule.smi (4114/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1311140 in molecule.smi (4113/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4068713 in molecule.smi (4116/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL451635 in molecule.smi (4115/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060058 in molecule.smi (4118/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4088294 in molecule.smi (4117/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104406 in molecule.smi (4120/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4080917 in molecule.smi (4119/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100057 in molecule.smi (4122/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4062826 in molecule.smi (4121/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4100639 in molecule.smi (4124/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4073838 in molecule.smi (4123/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4092905 in molecule.smi (4126/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4065242 in molecule.smi (4125/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4076907 in molecule.smi (4128/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091945 in molecule.smi (4127/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4062487 in molecule.smi (4130/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4084189 in molecule.smi (4129/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4061404 in molecule.smi (4131/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4102122 in molecule.smi (4133/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4089550 in molecule.smi (4132/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064154 in molecule.smi (4135/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4077866 in molecule.smi (4134/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4086639 in molecule.smi (4137/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4085722 in molecule.smi (4136/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094380 in molecule.smi (4139/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104534 in molecule.smi (4138/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091001 in molecule.smi (4141/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066891 in molecule.smi (4140/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4064304 in molecule.smi (4143/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4063109 in molecule.smi (4142/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4080800 in molecule.smi (4144/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4090498 in molecule.smi (4146/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4094837 in molecule.smi (4145/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104952 in molecule.smi (4148/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069472 in molecule.smi (4147/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4097249 in molecule.smi (4150/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4080726 in molecule.smi (4149/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4078265 in molecule.smi (4152/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070057 in molecule.smi (4151/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4067342 in molecule.smi (4154/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4096311 in molecule.smi (4153/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4071110 in molecule.smi (4156/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4088594 in molecule.smi (4155/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070092 in molecule.smi (4158/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091935 in molecule.smi (4157/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4077169 in molecule.smi (4159/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075825 in molecule.smi (4161/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4065259 in molecule.smi (4160/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4088659 in molecule.smi (4163/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075624 in molecule.smi (4162/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4063635 in molecule.smi (4165/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4074993 in molecule.smi (4164/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4083288 in molecule.smi (4166/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4075667 in molecule.smi (4168/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066908 in molecule.smi (4167/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4069776 in molecule.smi (4170/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4091683 in molecule.smi (4169/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4089264 in molecule.smi (4172/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4096963 in molecule.smi (4171/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425895 in molecule.smi (4174/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL887 in molecule.smi (4173/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425894 in molecule.smi (4175/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425892 in molecule.smi (4177/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425893 in molecule.smi (4176/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1303143 in molecule.smi (4179/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425889 in molecule.smi (4178/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425891 in molecule.smi (4181/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425881 in molecule.smi (4180/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425885 in molecule.smi (4183/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL3425890 in molecule.smi (4182/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4061433 in molecule.smi (4185/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1242180 in molecule.smi (4184/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4070093 in molecule.smi (4187/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4097284 in molecule.smi (4186/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4066392 in molecule.smi (4189/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4093946 in molecule.smi (4188/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4104589 in molecule.smi (4191/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4080358 in molecule.smi (4190/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL597863 in molecule.smi (4192/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4096862 in molecule.smi (4193/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4060972 in molecule.smi (4194/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4089155 in molecule.smi (4195/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4099308 in molecule.smi (4196/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4086152 in molecule.smi (4197/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4065910 in molecule.smi (4198/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4093514 in molecule.smi (4199/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4086383 in molecule.smi (4200/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4064920 in molecule.smi (4201/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4078528 in molecule.smi (4202/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4102810 in molecule.smi (4203/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4067639 in molecule.smi (4204/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4095051 in molecule.smi (4205/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4105221 in molecule.smi (4206/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4087364 in molecule.smi (4207/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4072484 in molecule.smi (4208/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4099404 in molecule.smi (4209/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4065374 in molecule.smi (4210/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4086807 in molecule.smi (4211/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL140 in molecule.smi (4212/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL165 in molecule.smi (4213/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4079599 in molecule.smi (4215/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3425908 in molecule.smi (4214/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4071149 in molecule.smi (4216/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4094833 in molecule.smi (4217/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4067388 in molecule.smi (4218/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4102571 in molecule.smi (4219/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4078305 in molecule.smi (4220/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4064686 in molecule.smi (4221/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4086191 in molecule.smi (4222/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4073273 in molecule.smi (4223/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4093947 in molecule.smi (4224/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4059766 in molecule.smi (4225/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4081699 in molecule.smi (4226/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4076207 in molecule.smi (4227/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4080691 in molecule.smi (4228/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4098654 in molecule.smi (4229/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4068935 in molecule.smi (4230/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4100298 in molecule.smi (4231/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4100407 in molecule.smi (4233/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4092562 in molecule.smi (4232/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4073582 in molecule.smi (4234/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4074655 in molecule.smi (4235/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4100935 in molecule.smi (4237/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL511921 in molecule.smi (4236/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4062800 in molecule.smi (4239/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4082959 in molecule.smi (4238/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4085450 in molecule.smi (4241/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4090751 in molecule.smi (4240/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4068888 in molecule.smi (4243/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4063877 in molecule.smi (4242/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4094543 in molecule.smi (4245/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4077157 in molecule.smi (4244/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4065639 in molecule.smi (4247/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4067881 in molecule.smi (4246/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4128985 in molecule.smi (4249/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4125708 in molecule.smi (4248/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4127747 in molecule.smi (4250/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4129147 in molecule.smi (4251/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4128454 in molecule.smi (4252/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4127071 in molecule.smi (4253/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4130217 in molecule.smi (4254/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4125722 in molecule.smi (4255/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4129007 in molecule.smi (4256/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4129420 in molecule.smi (4257/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4128191 in molecule.smi (4259/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4128040 in molecule.smi (4258/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4126674 in molecule.smi (4261/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4129940 in molecule.smi (4260/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4128061 in molecule.smi (4262/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4163705 in molecule.smi (4263/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4164010 in molecule.smi (4264/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4176598 in molecule.smi (4265/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4172587 in molecule.smi (4266/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4164347 in molecule.smi (4267/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4174514 in molecule.smi (4268/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4166300 in molecule.smi (4269/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1187434 in molecule.smi (4271/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4174269 in molecule.smi (4270/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4175159 in molecule.smi (4272/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4160912 in molecule.smi (4274/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4164525 in molecule.smi (4273/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4164315 in molecule.smi (4275/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4174963 in molecule.smi (4276/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4167757 in molecule.smi (4277/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4159837 in molecule.smi (4278/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4164387 in molecule.smi (4279/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4165897 in molecule.smi (4280/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4173815 in molecule.smi (4281/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4170372 in molecule.smi (4283/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4162452 in molecule.smi (4282/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4169326 in molecule.smi (4285/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162043 in molecule.smi (4284/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL4177164 in molecule.smi (4286/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168790 in molecule.smi (4288/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160856 in molecule.smi (4287/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167958 in molecule.smi (4289/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL469638 in molecule.smi (4290/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL586091 in molecule.smi (4291/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1172893 in molecule.smi (4292/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172271 in molecule.smi (4293/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4163781 in molecule.smi (4295/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172720 in molecule.smi (4294/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175914 in molecule.smi (4297/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165299 in molecule.smi (4296/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172510 in molecule.smi (4299/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4161860 in molecule.smi (4298/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168710 in molecule.smi (4301/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164186 in molecule.smi (4300/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164524 in molecule.smi (4303/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160790 in molecule.smi (4302/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168617 in molecule.smi (4305/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173258 in molecule.smi (4304/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL462946 in molecule.smi (4307/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160694 in molecule.smi (4306/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172401 in molecule.smi (4309/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL553245 in molecule.smi (4308/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL1171719 in molecule.smi (4310/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176125 in molecule.smi (4312/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162070 in molecule.smi (4311/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL2314746 in molecule.smi (4314/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165513 in molecule.smi (4313/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168934 in molecule.smi (4315/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL453019 in molecule.smi (4317/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164418 in molecule.smi (4316/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL465678 in molecule.smi (4319/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173190 in molecule.smi (4318/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4163761 in molecule.smi (4321/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4171637 in molecule.smi (4320/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175494 in molecule.smi (4323/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164862 in molecule.smi (4322/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168299 in molecule.smi (4324/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160394 in molecule.smi (4325/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165471 in molecule.smi (4326/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173389 in molecule.smi (4327/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4170793 in molecule.smi (4328/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160151 in molecule.smi (4329/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167398 in molecule.smi (4330/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175274 in molecule.smi (4331/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4163678 in molecule.smi (4332/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175003 in molecule.smi (4333/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167127 in molecule.smi (4334/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159880 in molecule.smi (4335/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4170530 in molecule.smi (4336/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164981 in molecule.smi (4337/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172883 in molecule.smi (4338/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4166464 in molecule.smi (4339/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4174372 in molecule.smi (4340/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4163016 in molecule.smi (4341/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4170940 in molecule.smi (4342/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173313 in molecule.smi (4343/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162617 in molecule.smi (4344/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4169887 in molecule.smi (4345/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159243 in molecule.smi (4346/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173902 in molecule.smi (4347/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165973 in molecule.smi (4348/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165562 in molecule.smi (4349/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176179 in molecule.smi (4350/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162121 in molecule.smi (4351/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172797 in molecule.smi (4352/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172353 in molecule.smi (4353/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168987 in molecule.smi (4355/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173405 in molecule.smi (4354/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160115 in molecule.smi (4357/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4161065 in molecule.smi (4356/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168037 in molecule.smi (4358/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4166513 in molecule.smi (4360/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4177133 in molecule.smi (4359/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4169938 in molecule.smi (4362/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162015 in molecule.smi (4361/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159705 in molecule.smi (4364/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4170331 in molecule.smi (4363/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4163077 in molecule.smi (4366/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173784 in molecule.smi (4365/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165327 in molecule.smi (4368/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173239 in molecule.smi (4367/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160310 in molecule.smi (4370/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4171568 in molecule.smi (4369/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165631 in molecule.smi (4372/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168225 in molecule.smi (4371/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173562 in molecule.smi (4373/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4174452 in molecule.smi (4374/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4166141 in molecule.smi (4376/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160696 in molecule.smi (4375/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4174056 in molecule.smi (4377/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162692 in molecule.smi (4378/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4170614 in molecule.smi (4379/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4172950 in molecule.smi (4380/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162281 in molecule.smi (4381/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175814 in molecule.smi (4382/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4170921 in molecule.smi (4384/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167954 in molecule.smi (4383/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164078 in molecule.smi (4386/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162993 in molecule.smi (4385/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167524 in molecule.smi (4388/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4174729 in molecule.smi (4387/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159640 in molecule.smi (4390/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159609 in molecule.smi (4389/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175175 in molecule.smi (4392/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167973 in molecule.smi (4391/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4177242 in molecule.smi (4394/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164540 in molecule.smi (4393/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3091687 in molecule.smi (4396/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4169394 in molecule.smi (4395/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167058 in molecule.smi (4397/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4174290 in molecule.smi (4398/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159074 in molecule.smi (4399/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4177075 in molecule.smi (4400/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4162999 in molecule.smi (4401/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173704 in molecule.smi (4402/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4161805 in molecule.smi (4403/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4171947 in molecule.smi (4404/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4161343 in molecule.smi (4405/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4171941 in molecule.smi (4406/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176452 in molecule.smi (4407/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168588 in molecule.smi (4408/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168198 in molecule.smi (4409/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160280 in molecule.smi (4410/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164761 in molecule.smi (4411/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4175384 in molecule.smi (4412/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4166257 in molecule.smi (4413/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176868 in molecule.smi (4414/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4165100 in molecule.smi (4415/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4173000 in molecule.smi (4416/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168520 in molecule.smi (4417/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176380 in molecule.smi (4418/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4171284 in molecule.smi (4419/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4163376 in molecule.smi (4420/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4166003 in molecule.smi (4421/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176610 in molecule.smi (4422/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4171520 in molecule.smi (4423/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4160908 in molecule.smi (4424/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4168179 in molecule.smi (4425/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4176034 in molecule.smi (4426/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4159832 in molecule.smi (4427/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4167753 in molecule.smi (4428/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4174958 in molecule.smi (4429/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4164310 in molecule.smi (4430/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4207758 in molecule.smi (4431/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4207230 in molecule.smi (4432/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4212110 in molecule.smi (4433/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4209728 in molecule.smi (4434/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214643 in molecule.smi (4435/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213792 in molecule.smi (4436/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4208866 in molecule.smi (4437/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4218191 in molecule.smi (4438/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213253 in molecule.smi (4439/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205954 in molecule.smi (4440/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217663 in molecule.smi (4441/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210316 in molecule.smi (4442/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205425 in molecule.smi (4443/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214707 in molecule.smi (4444/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4209803 in molecule.smi (4445/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4209518 in molecule.smi (4446/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214430 in molecule.smi (4447/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205144 in molecule.smi (4448/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210041 in molecule.smi (4449/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4204315 in molecule.smi (4450/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4208641 in molecule.smi (4451/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213042 in molecule.smi (4452/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217466 in molecule.smi (4453/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205764 in molecule.smi (4454/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4206041 in molecule.smi (4455/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217755 in molecule.smi (4456/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210401 in molecule.smi (4457/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205520 in molecule.smi (4458/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213874 in molecule.smi (4459/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4208961 in molecule.smi (4460/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4218281 in molecule.smi (4461/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213345 in molecule.smi (4462/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205110 in molecule.smi (4463/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4216807 in molecule.smi (4464/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4218997 in molecule.smi (4465/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4208392 in molecule.smi (4466/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4207876 in molecule.smi (4467/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214951 in molecule.smi (4468/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4212229 in molecule.smi (4469/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217212 in molecule.smi (4470/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214755 in molecule.smi (4471/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4203538 in molecule.smi (4472/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4203653 in molecule.smi (4473/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4216806 in molecule.smi (4474/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL3891025 in molecule.smi (4475/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4202749 in molecule.smi (4476/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4218829 in molecule.smi (4477/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4207101 in molecule.smi (4478/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205651 in molecule.smi (4479/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210517 in molecule.smi (4480/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210020 in molecule.smi (4481/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214932 in molecule.smi (4482/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4212275 in molecule.smi (4483/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4207386 in molecule.smi (4484/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217666 in molecule.smi (4485/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4212713 in molecule.smi (4486/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205426 in molecule.smi (4487/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217139 in molecule.smi (4488/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4208869 in molecule.smi (4489/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4203988 in molecule.smi (4490/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217746 in molecule.smi (4491/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4208322 in molecule.smi (4492/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213147 in molecule.smi (4493/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4209420 in molecule.smi (4494/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4209795 in molecule.smi (4495/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4216066 in molecule.smi (4496/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4203672 in molecule.smi (4497/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4213591 in molecule.smi (4498/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217346 in molecule.smi (4499/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217660 in molecule.smi (4500/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4215154 in molecule.smi (4501/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210729 in molecule.smi (4502/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4215217 in molecule.smi (4503/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4204901 in molecule.smi (4504/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214900 in molecule.smi (4505/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4206484 in molecule.smi (4506/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4202784 in molecule.smi (4507/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4209755 in molecule.smi (4508/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4214668 in molecule.smi (4509/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205374 in molecule.smi (4510/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4210274 in molecule.smi (4511/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4217617 in molecule.smi (4512/4695). Average speed: 0.24 s/mol.\n", + "Processing CHEMBL4205910 in molecule.smi (4513/4695). 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Average speed: 0.24 s/mol.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2p7rAVy_k_hH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "outputId": "fbd4553a-a474-49bd-afa2-625982aaf1a8" + }, + "source": [ + "! ls -l" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "total 34032\n", + "-rw-r--r-- 1 root root 385924 Jun 9 14:28 acetylcholinesterase_03_bioactivity_data_curated.csv\n", + "-rw-r--r-- 1 root root 8356701 Jun 9 14:49 descriptors_output.csv\n", + "drwxr-xr-x 3 root root 4096 Jun 9 14:27 __MACOSX\n", + "-rw-r--r-- 1 root root 310658 Jun 9 14:29 molecule.smi\n", + "drwxrwxr-x 4 root root 4096 May 30 10:10 PaDEL-Descriptor\n", + "-rw-r--r-- 1 root root 231 Jun 9 14:27 padel.sh\n", + "-rw-r--r-- 1 root root 25768637 Jun 9 14:27 padel.zip\n", + "drwxr-xr-x 1 root root 4096 May 29 18:19 sample_data\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gUMlPfFrxicj", + "colab_type": "text" + }, + "source": [ + "## **Preparing the X and Y Data Matrices**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "30aa4WP4ZA8M", + "colab_type": "text" + }, + "source": [ + "### **X data matrix**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3g319qxVl7tY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df3_X = pd.read_csv('descriptors_output.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hBp1PTObFQDd", + "colab_type": "code", + "outputId": "70f5dbde-55dc-446f-90e0-6057dcf0ad20", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 439 + } + }, + "source": [ + "df3_X" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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4695 rows × 881 columns

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" + ], + "text/plain": [ + " PubchemFP0 PubchemFP1 ... PubchemFP879 PubchemFP880\n", + "0 1 1 ... 0 0\n", + "1 1 1 ... 0 0\n", + "2 1 1 ... 0 0\n", + "3 1 1 ... 0 0\n", + "4 1 1 ... 0 0\n", + "... ... ... ... ... ...\n", + "4690 1 1 ... 0 0\n", + "4691 1 1 ... 0 0\n", + "4692 1 1 ... 0 0\n", + "4693 1 1 ... 0 0\n", + "4694 1 1 ... 0 0\n", + "\n", + "[4695 rows x 881 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6ePu814KZNBG", + "colab_type": "text" + }, + "source": [ + "## **Y variable**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9ssfxFCdr7ua", + "colab_type": "text" + }, + "source": [ + "### **Convert IC50 to pIC50**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FlhsSEW5FXTG", + "colab_type": "code", + "outputId": "635522b9-62f9-4546-cfdf-6aa66d4094ad", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + } + }, + "source": [ + "df3_Y = df3['pIC50']\n", + "df3_Y" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 6.124939\n", + "1 7.000000\n", + "2 4.301030\n", + "3 6.522879\n", + "4 6.096910\n", + " ... \n", + "4690 5.612610\n", + "4691 5.595166\n", + "4692 5.419075\n", + "4693 5.460924\n", + "4694 5.555955\n", + "Name: pIC50, Length: 4695, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eET6iZ1Aw3oe", + "colab_type": "text" + }, + "source": [ + "## **Combining X and Y variable**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FRBfBP3QxFJp", + "colab_type": "code", + "outputId": "2a6a3dba-ff09-4cd4-84db-885164ac817e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 439 + } + }, + "source": [ + "dataset3 = pd.concat([df3_X,df3_Y], axis=1)\n", + "dataset3" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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4695 rows × 882 columns

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" + ], + "text/plain": [ + " PubchemFP0 PubchemFP1 PubchemFP2 ... PubchemFP879 PubchemFP880 pIC50\n", + "0 1 1 1 ... 0 0 6.124939\n", + "1 1 1 1 ... 0 0 7.000000\n", + "2 1 1 1 ... 0 0 4.301030\n", + "3 1 1 0 ... 0 0 6.522879\n", + "4 1 1 0 ... 0 0 6.096910\n", + "... ... ... ... ... ... ... ...\n", + "4690 1 1 1 ... 0 0 5.612610\n", + "4691 1 1 1 ... 0 0 5.595166\n", + "4692 1 1 1 ... 0 0 5.419075\n", + "4693 1 1 1 ... 0 0 5.460924\n", + "4694 1 1 1 ... 0 0 5.555955\n", + "\n", + "[4695 rows x 882 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "75npGyvhae0e", + "colab_type": "code", + "colab": {} + }, + "source": [ + "dataset3.to_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_aromatase_pubchem_fp.csv', index=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nFpLoNRHeRa6", + "colab_type": "text" + }, + "source": [ + "# **Let's download the CSV file to your local computer for the Part 3B (Model Building).**" + ] + } + ] +} \ No newline at end of file From 18bd2120d47294a15f33ab7b8a6cced7b5fd1cda Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 10 Jun 2020 00:24:15 +0700 Subject: [PATCH 08/73] Delete CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb --- ...erase_Descriptor_Dataset_Preparation.ipynb | 8786 ----------------- 1 file changed, 8786 deletions(-) delete mode 100644 python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb diff --git a/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb b/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb deleted file mode 100644 index fcb96a1..0000000 --- a/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb +++ /dev/null @@ -1,8786 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "CDD-ML-Part-3-Model-Building-Acetylcholinesterase.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "l0Y7_lgN4jzM", - "colab_type": "text" - }, - "source": [ - "# **Bioinformatics Project - Computational Drug Discovery [Part 3] Descriptor Calculation and Dataset Preparation**\n", - "\n", - "Chanin Nantasenamat\n", - "\n", - "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", - "\n", - "In this Jupyter notebook, we will be building a real-life **data science project** that you can include in your **data science portfolio**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", - "\n", - "In **Part 3**, we will be calculating molecular descriptors that are essentially quantitative description of the compounds in the dataset. Finally, we will be preparing this into a dataset for subsequent model building in Part 4.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "o-4IOizard4P", - "colab_type": "text" - }, - "source": [ - "## **Download PaDEL-Descriptor**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "H0mjQ2PcrSe5", - "colab_type": "code", - "outputId": "f89487f7-e16e-4c1b-9ae9-22a82e3b4213", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 561 - } - }, - "source": [ - "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n", - "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "text": [ - "--2020-06-09 14:27:17-- https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n", - "Resolving github.com (github.com)... 140.82.113.4\n", - "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip [following]\n", - "--2020-06-09 14:27:17-- https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 25768637 (25M) [application/zip]\n", - "Saving to: ‘padel.zip’\n", - "\n", - "padel.zip 100%[===================>] 24.57M 33.2MB/s in 0.7s \n", - "\n", - "2020-06-09 14:27:21 (33.2 MB/s) - ‘padel.zip’ saved [25768637/25768637]\n", - "\n", - "--2020-06-09 14:27:23-- https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh\n", - "Resolving github.com (github.com)... 140.82.113.4\n", - "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh [following]\n", - "--2020-06-09 14:27:24-- https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 231 [text/plain]\n", - "Saving to: ‘padel.sh’\n", - "\n", - "padel.sh 100%[===================>] 231 --.-KB/s in 0s \n", - "\n", - "2020-06-09 14:27:24 (12.5 MB/s) - ‘padel.sh’ saved [231/231]\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "64HnTL4tS-nA", - "colab_type": "code", - "outputId": "e0b9c445-e681-4328-908d-d0338c0da805", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "source": [ - "! unzip padel.zip" - ], - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Archive: padel.zip\n", - " creating: PaDEL-Descriptor/\n", - " inflating: __MACOSX/._PaDEL-Descriptor \n", - " inflating: PaDEL-Descriptor/MACCSFingerprinter.xml \n", - " inflating: __MACOSX/PaDEL-Descriptor/._MACCSFingerprinter.xml \n", - " inflating: PaDEL-Descriptor/AtomPairs2DFingerprinter.xml \n", - " inflating: __MACOSX/PaDEL-Descriptor/._AtomPairs2DFingerprinter.xml \n", - " inflating: PaDEL-Descriptor/EStateFingerprinter.xml \n", - " inflating: __MACOSX/PaDEL-Descriptor/._EStateFingerprinter.xml \n", - " inflating: PaDEL-Descriptor/Fingerprinter.xml \n", - " inflating: __MACOSX/PaDEL-Descriptor/._Fingerprinter.xml \n", - " inflating: PaDEL-Descriptor/.DS_Store \n", - " inflating: __MACOSX/PaDEL-Descriptor/._.DS_Store \n", - " creating: PaDEL-Descriptor/license/\n", - " inflating: __MACOSX/PaDEL-Descriptor/._license \n", - " inflating: PaDEL-Descriptor/KlekotaRothFingerprintCount.xml \n", - 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Here we will be using the **bioactivity_data_3class_pIC50.csv** file that essentially contain the pIC50 values that we will be using for building a regression model." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "JBs7QdJoubWC", - "colab_type": "code", - "outputId": "51ee5be0-78c8-4815-ecf9-2dfc981a6d8e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - } - }, - "source": [ - "! wget https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv" - ], - "execution_count": 24, - "outputs": [ - { - "output_type": "stream", - "text": [ - "--2020-06-09 15:50:01-- https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 655414 (640K) [text/plain]\n", - "Saving to: ‘acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv.1’\n", - "\n", - "\r acetylcho 0%[ ] 0 --.-KB/s \racetylcholinesteras 100%[===================>] 640.05K --.-KB/s in 0.1s \n", - "\n", - "2020-06-09 15:50:02 (6.33 MB/s) - ‘acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv.1’ saved [655414/655414]\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Fpu5C7HlwV9s", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import pandas as pd" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "GCcE8J5XwjtB", - "colab_type": "code", - "colab": {} - }, - "source": [ - "df3 = pd.read_csv('acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "60z_N6egNiSJ", - "colab_type": "code", - "outputId": "b931eadc-81e2-44a9-d63b-6a8a442e1061", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 419 - } - }, - "source": [ - "df3" - ], - "execution_count": 27, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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Unnamed: 0molecule_chembl_idcanonical_smilesclassMWLogPNumHDonorsNumHAcceptorspIC50
00CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1active312.3252.803200.06.06.124939
11CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1active376.9134.554600.05.07.000000
22CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1inactive426.8515.357400.05.04.301030
33CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)Factive404.8454.706900.05.06.522879
44CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)Cactive346.3343.095300.06.06.096910
..............................
46904690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...intermediate511.1507.072302.03.05.612610
46914691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...intermediate511.1507.072302.03.05.595166
46924692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...intermediate555.6017.181402.03.05.419075
46934693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...intermediate521.7026.327102.05.05.460924
46944694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...intermediate501.7156.290582.04.05.555955
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4695 rows × 9 columns

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" - ], - "text/plain": [ - " Unnamed: 0 molecule_chembl_id ... NumHAcceptors pIC50\n", - "0 0 CHEMBL133897 ... 6.0 6.124939\n", - "1 1 CHEMBL336398 ... 5.0 7.000000\n", - "2 2 CHEMBL131588 ... 5.0 4.301030\n", - "3 3 CHEMBL130628 ... 5.0 6.522879\n", - "4 4 CHEMBL130478 ... 6.0 6.096910\n", - "... ... ... ... ... ...\n", - "4690 4690 CHEMBL4293155 ... 3.0 5.612610\n", - "4691 4691 CHEMBL4282558 ... 3.0 5.595166\n", - "4692 4692 CHEMBL4281727 ... 3.0 5.419075\n", - "4693 4693 CHEMBL4292349 ... 5.0 5.460924\n", - "4694 4694 CHEMBL4278260 ... 4.0 5.555955\n", - "\n", - "[4695 rows x 9 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 27 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "BJDJkY43R-9F", - "colab_type": "code", - "colab": {} - }, - "source": [ - "selection = ['canonical_smiles','molecule_chembl_id']\n", - "df3_selection = df3[selection]\n", - "df3_selection.to_csv('molecule.smi', sep='\\t', index=False, header=False)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "nRSCoPVDSkf5", - "colab_type": "code", - "outputId": "c9e9f7ed-3cb1-43c5-ae7b-a23456f408d8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 102 - } - }, - "source": [ - "! cat molecule.smi | head -5" - ], - "execution_count": 29, - "outputs": [ - { - "output_type": "stream", - "text": [ - "CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1\tCHEMBL133897\n", - "O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1\tCHEMBL336398\n", - "CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1\tCHEMBL131588\n", - "O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F\tCHEMBL130628\n", - "CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C\tCHEMBL130478\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "GlYaJ9pzUGjS", - "colab_type": "code", - "outputId": "0c694214-bcfb-4715-8ff1-231f99ea8351", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "! cat molecule.smi | wc -l" - ], - "execution_count": 30, - "outputs": [ - { - "output_type": "stream", - "text": [ - "4695\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YzN_S4Quro5S", - "colab_type": "text" - }, - "source": [ - "## **Calculate fingerprint descriptors**\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JsgTV-ByxdMa", - "colab_type": "text" - }, - "source": [ - "### **Calculate PaDEL descriptors**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "6kN9jrGpS5nE", - "colab_type": "code", - "outputId": "c8edf24d-8126-4c21-e686-898838e47350", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "source": [ - "! bash padel.sh" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Processing CHEMBL133897 in molecule.smi (1/4695). \n", - "Processing CHEMBL336398 in molecule.smi (2/4695). \n", - "Processing CHEMBL131588 in molecule.smi (3/4695). Average speed: 5.49 s/mol.\n", - "Processing CHEMBL130628 in molecule.smi (4/4695). Average speed: 2.77 s/mol.\n", - "Processing CHEMBL130478 in molecule.smi (5/4695). Average speed: 2.18 s/mol.\n", - "Processing CHEMBL130112 in molecule.smi (6/4695). Average speed: 1.70 s/mol.\n", - "Processing CHEMBL130098 in molecule.smi (7/4695). Average speed: 1.45 s/mol.\n", - "Processing CHEMBL337486 in molecule.smi (8/4695). Average speed: 1.27 s/mol.\n", - "Processing CHEMBL336538 in molecule.smi (9/4695). Average speed: 1.13 s/mol.\n", - "Processing CHEMBL131051 in molecule.smi (10/4695). Average speed: 1.04 s/mol.\n", - "Processing CHEMBL341437 in molecule.smi (11/4695). Average speed: 0.94 s/mol.\n", - "Processing CHEMBL122983 in molecule.smi (13/4695). Average speed: 0.92 s/mol.\n", - "Processing CHEMBL335033 in molecule.smi (12/4695). Average speed: 0.91 s/mol.\n", - "Processing CHEMBL339995 in molecule.smi (15/4695). Average speed: 0.78 s/mol.\n", - "Processing CHEMBL338720 in molecule.smi (14/4695). Average speed: 0.81 s/mol.\n", - "Processing CHEMBL131536 in molecule.smi (17/4695). Average speed: 0.76 s/mol.\n", - "Processing CHEMBL335158 in molecule.smi (16/4695). Average speed: 0.76 s/mol.\n", - "Processing CHEMBL106126 in molecule.smi (18/4695). Average speed: 0.70 s/mol.\n", - "Processing CHEMBL334971 in molecule.smi (19/4695). Average speed: 0.66 s/mol.\n", - "Processing CHEMBL336625 in molecule.smi (20/4695). Average speed: 0.64 s/mol.\n", - "Processing CHEMBL130666 in molecule.smi (21/4695). Average speed: 0.63 s/mol.\n", - "Processing CHEMBL134061 in molecule.smi (22/4695). Average speed: 0.61 s/mol.\n", - "Processing CHEMBL133388 in molecule.smi (23/4695). Average speed: 0.59 s/mol.\n", - "Processing CHEMBL130645 in molecule.smi (24/4695). Average speed: 0.57 s/mol.\n", - "Processing CHEMBL133580 in molecule.smi (25/4695). Average speed: 0.55 s/mol.\n", - "Processing CHEMBL336524 in molecule.smi (26/4695). Average speed: 0.54 s/mol.\n", - "Processing CHEMBL336276 in molecule.smi (27/4695). Average speed: 0.52 s/mol.\n", - "Processing CHEMBL334395 in molecule.smi (28/4695). Average speed: 0.51 s/mol.\n", - "Processing CHEMBL131320 in molecule.smi (29/4695). Average speed: 0.50 s/mol.\n", - "Processing CHEMBL337714 in molecule.smi (31/4695). Average speed: 0.47 s/mol.\n", - "Processing CHEMBL339297 in molecule.smi (30/4695). Average speed: 0.48 s/mol.\n", - "Processing CHEMBL122575 in molecule.smi (32/4695). Average speed: 0.47 s/mol.\n", - "Processing CHEMBL130704 in molecule.smi (33/4695). Average speed: 0.45 s/mol.\n", - "Processing CHEMBL46151 in molecule.smi (34/4695). Average speed: 0.45 s/mol.\n", - "Processing CHEMBL54126 in molecule.smi (35/4695). Average speed: 0.44 s/mol.\n", - "Processing CHEMBL297316 in molecule.smi (36/4695). Average speed: 0.43 s/mol.\n", - "Processing CHEMBL65667 in molecule.smi (37/4695). Average speed: 0.42 s/mol.\n", - "Processing CHEMBL431519 in molecule.smi (38/4695). Average speed: 0.41 s/mol.\n", - "Processing CHEMBL296429 in molecule.smi (39/4695). Average speed: 0.41 s/mol.\n", - "Processing CHEMBL154972 in molecule.smi (40/4695). Average speed: 0.40 s/mol.\n", - "Processing CHEMBL152722 in molecule.smi (41/4695). Average speed: 0.40 s/mol.\n", - "Processing CHEMBL349127 in molecule.smi (43/4695). Average speed: 0.39 s/mol.\n", - "Processing CHEMBL544022 in molecule.smi (42/4695). Average speed: 0.39 s/mol.\n", - "Processing CHEMBL156659 in molecule.smi (44/4695). Average speed: 0.38 s/mol.\n", - "Processing CHEMBL45118 in molecule.smi (45/4695). Average speed: 0.37 s/mol.\n", - "Processing CHEMBL542609 in molecule.smi (46/4695). Average speed: 0.37 s/mol.\n", - "Processing CHEMBL155322 in molecule.smi (47/4695). Average speed: 0.36 s/mol.\n", - "Processing CHEMBL1203537 in molecule.smi (49/4695). Average speed: 0.35 s/mol.\n", - "Processing CHEMBL154211 in molecule.smi (48/4695). Average speed: 0.36 s/mol.\n", - "Processing CHEMBL47375 in molecule.smi (51/4695). Average speed: 0.34 s/mol.\n", - "Processing CHEMBL86 in molecule.smi (50/4695). Average speed: 0.35 s/mol.\n", - "Processing CHEMBL542360 in molecule.smi (52/4695). Average speed: 0.34 s/mol.\n", - "Processing CHEMBL154689 in molecule.smi (54/4695). Average speed: 0.33 s/mol.\n", - "Processing CHEMBL345849 in molecule.smi (53/4695). Average speed: 0.33 s/mol.\n", - "Processing CHEMBL346733 in molecule.smi (55/4695). Average speed: 0.33 s/mol.\n", - "Processing CHEMBL416464 in molecule.smi (56/4695). Average speed: 0.32 s/mol.\n", - "Processing CHEMBL1203539 in molecule.smi (57/4695). Average speed: 0.32 s/mol.\n", - "Processing CHEMBL347729 in molecule.smi (58/4695). Average speed: 0.31 s/mol.\n", - "Processing CHEMBL296786 in molecule.smi (59/4695). Average speed: 0.31 s/mol.\n", - "Processing CHEMBL63173 in molecule.smi (60/4695). Average speed: 0.30 s/mol.\n", - "Processing CHEMBL433041 in molecule.smi (61/4695). Average speed: 0.30 s/mol.\n", - "Processing CHEMBL33051 in molecule.smi (63/4695). Average speed: 0.30 s/mol.\n", - "Processing CHEMBL292788 in molecule.smi (62/4695). Average speed: 0.30 s/mol.\n", - "Processing CHEMBL155392 in molecule.smi (64/4695). Average speed: 0.30 s/mol.\n", - "Processing CHEMBL543546 in molecule.smi (66/4695). Average speed: 0.29 s/mol.\n", - "Processing CHEMBL153311 in molecule.smi (65/4695). Average speed: 0.29 s/mol.\n", - "Processing CHEMBL1420 in molecule.smi (67/4695). Average speed: 0.29 s/mol.\n", - "Processing CHEMBL556172 in molecule.smi (68/4695). Average speed: 0.28 s/mol.\n", - "Processing CHEMBL544669 in molecule.smi (70/4695). Average speed: 0.28 s/mol.\n", - "Processing CHEMBL154263 in molecule.smi (69/4695). Average speed: 0.28 s/mol.\n", - "Processing CHEMBL62664 in molecule.smi (71/4695). Average speed: 0.28 s/mol.\n", - "Processing CHEMBL155111 in molecule.smi (72/4695). Average speed: 0.27 s/mol.\n", - "Processing CHEMBL1203534 in molecule.smi (73/4695). Average speed: 0.27 s/mol.\n", - "Processing CHEMBL541044 in molecule.smi (74/4695). Average speed: 0.27 s/mol.\n", - "Processing CHEMBL543779 in molecule.smi (75/4695). Average speed: 0.27 s/mol.\n", - "Processing CHEMBL155495 in molecule.smi (76/4695). Average speed: 0.27 s/mol.\n", - "Processing CHEMBL64011 in molecule.smi (77/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL65585 in molecule.smi (78/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL347539 in molecule.smi (79/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL154433 in molecule.smi (80/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL157208 in molecule.smi (81/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL1790041 in molecule.smi (82/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL65356 in molecule.smi (83/4695). Average speed: 0.26 s/mol.\n", - "Processing CHEMBL154366 in molecule.smi (84/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL153821 in molecule.smi (85/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL416644 in molecule.smi (86/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL94 in molecule.smi (87/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL65018 in molecule.smi (89/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL303574 in molecule.smi (88/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL296277 in molecule.smi (90/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL66309 in molecule.smi (91/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1203535 in molecule.smi (92/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL296560 in molecule.smi (93/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL152260 in molecule.smi (94/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL154950 in molecule.smi (95/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL296669 in molecule.smi (96/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1203538 in molecule.smi (97/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL19060 in molecule.smi (98/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL422977 in molecule.smi (99/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL302179 in molecule.smi (101/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL11805 in molecule.smi (100/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL27673 in molecule.smi (102/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL132377 in molecule.smi (103/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL132133 in molecule.smi (104/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL95 in molecule.smi (105/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL134488 in molecule.smi (106/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL134382 in molecule.smi (107/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL59109 in molecule.smi (108/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL286990 in molecule.smi (109/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL294891 in molecule.smi (110/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1255901 in molecule.smi (111/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL89011 in molecule.smi (112/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL748 in molecule.smi (113/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL50608 in molecule.smi (114/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL29300 in molecule.smi (115/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL27675 in molecule.smi (116/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL435373 in molecule.smi (117/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL60167 in molecule.smi (118/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL89012 in molecule.smi (119/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL553212 in molecule.smi (121/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL314800 in molecule.smi (120/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL156630 in molecule.smi (122/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL315013 in molecule.smi (123/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL155754 in molecule.smi (124/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL87170 in molecule.smi (126/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL314658 in molecule.smi (125/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL87959 in molecule.smi (127/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87295 in molecule.smi (128/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL82810 in molecule.smi (129/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL157182 in molecule.smi (130/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL88049 in molecule.smi (131/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87128 in molecule.smi (132/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL160219 in molecule.smi (133/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL445916 in molecule.smi (134/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL85473 in molecule.smi (135/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL314502 in molecule.smi (136/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87320 in molecule.smi (137/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL348607 in molecule.smi (139/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87280 in molecule.smi (138/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL313809 in molecule.smi (140/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL314398 in molecule.smi (142/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87849 in molecule.smi (141/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL156931 in molecule.smi (144/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87373 in molecule.smi (143/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL83005 in molecule.smi (145/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL315515 in molecule.smi (146/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL441632 in molecule.smi (147/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL441808 in molecule.smi (149/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87937 in molecule.smi (148/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87649 in molecule.smi (150/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL156861 in molecule.smi (152/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL160836 in molecule.smi (151/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL314766 in molecule.smi (153/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL88026 in molecule.smi (154/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL86233 in molecule.smi (155/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL155350 in molecule.smi (156/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL155863 in molecule.smi (157/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87980 in molecule.smi (158/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL87441 in molecule.smi (159/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL158946 in molecule.smi (160/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL609440 in molecule.smi (162/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL87550 in molecule.smi (161/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL350464 in molecule.smi (163/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL313271 in molecule.smi (164/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL423313 in molecule.smi (165/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL309667 in molecule.smi (166/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL80221 in molecule.smi (168/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL419470 in molecule.smi (167/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL80247 in molecule.smi (170/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL431844 in molecule.smi (169/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL77896 in molecule.smi (172/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL418917 in molecule.smi (171/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL369661 in molecule.smi (173/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL74089 in molecule.smi (175/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL502 in molecule.smi (174/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL134057 in molecule.smi (176/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL76640 in molecule.smi (177/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL76241 in molecule.smi (178/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL431784 in molecule.smi (179/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL51934 in molecule.smi (180/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL352500 in molecule.smi (181/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL292314 in molecule.smi (182/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL355740 in molecule.smi (183/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL54727 in molecule.smi (184/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL75919 in molecule.smi (185/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL309351 in molecule.smi (186/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL76135 in molecule.smi (188/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL75743 in molecule.smi (187/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL440497 in molecule.smi (189/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL62084 in molecule.smi (191/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL368196 in molecule.smi (190/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL172118 in molecule.smi (192/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1202875 in molecule.smi (193/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL436367 in molecule.smi (194/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL132365 in molecule.smi (195/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1202865 in molecule.smi (197/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3215535 in molecule.smi (196/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL133520 in molecule.smi (198/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1202884 in molecule.smi (200/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL130373 in molecule.smi (199/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL263198 in molecule.smi (201/4695). 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Average speed: 0.18 s/mol.\n", - "Processing CHEMBL343365 in molecule.smi (885/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL292421 in molecule.smi (884/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL335133 in molecule.smi (886/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL12858 in molecule.smi (887/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL424100 in molecule.smi (888/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL13166 in molecule.smi (889/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL13122 in molecule.smi (890/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL137721 in molecule.smi (891/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL139685 in molecule.smi (892/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL342245 in molecule.smi (893/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL275537 in molecule.smi (894/4695). 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Average speed: 0.18 s/mol.\n", - "Processing CHEMBL426441 in molecule.smi (949/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL186144 in molecule.smi (951/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL361869 in molecule.smi (952/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL362711 in molecule.smi (954/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL189283 in molecule.smi (953/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL185976 in molecule.smi (955/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL188325 in molecule.smi (956/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL187812 in molecule.smi (957/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL191729 in molecule.smi (958/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL364125 in molecule.smi (959/4695). Average speed: 0.18 s/mol.\n", - "Processing CHEMBL610243 in molecule.smi (960/4695). 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Average speed: 0.19 s/mol.\n", - "Processing CHEMBL212377 in molecule.smi (1104/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL379935 in molecule.smi (1105/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL213964 in molecule.smi (1106/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL379563 in molecule.smi (1107/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL214160 in molecule.smi (1108/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL213944 in molecule.smi (1109/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL212771 in molecule.smi (1110/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL211652 in molecule.smi (1111/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL211596 in molecule.smi (1112/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL211943 in molecule.smi (1113/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL211897 in molecule.smi (1114/4695). 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Average speed: 0.19 s/mol.\n", - "Processing CHEMBL219063 in molecule.smi (1148/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL266045 in molecule.smi (1149/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL385269 in molecule.smi (1150/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL374184 in molecule.smi (1151/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL218940 in molecule.smi (1152/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL436302 in molecule.smi (1153/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL219406 in molecule.smi (1154/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL219405 in molecule.smi (1155/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL220294 in molecule.smi (1156/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL219172 in molecule.smi (1158/4695). Average speed: 0.19 s/mol.\n", - "Processing CHEMBL219400 in molecule.smi (1157/4695). 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Average speed: 0.20 s/mol.\n", - "Processing CHEMBL438324 in molecule.smi (1325/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL235761 in molecule.smi (1324/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL235759 in molecule.smi (1326/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL238257 in molecule.smi (1327/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL238256 in molecule.smi (1328/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL238085 in molecule.smi (1329/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL235751 in molecule.smi (1330/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL238067 in molecule.smi (1331/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL391356 in molecule.smi (1332/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL238764 in molecule.smi (1333/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL236173 in molecule.smi (1334/4695). 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Average speed: 0.20 s/mol.\n", - "Processing CHEMBL235014 in molecule.smi (1346/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL440983 in molecule.smi (1347/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL238230 in molecule.smi (1348/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL251077 in molecule.smi (1349/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL402484 in molecule.smi (1350/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL1524 in molecule.smi (1351/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL409610 in molecule.smi (1353/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL251076 in molecule.smi (1352/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL400092 in molecule.smi (1355/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL401114 in molecule.smi (1354/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL252723 in molecule.smi (1357/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL250688 in molecule.smi (1356/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL1523 in molecule.smi (1358/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL479291 in molecule.smi (1359/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL15056 in molecule.smi (1360/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL41 in molecule.smi (1361/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL255231 in molecule.smi (1362/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL255230 in molecule.smi (1363/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL109018 in molecule.smi (1364/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL257540 in molecule.smi (1365/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL403260 in molecule.smi (1366/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL254016 in molecule.smi (1367/4695). 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Average speed: 0.20 s/mol.\n", - "Processing CHEMBL406645 in molecule.smi (1400/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL442813 in molecule.smi (1403/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL481657 in molecule.smi (1402/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL502689 in molecule.smi (1405/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL416 in molecule.smi (1404/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL448060 in molecule.smi (1407/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL51628 in molecule.smi (1406/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL303846 in molecule.smi (1408/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL513279 in molecule.smi (1409/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL481658 in molecule.smi (1410/4695). Average speed: 0.20 s/mol.\n", - "Processing CHEMBL461949 in molecule.smi (1411/4695). 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Average speed: 0.21 s/mol.\n", - "Processing CHEMBL575810 in molecule.smi (1544/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL575619 in molecule.smi (1546/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL576005 in molecule.smi (1545/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL575621 in molecule.smi (1548/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL575620 in molecule.smi (1547/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL582883 in molecule.smi (1550/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL573102 in molecule.smi (1549/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL574474 in molecule.smi (1552/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL575813 in molecule.smi (1551/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL572480 in molecule.smi (1554/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL574487 in molecule.smi (1553/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL572453 in molecule.smi (1556/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL572532 in molecule.smi (1555/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL574847 in molecule.smi (1557/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1794936 in molecule.smi (1559/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL569978 in molecule.smi (1558/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL567532 in molecule.smi (1561/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL558598 in molecule.smi (1560/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL570159 in molecule.smi (1563/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL570158 in molecule.smi (1562/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL583855 in molecule.smi (1565/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL571730 in molecule.smi (1564/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL604478 in molecule.smi (1566/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL595115 in molecule.smi (1567/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL594870 in molecule.smi (1568/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL605303 in molecule.smi (1569/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL606348 in molecule.smi (1570/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL593933 in molecule.smi (1571/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL596236 in molecule.smi (1572/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL595114 in molecule.smi (1573/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL594186 in molecule.smi (1574/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL594187 in molecule.smi (1575/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL596457 in molecule.smi (1577/4695). 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Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1025 in molecule.smi (1588/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL603622 in molecule.smi (1589/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL599644 in molecule.smi (1590/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL597188 in molecule.smi (1591/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL597795 in molecule.smi (1593/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL604096 in molecule.smi (1592/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL598824 in molecule.smi (1595/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL597794 in molecule.smi (1594/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL599058 in molecule.smi (1596/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL603256 in molecule.smi (1597/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL599057 in molecule.smi (1598/4695). 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Average speed: 0.21 s/mol.\n", - "Processing CHEMBL140770 in molecule.smi (1764/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084256 in molecule.smi (1765/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL140999 in molecule.smi (1766/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141001 in molecule.smi (1767/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL138442 in molecule.smi (1768/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141622 in molecule.smi (1769/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL140106 in molecule.smi (1770/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141203 in molecule.smi (1771/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL142014 in molecule.smi (1772/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL140990 in molecule.smi (1773/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL142013 in molecule.smi (1774/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141042 in molecule.smi (1775/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL140328 in molecule.smi (1776/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141810 in molecule.smi (1777/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL342413 in molecule.smi (1778/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141276 in molecule.smi (1779/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL138552 in molecule.smi (1780/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL141515 in molecule.smi (1781/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL129753 in molecule.smi (1783/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083069 in molecule.smi (1782/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL337999 in molecule.smi (1785/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083070 in molecule.smi (1784/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128863 in molecule.smi (1787/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083379 in molecule.smi (1786/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL336297 in molecule.smi (1788/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128260 in molecule.smi (1790/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL130847 in molecule.smi (1789/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL131827 in molecule.smi (1791/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084090 in molecule.smi (1793/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL130918 in molecule.smi (1792/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL340625 in molecule.smi (1794/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084883 in molecule.smi (1796/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128551 in molecule.smi (1795/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084091 in molecule.smi (1798/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128618 in molecule.smi (1797/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128471 in molecule.smi (1800/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085129 in molecule.smi (1799/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128043 in molecule.smi (1801/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084366 in molecule.smi (1802/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128202 in molecule.smi (1803/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL339039 in molecule.smi (1804/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082419 in molecule.smi (1805/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL131219 in molecule.smi (1806/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL128452 in molecule.smi (1807/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083976 in molecule.smi (1808/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL131579 in molecule.smi (1809/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL131411 in molecule.smi (1810/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083977 in molecule.smi (1811/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL151417 in molecule.smi (1812/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL281588 in molecule.smi (1813/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL278819 in molecule.smi (1814/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085019 in molecule.smi (1815/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL150985 in molecule.smi (1816/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085020 in molecule.smi (1817/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085021 in molecule.smi (1818/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083676 in molecule.smi (1819/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL150365 in molecule.smi (1820/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082740 in molecule.smi (1821/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082741 in molecule.smi (1822/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL356224 in molecule.smi (1823/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085937 in molecule.smi (1824/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085274 in molecule.smi (1825/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085275 in molecule.smi (1826/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084255 in molecule.smi (1827/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL25264 in molecule.smi (1828/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083054 in molecule.smi (1829/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1086185 in molecule.smi (1830/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083055 in molecule.smi (1831/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084257 in molecule.smi (1832/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083057 in molecule.smi (1833/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083058 in molecule.smi (1834/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083367 in molecule.smi (1835/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL25412 in molecule.smi (1836/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084542 in molecule.smi (1837/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084543 in molecule.smi (1838/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084786 in molecule.smi (1839/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084788 in molecule.smi (1841/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084787 in molecule.smi (1840/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084789 in molecule.smi (1842/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082434 in molecule.smi (1844/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082433 in molecule.smi (1843/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084275 in molecule.smi (1846/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082435 in molecule.smi (1845/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084790 in molecule.smi (1847/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL100334 in molecule.smi (1849/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082436 in molecule.smi (1848/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL102142 in molecule.smi (1850/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL431058 in molecule.smi (1851/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL430679 in molecule.smi (1853/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL101518 in molecule.smi (1852/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL101940 in molecule.smi (1854/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL100510 in molecule.smi (1856/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL101809 in molecule.smi (1855/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085031 in molecule.smi (1858/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085030 in molecule.smi (1857/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL101155 in molecule.smi (1859/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL317541 in molecule.smi (1860/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL420827 in molecule.smi (1862/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL101988 in molecule.smi (1861/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL329518 in molecule.smi (1863/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL101028 in molecule.smi (1864/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085032 in molecule.smi (1865/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL99605 in molecule.smi (1866/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL329733 in molecule.smi (1867/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL98497 in molecule.smi (1868/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083789 in molecule.smi (1869/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083790 in molecule.smi (1871/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL258928 in molecule.smi (1870/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084093 in molecule.smi (1873/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084092 in molecule.smi (1872/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084094 in molecule.smi (1875/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083791 in molecule.smi (1874/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084095 in molecule.smi (1876/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084367 in molecule.smi (1877/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084368 in molecule.smi (1878/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084369 in molecule.smi (1879/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084096 in molecule.smi (1880/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084530 in molecule.smi (1881/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084531 in molecule.smi (1882/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084774 in molecule.smi (1883/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084775 in molecule.smi (1884/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082738 in molecule.smi (1885/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1082739 in molecule.smi (1886/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL118752 in molecule.smi (1887/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL330883 in molecule.smi (1888/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL118968 in molecule.smi (1889/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL331637 in molecule.smi (1890/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL118033 in molecule.smi (1891/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084952 in molecule.smi (1892/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL332454 in molecule.smi (1893/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL119634 in molecule.smi (1894/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL118139 in molecule.smi (1895/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL117826 in molecule.smi (1896/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL115697 in molecule.smi (1897/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL325203 in molecule.smi (1898/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL115580 in molecule.smi (1899/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL119528 in molecule.smi (1900/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL119390 in molecule.smi (1901/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL119389 in molecule.smi (1902/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085276 in molecule.smi (1903/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083677 in molecule.smi (1904/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083678 in molecule.smi (1905/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083982 in molecule.smi (1906/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083056 in molecule.smi (1907/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085277 in molecule.smi (1908/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085278 in molecule.smi (1909/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085526 in molecule.smi (1910/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085527 in molecule.smi (1911/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1086245 in molecule.smi (1912/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1086246 in molecule.smi (1913/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1086247 in molecule.smi (1914/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1086248 in molecule.smi (1915/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084276 in molecule.smi (1916/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL94054 in molecule.smi (1917/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL90241 in molecule.smi (1918/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL91240 in molecule.smi (1919/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL90264 in molecule.smi (1920/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL91432 in molecule.smi (1921/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL91774 in molecule.smi (1922/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL421501 in molecule.smi (1923/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL76014 in molecule.smi (1924/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL74827 in molecule.smi (1925/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL73800 in molecule.smi (1926/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL74940 in molecule.smi (1928/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL75274 in molecule.smi (1927/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL312761 in molecule.smi (1930/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL74927 in molecule.smi (1929/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL307004 in molecule.smi (1932/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL73160 in molecule.smi (1931/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL75305 in molecule.smi (1934/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL312310 in molecule.smi (1933/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL75985 in molecule.smi (1936/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL76173 in molecule.smi (1935/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL307908 in molecule.smi (1938/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083488 in molecule.smi (1937/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084155 in molecule.smi (1940/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL306492 in molecule.smi (1939/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084777 in molecule.smi (1942/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084776 in molecule.smi (1941/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085018 in molecule.smi (1944/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085017 in molecule.smi (1943/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085932 in molecule.smi (1946/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083374 in molecule.smi (1945/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085938 in molecule.smi (1948/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085933 in molecule.smi (1947/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085939 in molecule.smi (1949/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL224158 in molecule.smi (1951/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1085940 in molecule.smi (1950/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083983 in molecule.smi (1953/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL224335 in molecule.smi (1952/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL389192 in molecule.smi (1955/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1083984 in molecule.smi (1954/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084263 in molecule.smi (1957/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL1084262 in molecule.smi (1956/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL388551 in molecule.smi (1959/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL224336 in molecule.smi (1958/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL223170 in molecule.smi (1960/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL224202 in molecule.smi (1961/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL224998 in molecule.smi (1963/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL388575 in molecule.smi (1962/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL223714 in molecule.smi (1965/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL388627 in molecule.smi (1964/4695). Average speed: 0.21 s/mol.\n", - "Processing CHEMBL426015 in molecule.smi (1967/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL427082 in molecule.smi (1966/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1086007 in molecule.smi (1969/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL388615 in molecule.smi (1968/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084545 in molecule.smi (1971/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084544 in molecule.smi (1970/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL427342 in molecule.smi (1972/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL325109 in molecule.smi (1974/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL185896 in molecule.smi (1973/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL189870 in molecule.smi (1976/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL362579 in molecule.smi (1975/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL360186 in molecule.smi (1977/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL188019 in molecule.smi (1979/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL187030 in molecule.smi (1978/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1086358 in molecule.smi (1980/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL189764 in molecule.smi (1982/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL186896 in molecule.smi (1981/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL189875 in molecule.smi (1984/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1085465 in molecule.smi (1983/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL186349 in molecule.smi (1986/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL187017 in molecule.smi (1985/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL186400 in molecule.smi (1988/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL187370 in molecule.smi (1987/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL328085 in molecule.smi (1989/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1085702 in molecule.smi (1990/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1085703 in molecule.smi (1991/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL3215602 in molecule.smi (1992/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL3215816 in molecule.smi (1993/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL3216469 in molecule.smi (1994/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL310555 in molecule.smi (1995/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1171854 in molecule.smi (1996/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1172830 in molecule.smi (1997/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1173543 in molecule.smi (1998/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1173603 in molecule.smi (1999/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1173604 in molecule.smi (2000/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1173605 in molecule.smi (2001/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1172913 in molecule.smi (2002/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1173624 in molecule.smi (2003/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1173090 in molecule.smi (2004/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1213295 in molecule.smi (2005/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243360 in molecule.smi (2006/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243329 in molecule.smi (2007/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243359 in molecule.smi (2008/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1240668 in molecule.smi (2009/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243297 in molecule.smi (2010/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243269 in molecule.smi (2011/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243268 in molecule.smi (2012/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243388 in molecule.smi (2014/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243234 in molecule.smi (2013/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1243170 in molecule.smi (2015/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1241457 in molecule.smi (2017/4695). 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Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257887 in molecule.smi (2028/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257886 in molecule.smi (2029/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257885 in molecule.smi (2030/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257767 in molecule.smi (2031/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257766 in molecule.smi (2032/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257644 in molecule.smi (2033/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257413 in molecule.smi (2034/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257525 in molecule.smi (2035/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1257412 in molecule.smi (2036/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1256415 in molecule.smi (2037/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1276594 in molecule.smi (2038/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1276534 in molecule.smi (2039/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1276567 in molecule.smi (2040/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1275924 in molecule.smi (2041/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289045 in molecule.smi (2042/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289159 in molecule.smi (2043/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289160 in molecule.smi (2044/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289273 in molecule.smi (2045/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289274 in molecule.smi (2046/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289391 in molecule.smi (2047/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289392 in molecule.smi (2048/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289507 in molecule.smi (2050/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289506 in molecule.smi (2049/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288821 in molecule.smi (2051/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289615 in molecule.smi (2052/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289719 in molecule.smi (2053/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289825 in molecule.smi (2054/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288822 in molecule.smi (2055/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1289942 in molecule.smi (2056/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1290043 in molecule.smi (2057/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288823 in molecule.smi (2058/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288824 in molecule.smi (2059/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1290152 in molecule.smi (2060/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288825 in molecule.smi (2061/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288826 in molecule.smi (2062/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288827 in molecule.smi (2063/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288828 in molecule.smi (2064/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1288829 in molecule.smi (2065/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642977 in molecule.smi (2066/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642979 in molecule.smi (2068/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL594629 in molecule.smi (2067/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642976 in molecule.smi (2069/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642980 in molecule.smi (2071/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642988 in molecule.smi (2070/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642978 in molecule.smi (2073/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642975 in molecule.smi (2072/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642987 in molecule.smi (2074/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642986 in molecule.smi (2075/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642984 in molecule.smi (2076/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642973 in molecule.smi (2078/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642983 in molecule.smi (2077/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642981 in molecule.smi (2080/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642985 in molecule.smi (2079/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642972 in molecule.smi (2081/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642982 in molecule.smi (2083/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642971 in molecule.smi (2082/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642974 in molecule.smi (2085/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1642970 in molecule.smi (2084/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL291233 in molecule.smi (2086/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL426113 in molecule.smi (2088/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1200970 in molecule.smi (2087/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL451825 in molecule.smi (2089/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL397871 in molecule.smi (2090/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL238977 in molecule.smi (2091/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1644323 in molecule.smi (2092/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1644325 in molecule.smi (2093/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1644328 in molecule.smi (2094/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651042 in molecule.smi (2095/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651043 in molecule.smi (2096/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651044 in molecule.smi (2097/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651045 in molecule.smi (2098/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651046 in molecule.smi (2099/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651040 in molecule.smi (2101/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651041 in molecule.smi (2100/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651047 in molecule.smi (2102/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651048 in molecule.smi (2103/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651038 in molecule.smi (2104/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1196495 in molecule.smi (2106/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651125 in molecule.smi (2105/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651126 in molecule.smi (2107/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651128 in molecule.smi (2109/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651127 in molecule.smi (2108/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651130 in molecule.smi (2111/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651129 in molecule.smi (2110/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651132 in molecule.smi (2113/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651131 in molecule.smi (2112/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651134 in molecule.smi (2115/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1651133 in molecule.smi (2114/4695). 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Average speed: 0.22 s/mol.\n", - "Processing CHEMBL105017 in molecule.smi (2160/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL225577 in molecule.smi (2161/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL104387 in molecule.smi (2163/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL105257 in molecule.smi (2162/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1761996 in molecule.smi (2164/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1761997 in molecule.smi (2165/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1761998 in molecule.smi (2166/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1761999 in molecule.smi (2167/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1762000 in molecule.smi (2168/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1762001 in molecule.smi (2169/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1762003 in molecule.smi (2170/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1555 in molecule.smi (2171/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766151 in molecule.smi (2172/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084213 in molecule.smi (2173/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084212 in molecule.smi (2174/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084211 in molecule.smi (2175/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084210 in molecule.smi (2176/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766150 in molecule.smi (2177/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1082981 in molecule.smi (2178/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1082979 in molecule.smi (2180/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1082980 in molecule.smi (2179/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1082978 in molecule.smi (2182/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1085505 in molecule.smi (2181/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766148 in molecule.smi (2184/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766149 in molecule.smi (2183/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766146 in molecule.smi (2186/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766147 in molecule.smi (2185/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766145 in molecule.smi (2187/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766144 in molecule.smi (2188/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766143 in molecule.smi (2189/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766142 in molecule.smi (2190/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766141 in molecule.smi (2191/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766140 in molecule.smi (2192/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766139 in molecule.smi (2193/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766020 in molecule.smi (2194/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766019 in molecule.smi (2195/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766018 in molecule.smi (2196/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766017 in molecule.smi (2197/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1082977 in molecule.smi (2199/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1766016 in molecule.smi (2198/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1086014 in molecule.smi (2201/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1084808 in molecule.smi (2200/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1085780 in molecule.smi (2203/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1085781 in molecule.smi (2202/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770549 in molecule.smi (2205/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770548 in molecule.smi (2204/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770551 in molecule.smi (2207/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770550 in molecule.smi (2206/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770552 in molecule.smi (2208/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770553 in molecule.smi (2209/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770554 in molecule.smi (2210/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1770555 in molecule.smi (2211/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL188821 in molecule.smi (2212/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL187404 in molecule.smi (2213/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1771549 in molecule.smi (2214/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1771556 in molecule.smi (2215/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773478 in molecule.smi (2216/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773479 in molecule.smi (2217/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773480 in molecule.smi (2218/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773481 in molecule.smi (2219/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773482 in molecule.smi (2220/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773483 in molecule.smi (2221/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773484 in molecule.smi (2222/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773485 in molecule.smi (2223/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773486 in molecule.smi (2224/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773487 in molecule.smi (2225/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773488 in molecule.smi (2226/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773489 in molecule.smi (2227/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773490 in molecule.smi (2228/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773491 in molecule.smi (2229/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1773492 in molecule.smi (2230/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1080386 in molecule.smi (2231/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1782707 in molecule.smi (2232/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1206 in molecule.smi (2233/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1782730 in molecule.smi (2234/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1784957 in molecule.smi (2235/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1784956 in molecule.smi (2236/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1784955 in molecule.smi (2237/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1784958 in molecule.smi (2238/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783529 in molecule.smi (2239/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783530 in molecule.smi (2240/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783531 in molecule.smi (2241/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783532 in molecule.smi (2242/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783533 in molecule.smi (2244/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1782071 in molecule.smi (2243/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783534 in molecule.smi (2245/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783535 in molecule.smi (2246/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783520 in molecule.smi (2247/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783521 in molecule.smi (2248/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783522 in molecule.smi (2249/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783523 in molecule.smi (2250/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783524 in molecule.smi (2251/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783525 in molecule.smi (2252/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783526 in molecule.smi (2253/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783527 in molecule.smi (2254/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783528 in molecule.smi (2255/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783514 in molecule.smi (2256/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783515 in molecule.smi (2257/4695). Average speed: 0.22 s/mol.\n", - "Processing CHEMBL1783516 in molecule.smi (2258/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1783517 in molecule.smi (2259/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1783518 in molecule.smi (2260/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1783519 in molecule.smi (2261/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1783499 in molecule.smi (2262/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL589390 in molecule.smi (2263/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1814729 in molecule.smi (2264/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1814730 in molecule.smi (2265/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819169 in molecule.smi (2267/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1814731 in molecule.smi (2266/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819171 in molecule.smi (2269/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819170 in molecule.smi (2268/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819172 in molecule.smi (2270/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819173 in molecule.smi (2271/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819174 in molecule.smi (2272/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819175 in molecule.smi (2273/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819176 in molecule.smi (2274/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819177 in molecule.smi (2275/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819178 in molecule.smi (2276/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819179 in molecule.smi (2277/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1819180 in molecule.smi (2278/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834076 in molecule.smi (2279/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834060 in molecule.smi (2280/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834061 in molecule.smi (2281/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834062 in molecule.smi (2282/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834063 in molecule.smi (2283/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834064 in molecule.smi (2284/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834065 in molecule.smi (2285/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834066 in molecule.smi (2286/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834067 in molecule.smi (2287/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834068 in molecule.smi (2288/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834069 in molecule.smi (2289/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834070 in molecule.smi (2290/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834071 in molecule.smi (2291/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834072 in molecule.smi (2292/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834073 in molecule.smi (2293/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834074 in molecule.smi (2294/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834075 in molecule.smi (2295/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834077 in molecule.smi (2296/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834078 in molecule.smi (2297/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834079 in molecule.smi (2298/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834806 in molecule.smi (2299/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834808 in molecule.smi (2300/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834807 in molecule.smi (2301/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834809 in molecule.smi (2302/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834877 in molecule.smi (2303/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834810 in molecule.smi (2304/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1834811 in molecule.smi (2305/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL221753 in molecule.smi (2306/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL31184 in molecule.smi (2308/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL12089 in molecule.smi (2307/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL990 in molecule.smi (2309/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL195437 in molecule.smi (2311/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL40583 in molecule.smi (2310/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL305660 in molecule.smi (2312/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL64894 in molecule.smi (2313/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL808 in molecule.smi (2314/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL496 in molecule.smi (2315/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1200623 in molecule.smi (2316/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL672 in molecule.smi (2317/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL7002 in molecule.smi (2318/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL12131 in molecule.smi (2319/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL104 in molecule.smi (2320/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL411 in molecule.smi (2321/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL334255 in molecule.smi (2322/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3183075 in molecule.smi (2323/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1262 in molecule.smi (2324/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL7568 in molecule.smi (2325/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL91 in molecule.smi (2326/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL19215 in molecule.smi (2327/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL481 in molecule.smi (2328/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1306 in molecule.smi (2329/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL81 in molecule.smi (2330/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912062 in molecule.smi (2331/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912061 in molecule.smi (2332/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912060 in molecule.smi (2333/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912059 in molecule.smi (2334/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912058 in molecule.smi (2335/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912063 in molecule.smi (2336/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1912064 in molecule.smi (2337/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1917825 in molecule.smi (2338/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1917826 in molecule.smi (2339/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1618217 in molecule.smi (2340/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1618106 in molecule.smi (2341/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1914495 in molecule.smi (2342/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915758 in molecule.smi (2343/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915759 in molecule.smi (2344/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1086927 in molecule.smi (2345/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1087180 in molecule.smi (2346/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1087131 in molecule.smi (2348/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL465514 in molecule.smi (2347/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915760 in molecule.smi (2350/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL240972 in molecule.smi (2349/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915762 in molecule.smi (2352/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915761 in molecule.smi (2351/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915763 in molecule.smi (2354/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1098479 in molecule.smi (2353/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915764 in molecule.smi (2355/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL465487 in molecule.smi (2356/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL512830 in molecule.smi (2358/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL460969 in molecule.smi (2357/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915765 in molecule.smi (2359/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1915766 in molecule.smi (2360/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1916475 in molecule.smi (2361/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1916766 in molecule.smi (2362/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1916768 in molecule.smi (2363/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1916770 in molecule.smi (2364/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1916927 in molecule.smi (2365/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1916929 in molecule.smi (2366/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1922540 in molecule.smi (2367/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1922541 in molecule.smi (2368/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL180483 in molecule.smi (2369/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1929421 in molecule.smi (2370/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940736 in molecule.smi (2371/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940737 in molecule.smi (2372/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940738 in molecule.smi (2373/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940612 in molecule.smi (2374/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940613 in molecule.smi (2375/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940614 in molecule.smi (2376/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940615 in molecule.smi (2377/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940616 in molecule.smi (2378/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940617 in molecule.smi (2379/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940618 in molecule.smi (2380/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1940619 in molecule.smi (2381/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952057 in molecule.smi (2382/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952058 in molecule.smi (2383/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952059 in molecule.smi (2384/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952060 in molecule.smi (2385/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952061 in molecule.smi (2386/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952062 in molecule.smi (2387/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952063 in molecule.smi (2388/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952064 in molecule.smi (2389/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952065 in molecule.smi (2390/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952066 in molecule.smi (2391/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952067 in molecule.smi (2392/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952047 in molecule.smi (2393/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952048 in molecule.smi (2394/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952049 in molecule.smi (2395/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952050 in molecule.smi (2396/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952051 in molecule.smi (2397/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952052 in molecule.smi (2398/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952053 in molecule.smi (2399/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952054 in molecule.smi (2400/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952055 in molecule.smi (2401/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952056 in molecule.smi (2402/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011402 in molecule.smi (2403/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL459258 in molecule.smi (2404/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011403 in molecule.smi (2405/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011404 in molecule.smi (2406/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL253152 in molecule.smi (2407/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL374130 in molecule.smi (2409/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011405 in molecule.smi (2408/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011406 in molecule.smi (2411/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL223256 in molecule.smi (2410/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL399121 in molecule.smi (2412/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL8996 in molecule.smi (2414/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL9352 in molecule.smi (2413/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3215882 in molecule.smi (2416/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011483 in molecule.smi (2415/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3217208 in molecule.smi (2418/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216777 in molecule.smi (2417/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011488 in molecule.smi (2420/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011487 in molecule.smi (2419/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216554 in molecule.smi (2422/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216116 in molecule.smi (2421/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216555 in molecule.smi (2424/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011491 in molecule.smi (2423/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216556 in molecule.smi (2425/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216557 in molecule.smi (2426/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011495 in molecule.smi (2427/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011496 in molecule.smi (2428/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216778 in molecule.smi (2429/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3216779 in molecule.smi (2430/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1994202 in molecule.smi (2431/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2011499 in molecule.smi (2432/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL340807 in molecule.smi (2433/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2018160 in molecule.smi (2434/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2018161 in molecule.smi (2435/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL376478 in molecule.smi (2436/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2018162 in molecule.smi (2437/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019052 in molecule.smi (2439/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019053 in molecule.smi (2438/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019048 in molecule.smi (2441/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019049 in molecule.smi (2440/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019046 in molecule.smi (2443/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019047 in molecule.smi (2442/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019043 in molecule.smi (2445/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019044 in molecule.smi (2444/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019040 in molecule.smi (2447/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019041 in molecule.smi (2446/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019038 in molecule.smi (2448/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019037 in molecule.smi (2449/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019035 in molecule.smi (2450/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019034 in molecule.smi (2451/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019032 in molecule.smi (2452/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2019030 in molecule.smi (2453/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2022933 in molecule.smi (2454/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047529 in molecule.smi (2456/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047528 in molecule.smi (2455/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047530 in molecule.smi (2457/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047531 in molecule.smi (2458/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047231 in molecule.smi (2460/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047375 in molecule.smi (2459/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047229 in molecule.smi (2462/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047230 in molecule.smi (2461/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047228 in molecule.smi (2463/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047227 in molecule.smi (2464/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047226 in molecule.smi (2465/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047225 in molecule.smi (2466/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047224 in molecule.smi (2467/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2047223 in molecule.smi (2468/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064471 in molecule.smi (2469/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064470 in molecule.smi (2470/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064469 in molecule.smi (2471/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064466 in molecule.smi (2473/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064468 in molecule.smi (2472/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064464 in molecule.smi (2475/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064465 in molecule.smi (2474/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2071424 in molecule.smi (2477/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2064404 in molecule.smi (2476/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2071426 in molecule.smi (2479/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2071425 in molecule.smi (2478/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2071427 in molecule.smi (2480/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL463686 in molecule.smi (2481/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2071428 in molecule.smi (2482/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL1952289 in molecule.smi (2483/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088687 in molecule.smi (2484/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088780 in molecule.smi (2485/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088781 in molecule.smi (2486/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088782 in molecule.smi (2487/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088783 in molecule.smi (2488/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088784 in molecule.smi (2489/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088785 in molecule.smi (2490/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2088786 in molecule.smi (2491/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2147460 in molecule.smi (2492/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2147462 in molecule.smi (2493/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2147461 in molecule.smi (2494/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160227 in molecule.smi (2495/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160226 in molecule.smi (2496/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160223 in molecule.smi (2497/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160225 in molecule.smi (2498/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160221 in molecule.smi (2499/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160220 in molecule.smi (2500/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160224 in molecule.smi (2501/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160222 in molecule.smi (2502/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160217 in molecule.smi (2503/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160228 in molecule.smi (2504/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2160213 in molecule.smi (2505/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2163795 in molecule.smi (2507/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL258893 in molecule.smi (2506/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181440 in molecule.smi (2508/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181438 in molecule.smi (2509/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181435 in molecule.smi (2511/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181437 in molecule.smi (2510/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181433 in molecule.smi (2512/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181432 in molecule.smi (2513/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181430 in molecule.smi (2515/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2181431 in molecule.smi (2514/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206900 in molecule.smi (2516/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206899 in molecule.smi (2517/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206897 in molecule.smi (2519/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206898 in molecule.smi (2518/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206896 in molecule.smi (2520/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206895 in molecule.smi (2521/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206894 in molecule.smi (2522/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206893 in molecule.smi (2523/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206892 in molecule.smi (2524/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2206891 in molecule.smi (2525/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238007 in molecule.smi (2526/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238006 in molecule.smi (2527/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238005 in molecule.smi (2528/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238004 in molecule.smi (2529/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238003 in molecule.smi (2530/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238002 in molecule.smi (2531/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238001 in molecule.smi (2532/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2238000 in molecule.smi (2533/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237999 in molecule.smi (2534/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237998 in molecule.smi (2535/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237996 in molecule.smi (2537/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237997 in molecule.smi (2536/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237994 in molecule.smi (2539/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237995 in molecule.smi (2538/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237992 in molecule.smi (2541/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237993 in molecule.smi (2540/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2237991 in molecule.smi (2542/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL400257 in molecule.smi (2543/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL607537 in molecule.smi (2544/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL256497 in molecule.smi (2545/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL21396 in molecule.smi (2546/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL128392 in molecule.smi (2547/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL128780 in molecule.smi (2548/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL129577 in molecule.smi (2549/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL128390 in molecule.smi (2550/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2236393 in molecule.smi (2552/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2236394 in molecule.smi (2551/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234536 in molecule.smi (2554/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234537 in molecule.smi (2553/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234534 in molecule.smi (2556/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234535 in molecule.smi (2555/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234532 in molecule.smi (2558/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234533 in molecule.smi (2557/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234531 in molecule.smi (2559/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234530 in molecule.smi (2560/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234529 in molecule.smi (2561/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234528 in molecule.smi (2562/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234527 in molecule.smi (2563/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234526 in molecule.smi (2564/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234525 in molecule.smi (2565/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234524 in molecule.smi (2566/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234523 in molecule.smi (2567/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234522 in molecule.smi (2568/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234521 in molecule.smi (2569/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234520 in molecule.smi (2570/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234519 in molecule.smi (2571/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234518 in molecule.smi (2572/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234517 in molecule.smi (2573/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234516 in molecule.smi (2574/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234515 in molecule.smi (2575/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234513 in molecule.smi (2577/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234514 in molecule.smi (2576/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234511 in molecule.smi (2579/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234512 in molecule.smi (2578/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234844 in molecule.smi (2581/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234845 in molecule.smi (2580/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234842 in molecule.smi (2583/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234843 in molecule.smi (2582/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234840 in molecule.smi (2585/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234841 in molecule.smi (2584/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234838 in molecule.smi (2587/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234839 in molecule.smi (2586/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234836 in molecule.smi (2589/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234837 in molecule.smi (2588/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234834 in molecule.smi (2591/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234835 in molecule.smi (2590/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234833 in molecule.smi (2592/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234831 in molecule.smi (2594/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234832 in molecule.smi (2593/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234829 in molecule.smi (2596/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234830 in molecule.smi (2595/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234546 in molecule.smi (2598/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234547 in molecule.smi (2597/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234545 in molecule.smi (2599/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252378 in molecule.smi (2601/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234544 in molecule.smi (2600/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL253603 in molecule.smi (2602/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL398250 in molecule.smi (2603/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL253602 in molecule.smi (2604/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL398249 in molecule.smi (2605/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252781 in molecule.smi (2606/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234543 in molecule.smi (2607/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234542 in molecule.smi (2608/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234541 in molecule.smi (2609/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234540 in molecule.smi (2610/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252578 in molecule.smi (2611/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252580 in molecule.smi (2612/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234539 in molecule.smi (2613/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2234538 in molecule.smi (2614/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL400675 in molecule.smi (2615/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252579 in molecule.smi (2616/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252380 in molecule.smi (2617/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL252379 in molecule.smi (2618/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL399040 in molecule.smi (2619/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL404407 in molecule.smi (2620/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL253783 in molecule.smi (2621/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL253782 in molecule.smi (2622/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL253386 in molecule.smi (2623/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2297278 in molecule.smi (2625/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2297277 in molecule.smi (2624/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL31574 in molecule.smi (2627/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL28 in molecule.smi (2626/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL309490 in molecule.smi (2629/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL164 in molecule.smi (2628/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL226335 in molecule.smi (2631/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2297279 in molecule.smi (2630/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2297280 in molecule.smi (2633/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL594229 in molecule.smi (2632/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL50 in molecule.smi (2634/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2314726 in molecule.smi (2635/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2323356 in molecule.smi (2636/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2323355 in molecule.smi (2637/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2323354 in molecule.smi (2638/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2323353 in molecule.smi (2639/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2323352 in molecule.smi (2640/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2323351 in molecule.smi (2641/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332981 in molecule.smi (2642/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332540 in molecule.smi (2643/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332541 in molecule.smi (2644/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332530 in molecule.smi (2645/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332531 in molecule.smi (2646/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332532 in molecule.smi (2647/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332533 in molecule.smi (2648/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332542 in molecule.smi (2650/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332534 in molecule.smi (2649/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2332543 in molecule.smi (2651/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332544 in molecule.smi (2652/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332545 in molecule.smi (2653/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332980 in molecule.smi (2654/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332979 in molecule.smi (2655/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332978 in molecule.smi (2656/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332977 in molecule.smi (2657/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332976 in molecule.smi (2658/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332975 in molecule.smi (2659/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332974 in molecule.smi (2660/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332550 in molecule.smi (2661/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332549 in molecule.smi (2662/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332548 in molecule.smi (2663/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332547 in molecule.smi (2664/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL152675 in molecule.smi (2666/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL350032 in molecule.smi (2665/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332546 in molecule.smi (2668/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL454594 in molecule.smi (2667/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332536 in molecule.smi (2670/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332537 in molecule.smi (2669/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332539 in molecule.smi (2672/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332535 in molecule.smi (2671/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL156016 in molecule.smi (2674/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2332538 in molecule.smi (2673/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2377874 in molecule.smi (2675/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2377878 in molecule.smi (2676/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2377877 in molecule.smi (2677/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2377876 in molecule.smi (2678/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2377875 in molecule.smi (2679/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2375480 in molecule.smi (2680/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2377873 in molecule.smi (2681/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2375483 in molecule.smi (2682/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2375482 in molecule.smi (2683/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2375481 in molecule.smi (2684/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380667 in molecule.smi (2685/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380677 in molecule.smi (2686/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380676 in molecule.smi (2687/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380675 in molecule.smi (2688/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380674 in molecule.smi (2689/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380672 in molecule.smi (2691/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380673 in molecule.smi (2690/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380670 in molecule.smi (2693/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380671 in molecule.smi (2692/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380668 in molecule.smi (2695/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2380669 in molecule.smi (2694/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2386558 in molecule.smi (2697/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2381401 in molecule.smi (2696/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396914 in molecule.smi (2698/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396913 in molecule.smi (2699/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1429841 in molecule.smi (2701/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396912 in molecule.smi (2700/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396910 in molecule.smi (2703/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396911 in molecule.smi (2702/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396908 in molecule.smi (2705/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396909 in molecule.smi (2704/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396907 in molecule.smi (2707/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1572608 in molecule.smi (2706/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396906 in molecule.smi (2708/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396904 in molecule.smi (2710/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396905 in molecule.smi (2709/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1376358 in molecule.smi (2712/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2396903 in molecule.smi (2711/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL145 in molecule.smi (2713/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2402007 in molecule.smi (2715/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1443379 in molecule.smi (2714/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL164660 in molecule.smi (2716/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1079905 in molecule.smi (2717/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL113 in molecule.smi (2719/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL628 in molecule.smi (2718/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2409142 in molecule.smi (2721/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1424 in molecule.smi (2720/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2409141 in molecule.smi (2723/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL448799 in molecule.smi (2722/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413744 in molecule.smi (2725/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2409140 in molecule.smi (2724/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413742 in molecule.smi (2727/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413743 in molecule.smi (2726/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413740 in molecule.smi (2729/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413741 in molecule.smi (2728/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413738 in molecule.smi (2731/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413739 in molecule.smi (2730/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413736 in molecule.smi (2733/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413737 in molecule.smi (2732/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413734 in molecule.smi (2735/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413735 in molecule.smi (2734/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2413732 in molecule.smi (2737/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413733 in molecule.smi (2736/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413561 in molecule.smi (2738/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413560 in molecule.smi (2739/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413559 in molecule.smi (2740/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413558 in molecule.smi (2741/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413745 in molecule.smi (2742/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2413746 in molecule.smi (2743/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL377740 in molecule.smi (2744/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419690 in molecule.smi (2745/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419688 in molecule.smi (2747/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419689 in molecule.smi (2746/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419687 in molecule.smi (2748/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419686 in molecule.smi (2749/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419684 in molecule.smi (2751/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419685 in molecule.smi (2750/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419683 in molecule.smi (2752/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419681 in molecule.smi (2754/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2419682 in molecule.smi (2753/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425415 in molecule.smi (2755/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425414 in molecule.smi (2756/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425413 in molecule.smi (2757/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425412 in molecule.smi (2758/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425410 in molecule.smi (2759/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425409 in molecule.smi (2760/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425408 in molecule.smi (2761/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425407 in molecule.smi (2762/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425418 in molecule.smi (2763/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2425417 in molecule.smi (2764/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441695 in molecule.smi (2765/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441694 in molecule.smi (2766/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441693 in molecule.smi (2767/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441692 in molecule.smi (2768/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441691 in molecule.smi (2769/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441690 in molecule.smi (2770/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441689 in molecule.smi (2771/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441688 in molecule.smi (2772/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441687 in molecule.smi (2773/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441686 in molecule.smi (2774/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441685 in molecule.smi (2775/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441684 in molecule.smi (2776/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2440134 in molecule.smi (2777/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441683 in molecule.smi (2778/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441682 in molecule.smi (2779/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441681 in molecule.smi (2780/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441680 in molecule.smi (2781/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441679 in molecule.smi (2782/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441678 in molecule.smi (2783/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441676 in molecule.smi (2785/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441677 in molecule.smi (2784/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441674 in molecule.smi (2787/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441675 in molecule.smi (2786/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441672 in molecule.smi (2789/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441673 in molecule.smi (2788/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441670 in molecule.smi (2791/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441671 in molecule.smi (2790/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441669 in molecule.smi (2792/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441668 in molecule.smi (2793/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441667 in molecule.smi (2794/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441666 in molecule.smi (2795/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL2441665 in molecule.smi (2796/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087683 in molecule.smi (2797/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087682 in molecule.smi (2798/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087681 in molecule.smi (2799/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087680 in molecule.smi (2800/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087679 in molecule.smi (2801/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087677 in molecule.smi (2802/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087676 in molecule.smi (2803/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087675 in molecule.smi (2804/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3087674 in molecule.smi (2805/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3085881 in molecule.smi (2806/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL398628 in molecule.smi (2808/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL430403 in molecule.smi (2807/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3086278 in molecule.smi (2810/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3086279 in molecule.smi (2809/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3086277 in molecule.smi (2811/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093180 in molecule.smi (2813/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3086276 in molecule.smi (2812/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093174 in molecule.smi (2815/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093175 in molecule.smi (2814/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093791 in molecule.smi (2817/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093173 in molecule.smi (2816/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093804 in molecule.smi (2819/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093787 in molecule.smi (2818/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093802 in molecule.smi (2821/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093803 in molecule.smi (2820/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093801 in molecule.smi (2822/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093800 in molecule.smi (2823/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093799 in molecule.smi (2824/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093798 in molecule.smi (2825/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093797 in molecule.smi (2826/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093796 in molecule.smi (2827/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093795 in molecule.smi (2828/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3093794 in molecule.smi (2829/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL187266 in molecule.smi (2830/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL363535 in molecule.smi (2831/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL227075 in molecule.smi (2832/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3099500 in molecule.smi (2833/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL51085 in molecule.smi (2834/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115026 in molecule.smi (2835/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3112614 in molecule.smi (2836/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115025 in molecule.smi (2837/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115024 in molecule.smi (2838/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115023 in molecule.smi (2839/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115022 in molecule.smi (2840/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115021 in molecule.smi (2841/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115020 in molecule.smi (2842/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115029 in molecule.smi (2843/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115028 in molecule.smi (2844/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115027 in molecule.smi (2845/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115038 in molecule.smi (2846/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115037 in molecule.smi (2847/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115036 in molecule.smi (2848/4695). Average speed: 0.23 s/mol.\n", - "Processing CHEMBL3115035 in molecule.smi (2849/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115034 in molecule.smi (2850/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115033 in molecule.smi (2851/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115032 in molecule.smi (2852/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115031 in molecule.smi (2853/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115030 in molecule.smi (2854/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115198 in molecule.smi (2855/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115197 in molecule.smi (2856/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115196 in molecule.smi (2857/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115048 in molecule.smi (2858/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115046 in molecule.smi (2860/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115047 in molecule.smi (2859/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115045 in molecule.smi (2861/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115044 in molecule.smi (2862/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115043 in molecule.smi (2863/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115042 in molecule.smi (2864/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115041 in molecule.smi (2865/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115040 in molecule.smi (2866/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3115039 in molecule.smi (2867/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116301 in molecule.smi (2868/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116300 in molecule.smi (2869/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116299 in molecule.smi (2870/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116298 in molecule.smi (2871/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116297 in molecule.smi (2872/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116296 in molecule.smi (2873/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116295 in molecule.smi (2874/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116294 in molecule.smi (2875/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116293 in molecule.smi (2876/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116292 in molecule.smi (2877/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116291 in molecule.smi (2878/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116290 in molecule.smi (2879/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116289 in molecule.smi (2880/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1973869 in molecule.smi (2881/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116288 in molecule.smi (2882/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116287 in molecule.smi (2883/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116286 in molecule.smi (2884/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116285 in molecule.smi (2885/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116284 in molecule.smi (2886/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116283 in molecule.smi (2887/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116282 in molecule.smi (2888/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116281 in molecule.smi (2889/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116280 in molecule.smi (2890/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116278 in molecule.smi (2892/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3116279 in molecule.smi (2891/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122169 in molecule.smi (2893/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122167 in molecule.smi (2895/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122168 in molecule.smi (2894/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122150 in molecule.smi (2897/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122166 in molecule.smi (2896/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122148 in molecule.smi (2899/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122149 in molecule.smi (2898/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122147 in molecule.smi (2900/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122145 in molecule.smi (2902/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122146 in molecule.smi (2901/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122172 in molecule.smi (2903/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3120185 in molecule.smi (2904/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122171 in molecule.smi (2905/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3122170 in molecule.smi (2906/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3127003 in molecule.smi (2907/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3126996 in molecule.smi (2908/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3127006 in molecule.smi (2909/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133436 in molecule.smi (2910/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133433 in molecule.smi (2912/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133434 in molecule.smi (2911/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133432 in molecule.smi (2913/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133430 in molecule.smi (2914/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133427 in molecule.smi (2915/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133444 in molecule.smi (2916/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133443 in molecule.smi (2917/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133442 in molecule.smi (2918/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133440 in molecule.smi (2919/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3133439 in molecule.smi (2920/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220507 in molecule.smi (2921/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1325943 in molecule.smi (2922/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220508 in molecule.smi (2923/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220509 in molecule.smi (2924/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220510 in molecule.smi (2925/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220801 in molecule.smi (2927/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220513 in molecule.smi (2926/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220803 in molecule.smi (2929/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220802 in molecule.smi (2928/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220804 in molecule.smi (2930/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220805 in molecule.smi (2931/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220806 in molecule.smi (2932/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220807 in molecule.smi (2933/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3220808 in molecule.smi (2934/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237628 in molecule.smi (2935/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237629 in molecule.smi (2936/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237630 in molecule.smi (2937/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237631 in molecule.smi (2938/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237632 in molecule.smi (2939/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237633 in molecule.smi (2940/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237634 in molecule.smi (2941/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237520 in molecule.smi (2942/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1377579 in molecule.smi (2944/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3237527 in molecule.smi (2943/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1344483 in molecule.smi (2945/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233990 in molecule.smi (2946/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233991 in molecule.smi (2947/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233022 in molecule.smi (2948/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233992 in molecule.smi (2949/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1191355 in molecule.smi (2951/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233993 in molecule.smi (2950/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233994 in molecule.smi (2953/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1194868 in molecule.smi (2952/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233995 in molecule.smi (2954/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1190535 in molecule.smi (2956/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1192998 in molecule.smi (2955/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233996 in molecule.smi (2957/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233997 in molecule.smi (2958/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233999 in molecule.smi (2960/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233998 in molecule.smi (2959/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234000 in molecule.smi (2962/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1556015 in molecule.smi (2961/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1611608 in molecule.smi (2963/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL388560 in molecule.smi (2965/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234001 in molecule.smi (2964/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3235503 in molecule.smi (2967/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3235502 in molecule.smi (2966/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2171309 in molecule.smi (2968/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234591 in molecule.smi (2969/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234587 in molecule.smi (2971/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234592 in molecule.smi (2970/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234589 in molecule.smi (2973/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234588 in molecule.smi (2972/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234590 in molecule.smi (2974/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234593 in molecule.smi (2975/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234594 in molecule.smi (2976/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233826 in molecule.smi (2977/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233827 in molecule.smi (2978/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233828 in molecule.smi (2979/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233829 in molecule.smi (2980/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233830 in molecule.smi (2981/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233831 in molecule.smi (2982/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233832 in molecule.smi (2983/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234038 in molecule.smi (2984/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234039 in molecule.smi (2985/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3234040 in molecule.smi (2986/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL418068 in molecule.smi (2987/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2007996 in molecule.smi (2988/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233935 in molecule.smi (2990/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3233934 in molecule.smi (2989/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261985 in molecule.smi (2992/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261984 in molecule.smi (2991/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261987 in molecule.smi (2994/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261986 in molecule.smi (2993/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261989 in molecule.smi (2996/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261988 in molecule.smi (2995/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261990 in molecule.smi (2997/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261991 in molecule.smi (2998/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3261992 in molecule.smi (2999/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262658 in molecule.smi (3000/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262661 in molecule.smi (3001/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262662 in molecule.smi (3002/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262663 in molecule.smi (3003/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262664 in molecule.smi (3004/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262665 in molecule.smi (3005/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262666 in molecule.smi (3006/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262668 in molecule.smi (3008/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262667 in molecule.smi (3007/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262670 in molecule.smi (3010/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262669 in molecule.smi (3009/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262671 in molecule.smi (3011/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262673 in molecule.smi (3013/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3262672 in molecule.smi (3012/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263725 in molecule.smi (3015/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL510363 in molecule.smi (3014/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263726 in molecule.smi (3017/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263724 in molecule.smi (3016/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263728 in molecule.smi (3019/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263727 in molecule.smi (3018/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263730 in molecule.smi (3021/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263729 in molecule.smi (3020/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263732 in molecule.smi (3023/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263731 in molecule.smi (3022/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263733 in molecule.smi (3024/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263734 in molecule.smi (3025/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263735 in molecule.smi (3026/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263736 in molecule.smi (3027/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3263737 in molecule.smi (3028/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289924 in molecule.smi (3029/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289925 in molecule.smi (3030/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289926 in molecule.smi (3031/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289927 in molecule.smi (3032/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289928 in molecule.smi (3033/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289929 in molecule.smi (3034/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289931 in molecule.smi (3036/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289930 in molecule.smi (3035/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289932 in molecule.smi (3037/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289933 in molecule.smi (3038/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289934 in molecule.smi (3039/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289936 in molecule.smi (3041/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289935 in molecule.smi (3040/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289938 in molecule.smi (3043/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289937 in molecule.smi (3042/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289939 in molecule.smi (3044/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289941 in molecule.smi (3046/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289940 in molecule.smi (3045/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289942 in molecule.smi (3047/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3289943 in molecule.smi (3048/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290187 in molecule.smi (3049/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290188 in molecule.smi (3050/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290189 in molecule.smi (3051/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290190 in molecule.smi (3052/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290192 in molecule.smi (3054/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290191 in molecule.smi (3053/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290193 in molecule.smi (3055/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290195 in molecule.smi (3057/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290194 in molecule.smi (3056/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3290196 in molecule.smi (3058/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL40275 in molecule.smi (3059/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL385181 in molecule.smi (3060/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL32778 in molecule.smi (3061/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3360486 in molecule.smi (3062/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3360487 in molecule.smi (3063/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353039 in molecule.smi (3064/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353040 in molecule.smi (3065/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353041 in molecule.smi (3066/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353042 in molecule.smi (3067/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353566 in molecule.smi (3068/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353567 in molecule.smi (3069/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3353568 in molecule.smi (3070/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323068 in molecule.smi (3072/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3361270 in molecule.smi (3071/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323065 in molecule.smi (3073/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323062 in molecule.smi (3074/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323059 in molecule.smi (3075/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323056 in molecule.smi (3076/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323053 in molecule.smi (3077/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323051 in molecule.smi (3078/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323049 in molecule.smi (3079/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323046 in molecule.smi (3080/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323043 in molecule.smi (3081/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323042 in molecule.smi (3082/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323039 in molecule.smi (3083/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323036 in molecule.smi (3084/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3323035 in molecule.smi (3085/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322157 in molecule.smi (3086/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322140 in molecule.smi (3087/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322141 in molecule.smi (3088/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322142 in molecule.smi (3089/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322158 in molecule.smi (3090/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322159 in molecule.smi (3091/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322160 in molecule.smi (3092/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322161 in molecule.smi (3093/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322162 in molecule.smi (3094/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322155 in molecule.smi (3095/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322156 in molecule.smi (3096/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3313966 in molecule.smi (3097/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1134 in molecule.smi (3098/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322231 in molecule.smi (3100/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1196204 in molecule.smi (3099/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322233 in molecule.smi (3102/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322234 in molecule.smi (3101/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3322232 in molecule.smi (3103/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL278020 in molecule.smi (3104/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3310703 in molecule.smi (3105/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3360758 in molecule.smi (3106/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3310705 in molecule.smi (3107/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3360757 in molecule.smi (3109/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3310704 in molecule.smi (3108/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3310697 in molecule.smi (3111/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3310706 in molecule.smi (3110/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3318018 in molecule.smi (3113/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3310699 in molecule.smi (3112/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2391486 in molecule.smi (3115/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3318017 in molecule.smi (3114/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL276473 in molecule.smi (3116/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2158994 in molecule.smi (3117/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338992 in molecule.smi (3119/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338991 in molecule.smi (3118/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338994 in molecule.smi (3121/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338993 in molecule.smi (3120/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338995 in molecule.smi (3123/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3337471 in molecule.smi (3122/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338997 in molecule.smi (3125/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338996 in molecule.smi (3124/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338998 in molecule.smi (3126/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338999 in molecule.smi (3127/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339000 in molecule.smi (3128/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339001 in molecule.smi (3129/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339002 in molecule.smi (3130/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339003 in molecule.smi (3131/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339004 in molecule.smi (3132/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339005 in molecule.smi (3133/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339006 in molecule.smi (3134/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339007 in molecule.smi (3135/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3339008 in molecule.smi (3136/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3355581 in molecule.smi (3137/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3355580 in molecule.smi (3138/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3355579 in molecule.smi (3139/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL151763 in molecule.smi (3140/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335072 in molecule.smi (3141/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335071 in molecule.smi (3142/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335070 in molecule.smi (3143/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3334506 in molecule.smi (3144/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335068 in molecule.smi (3146/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335069 in molecule.smi (3145/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335066 in molecule.smi (3148/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335067 in molecule.smi (3147/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335064 in molecule.smi (3150/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335065 in molecule.smi (3149/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335062 in molecule.smi (3152/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335063 in molecule.smi (3151/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335061 in molecule.smi (3153/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335060 in molecule.smi (3154/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335059 in molecule.smi (3155/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335058 in molecule.smi (3156/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335057 in molecule.smi (3157/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335056 in molecule.smi (3158/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335055 in molecule.smi (3159/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335054 in molecule.smi (3160/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335053 in molecule.smi (3161/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335052 in molecule.smi (3162/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335051 in molecule.smi (3163/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335050 in molecule.smi (3164/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335049 in molecule.smi (3165/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335048 in molecule.smi (3166/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335047 in molecule.smi (3167/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335046 in molecule.smi (3168/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335032 in molecule.smi (3169/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335031 in molecule.smi (3170/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335030 in molecule.smi (3171/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335029 in molecule.smi (3172/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335028 in molecule.smi (3173/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335027 in molecule.smi (3174/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335026 in molecule.smi (3175/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335025 in molecule.smi (3176/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335024 in molecule.smi (3177/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335023 in molecule.smi (3178/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335022 in molecule.smi (3179/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335021 in molecule.smi (3180/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335020 in molecule.smi (3181/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335019 in molecule.smi (3182/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335017 in molecule.smi (3184/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3335018 in molecule.smi (3183/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343708 in molecule.smi (3186/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343705 in molecule.smi (3185/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343712 in molecule.smi (3188/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343711 in molecule.smi (3187/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343714 in molecule.smi (3190/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343713 in molecule.smi (3189/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3355594 in molecule.smi (3191/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338389 in molecule.smi (3192/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338388 in molecule.smi (3193/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338387 in molecule.smi (3194/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3338386 in molecule.smi (3195/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343884 in molecule.smi (3196/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343885 in molecule.smi (3197/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343920 in molecule.smi (3198/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343922 in molecule.smi (3199/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343882 in molecule.smi (3200/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343923 in molecule.smi (3201/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343925 in molecule.smi (3203/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343924 in molecule.smi (3202/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343927 in molecule.smi (3205/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343926 in molecule.smi (3204/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343929 in molecule.smi (3207/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343928 in molecule.smi (3206/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343931 in molecule.smi (3209/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343930 in molecule.smi (3208/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356528 in molecule.smi (3211/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3343932 in molecule.smi (3210/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356529 in molecule.smi (3212/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356531 in molecule.smi (3214/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356530 in molecule.smi (3213/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356533 in molecule.smi (3216/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356532 in molecule.smi (3215/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356535 in molecule.smi (3218/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356534 in molecule.smi (3217/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356537 in molecule.smi (3220/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356536 in molecule.smi (3219/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356539 in molecule.smi (3222/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356538 in molecule.smi (3221/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356950 in molecule.smi (3224/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356540 in molecule.smi (3223/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356951 in molecule.smi (3225/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356953 in molecule.smi (3227/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356952 in molecule.smi (3226/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3356954 in molecule.smi (3228/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3393675 in molecule.smi (3229/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3393674 in molecule.smi (3230/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3393673 in molecule.smi (3231/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403878 in molecule.smi (3232/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403877 in molecule.smi (3233/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3400187 in molecule.smi (3234/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403874 in molecule.smi (3235/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402707 in molecule.smi (3236/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402708 in molecule.smi (3237/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402709 in molecule.smi (3238/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402710 in molecule.smi (3239/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402712 in molecule.smi (3240/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402713 in molecule.smi (3241/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402714 in molecule.smi (3242/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402715 in molecule.smi (3243/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402717 in molecule.smi (3244/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402718 in molecule.smi (3245/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402719 in molecule.smi (3246/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3402720 in molecule.smi (3247/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403305 in molecule.smi (3248/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403307 in molecule.smi (3250/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403306 in molecule.smi (3249/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403309 in molecule.smi (3252/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403308 in molecule.smi (3251/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403311 in molecule.smi (3254/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403310 in molecule.smi (3253/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403312 in molecule.smi (3255/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403314 in molecule.smi (3257/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403313 in molecule.smi (3256/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403315 in molecule.smi (3258/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3403316 in molecule.smi (3259/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407587 in molecule.smi (3260/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407586 in molecule.smi (3261/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407585 in molecule.smi (3262/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407584 in molecule.smi (3263/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407583 in molecule.smi (3264/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407582 in molecule.smi (3265/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407581 in molecule.smi (3266/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3407591 in molecule.smi (3267/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410957 in molecule.smi (3268/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410956 in molecule.smi (3269/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410955 in molecule.smi (3270/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410954 in molecule.smi (3271/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410953 in molecule.smi (3272/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410952 in molecule.smi (3273/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3410951 in molecule.smi (3274/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417009 in molecule.smi (3275/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL85251 in molecule.smi (3276/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3414597 in molecule.smi (3277/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3416996 in molecule.smi (3279/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3416995 in molecule.smi (3278/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3416998 in molecule.smi (3281/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3416997 in molecule.smi (3280/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417000 in molecule.smi (3283/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3416999 in molecule.smi (3282/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417002 in molecule.smi (3285/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417001 in molecule.smi (3284/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417004 in molecule.smi (3287/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417003 in molecule.smi (3286/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417006 in molecule.smi (3288/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417008 in molecule.smi (3289/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415643 in molecule.smi (3290/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415558 in molecule.smi (3291/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415559 in molecule.smi (3292/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415560 in molecule.smi (3293/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415563 in molecule.smi (3295/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415561 in molecule.smi (3294/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415564 in molecule.smi (3296/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415566 in molecule.smi (3298/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415565 in molecule.smi (3297/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415568 in molecule.smi (3299/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415570 in molecule.smi (3301/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415569 in molecule.smi (3300/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415573 in molecule.smi (3302/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415574 in molecule.smi (3303/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415575 in molecule.smi (3304/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415576 in molecule.smi (3305/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415578 in molecule.smi (3306/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415579 in molecule.smi (3307/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415580 in molecule.smi (3308/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3415581 in molecule.smi (3309/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417300 in molecule.smi (3310/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3417299 in molecule.smi (3311/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577505 in molecule.smi (3312/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577508 in molecule.smi (3313/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577509 in molecule.smi (3314/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577510 in molecule.smi (3315/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577511 in molecule.smi (3316/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577517 in molecule.smi (3317/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577520 in molecule.smi (3318/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577522 in molecule.smi (3319/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3359895 in molecule.smi (3321/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3577524 in molecule.smi (3320/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582200 in molecule.smi (3322/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582202 in molecule.smi (3324/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582201 in molecule.smi (3323/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582204 in molecule.smi (3326/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582203 in molecule.smi (3325/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582206 in molecule.smi (3328/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582205 in molecule.smi (3327/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582208 in molecule.smi (3330/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582207 in molecule.smi (3329/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582210 in molecule.smi (3332/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582209 in molecule.smi (3331/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582212 in molecule.smi (3334/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582211 in molecule.smi (3333/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582213 in molecule.smi (3335/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582214 in molecule.smi (3336/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582215 in molecule.smi (3337/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582218 in molecule.smi (3339/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582216 in molecule.smi (3338/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582224 in molecule.smi (3341/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582223 in molecule.smi (3340/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582226 in molecule.smi (3343/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582225 in molecule.smi (3342/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582228 in molecule.smi (3345/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582227 in molecule.smi (3344/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582230 in molecule.smi (3347/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582229 in molecule.smi (3346/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582231 in molecule.smi (3348/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3582232 in molecule.smi (3349/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3581580 in molecule.smi (3350/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3581576 in molecule.smi (3351/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3581573 in molecule.smi (3352/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3581572 in molecule.smi (3353/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3585374 in molecule.smi (3354/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586574 in molecule.smi (3356/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3585375 in molecule.smi (3355/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586576 in molecule.smi (3357/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586577 in molecule.smi (3358/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586582 in molecule.smi (3359/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586583 in molecule.smi (3360/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586592 in molecule.smi (3361/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586593 in molecule.smi (3362/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586594 in molecule.smi (3363/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586595 in molecule.smi (3364/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586600 in molecule.smi (3365/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586602 in molecule.smi (3366/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586604 in molecule.smi (3367/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586605 in molecule.smi (3368/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586608 in molecule.smi (3369/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586610 in molecule.smi (3370/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586614 in molecule.smi (3371/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586196 in molecule.smi (3372/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586197 in molecule.smi (3373/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586198 in molecule.smi (3374/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586199 in molecule.smi (3375/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586201 in molecule.smi (3377/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586200 in molecule.smi (3376/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586202 in molecule.smi (3378/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL506258 in molecule.smi (3380/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586203 in molecule.smi (3379/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586205 in molecule.smi (3382/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3586204 in molecule.smi (3381/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3586206 in molecule.smi (3383/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3586207 in molecule.smi (3384/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL295124 in molecule.smi (3385/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585777 in molecule.smi (3386/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585776 in molecule.smi (3387/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585775 in molecule.smi (3388/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585778 in molecule.smi (3389/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585779 in molecule.smi (3390/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585780 in molecule.smi (3391/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585781 in molecule.smi (3392/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585782 in molecule.smi (3393/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585783 in molecule.smi (3394/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3585784 in molecule.smi (3395/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597939 in molecule.smi (3396/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597941 in molecule.smi (3397/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597942 in molecule.smi (3398/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597946 in molecule.smi (3400/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597944 in molecule.smi (3399/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597948 in molecule.smi (3401/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597548 in molecule.smi (3403/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597556 in molecule.smi (3402/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597068 in molecule.smi (3404/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597067 in molecule.smi (3405/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597064 in molecule.smi (3407/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3597065 in molecule.smi (3406/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597063 in molecule.smi (3408/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597062 in molecule.smi (3409/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597061 in molecule.smi (3410/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597060 in molecule.smi (3411/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597059 in molecule.smi (3412/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597058 in molecule.smi (3413/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597057 in molecule.smi (3414/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597056 in molecule.smi (3415/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597055 in molecule.smi (3416/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597054 in molecule.smi (3417/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597010 in molecule.smi (3418/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597009 in molecule.smi (3419/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597008 in molecule.smi (3420/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597007 in molecule.smi (3421/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597005 in molecule.smi (3423/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597006 in molecule.smi (3422/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597003 in molecule.smi (3425/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597004 in molecule.smi (3424/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597001 in molecule.smi (3427/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597002 in molecule.smi (3426/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3600551 in molecule.smi (3429/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3597000 in molecule.smi (3428/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3600553 in molecule.smi (3431/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3600552 in molecule.smi (3430/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3600554 in molecule.smi (3432/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3600555 in molecule.smi (3433/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3600556 in molecule.smi (3434/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605437 in molecule.smi (3435/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605436 in molecule.smi (3436/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605434 in molecule.smi (3437/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605433 in molecule.smi (3438/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605432 in molecule.smi (3439/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605431 in molecule.smi (3440/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605430 in molecule.smi (3441/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605428 in molecule.smi (3442/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605426 in molecule.smi (3443/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605425 in molecule.smi (3444/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605424 in molecule.smi (3445/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605423 in molecule.smi (3446/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605422 in molecule.smi (3447/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605419 in molecule.smi (3448/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605418 in molecule.smi (3449/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605316 in molecule.smi (3450/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605314 in molecule.smi (3451/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605313 in molecule.smi (3452/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605310 in molecule.smi (3453/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605309 in molecule.smi (3454/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3605308 in molecule.smi (3455/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604192 in molecule.smi (3456/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604193 in molecule.smi (3457/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604194 in molecule.smi (3458/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604195 in molecule.smi (3459/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604196 in molecule.smi (3460/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604197 in molecule.smi (3461/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604198 in molecule.smi (3462/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3604199 in molecule.smi (3463/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3621335 in molecule.smi (3464/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3621334 in molecule.smi (3465/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3621333 in molecule.smi (3466/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3621332 in molecule.smi (3467/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623558 in molecule.smi (3469/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3621322 in molecule.smi (3468/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623557 in molecule.smi (3470/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623556 in molecule.smi (3471/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623555 in molecule.smi (3472/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623554 in molecule.smi (3473/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623553 in molecule.smi (3474/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623552 in molecule.smi (3475/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623551 in molecule.smi (3476/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623550 in molecule.smi (3477/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623549 in molecule.smi (3478/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623548 in molecule.smi (3479/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL89354 in molecule.smi (3480/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3137700 in molecule.smi (3481/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL167911 in molecule.smi (3482/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL446060 in molecule.smi (3483/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623563 in molecule.smi (3484/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623562 in molecule.smi (3485/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623561 in molecule.smi (3486/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623560 in molecule.smi (3487/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623559 in molecule.smi (3488/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623567 in molecule.smi (3489/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623566 in molecule.smi (3490/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623565 in molecule.smi (3491/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3623564 in molecule.smi (3492/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628053 in molecule.smi (3493/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628054 in molecule.smi (3494/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628055 in molecule.smi (3495/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628056 in molecule.smi (3496/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628057 in molecule.smi (3497/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628058 in molecule.smi (3498/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628059 in molecule.smi (3499/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628061 in molecule.smi (3501/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628060 in molecule.smi (3500/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628063 in molecule.smi (3503/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628062 in molecule.smi (3502/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628065 in molecule.smi (3505/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628064 in molecule.smi (3504/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628067 in molecule.smi (3507/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628066 in molecule.smi (3506/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628183 in molecule.smi (3508/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628184 in molecule.smi (3509/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628185 in molecule.smi (3510/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3628186 in molecule.smi (3511/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632858 in molecule.smi (3512/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632857 in molecule.smi (3513/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632856 in molecule.smi (3514/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632855 in molecule.smi (3515/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632853 in molecule.smi (3517/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632854 in molecule.smi (3516/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632846 in molecule.smi (3519/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632852 in molecule.smi (3518/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632847 in molecule.smi (3520/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632626 in molecule.smi (3521/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632848 in molecule.smi (3522/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632849 in molecule.smi (3523/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632850 in molecule.smi (3524/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632851 in molecule.smi (3525/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632987 in molecule.smi (3526/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632988 in molecule.smi (3527/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632989 in molecule.smi (3528/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632991 in molecule.smi (3530/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632990 in molecule.smi (3529/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632992 in molecule.smi (3531/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632993 in molecule.smi (3532/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL153 in molecule.smi (3534/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3632994 in molecule.smi (3533/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642057 in molecule.smi (3535/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642053 in molecule.smi (3537/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642042 in molecule.smi (3536/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642043 in molecule.smi (3538/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642045 in molecule.smi (3539/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642054 in molecule.smi (3540/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642047 in molecule.smi (3541/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642049 in molecule.smi (3542/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642041 in molecule.smi (3543/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642046 in molecule.smi (3544/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642050 in molecule.smi (3545/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642052 in molecule.smi (3546/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642055 in molecule.smi (3547/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642044 in molecule.smi (3548/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642051 in molecule.smi (3549/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642040 in molecule.smi (3550/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642048 in molecule.smi (3551/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3642056 in molecule.smi (3552/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3736339 in molecule.smi (3553/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3735606 in molecule.smi (3554/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3734787 in molecule.smi (3555/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3735042 in molecule.smi (3556/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL2179375 in molecule.smi (3557/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3401159 in molecule.smi (3558/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752974 in molecule.smi (3559/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752232 in molecule.smi (3560/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754631 in molecule.smi (3561/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754448 in molecule.smi (3562/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752908 in molecule.smi (3563/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754585 in molecule.smi (3564/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754622 in molecule.smi (3565/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752710 in molecule.smi (3566/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL189886 in molecule.smi (3567/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754480 in molecule.smi (3568/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754739 in molecule.smi (3569/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754146 in molecule.smi (3571/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752926 in molecule.smi (3570/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752356 in molecule.smi (3573/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3751941 in molecule.smi (3572/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752543 in molecule.smi (3575/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752969 in molecule.smi (3574/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3753405 in molecule.smi (3577/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752467 in molecule.smi (3576/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754694 in molecule.smi (3579/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754341 in molecule.smi (3578/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754778 in molecule.smi (3581/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3753957 in molecule.smi (3580/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3753360 in molecule.smi (3583/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752384 in molecule.smi (3582/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754005 in molecule.smi (3585/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754672 in molecule.smi (3584/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3751866 in molecule.smi (3587/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752685 in molecule.smi (3586/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3754291 in molecule.smi (3588/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3753850 in molecule.smi (3590/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752119 in molecule.smi (3589/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3753706 in molecule.smi (3592/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3753508 in molecule.smi (3591/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752555 in molecule.smi (3594/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752451 in molecule.smi (3593/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3752227 in molecule.smi (3595/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3758282 in molecule.smi (3597/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3759090 in molecule.smi (3596/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL395280 in molecule.smi (3599/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3759013 in molecule.smi (3598/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764433 in molecule.smi (3600/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764714 in molecule.smi (3602/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764900 in molecule.smi (3601/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764065 in molecule.smi (3604/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763417 in molecule.smi (3603/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764488 in molecule.smi (3605/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765098 in molecule.smi (3607/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763832 in molecule.smi (3606/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763973 in molecule.smi (3608/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764376 in molecule.smi (3610/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763232 in molecule.smi (3609/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765476 in molecule.smi (3612/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763609 in molecule.smi (3611/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763820 in molecule.smi (3613/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765697 in molecule.smi (3614/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763482 in molecule.smi (3616/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765569 in molecule.smi (3615/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765093 in molecule.smi (3617/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764100 in molecule.smi (3618/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765496 in molecule.smi (3620/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764679 in molecule.smi (3619/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763778 in molecule.smi (3621/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763203 in molecule.smi (3622/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764305 in molecule.smi (3624/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765162 in molecule.smi (3623/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764609 in molecule.smi (3626/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3765293 in molecule.smi (3625/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3763535 in molecule.smi (3628/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764762 in molecule.smi (3627/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769555 in molecule.smi (3630/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3764211 in molecule.smi (3629/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770203 in molecule.smi (3632/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769537 in molecule.smi (3631/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770803 in molecule.smi (3634/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770577 in molecule.smi (3633/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769862 in molecule.smi (3636/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770479 in molecule.smi (3635/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769833 in molecule.smi (3638/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770978 in molecule.smi (3637/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769576 in molecule.smi (3639/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769811 in molecule.smi (3641/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3771233 in molecule.smi (3640/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3769834 in molecule.smi (3643/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770569 in molecule.smi (3642/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770071 in molecule.smi (3644/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770206 in molecule.smi (3645/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1801816 in molecule.smi (3646/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770866 in molecule.smi (3647/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770516 in molecule.smi (3648/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1801815 in molecule.smi (3649/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1411769 in molecule.smi (3650/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3770356 in molecule.smi (3651/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1327885 in molecule.smi (3652/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL239618 in molecule.smi (3653/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL240047 in molecule.smi (3654/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL394753 in molecule.smi (3655/4695). 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Average speed: 0.25 s/mol.\n", - "Processing CHEMBL168938 in molecule.smi (3700/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819220 in molecule.smi (3701/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818167 in molecule.smi (3702/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818801 in molecule.smi (3704/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818084 in molecule.smi (3703/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819113 in molecule.smi (3706/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819531 in molecule.smi (3705/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819574 in molecule.smi (3707/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819247 in molecule.smi (3708/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818918 in molecule.smi (3709/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819024 in molecule.smi (3710/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818374 in molecule.smi (3711/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL524100 in molecule.smi (3712/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL489354 in molecule.smi (3713/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1254269 in molecule.smi (3714/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819036 in molecule.smi (3715/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL535 in molecule.smi (3716/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL477772 in molecule.smi (3717/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818089 in molecule.smi (3718/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818086 in molecule.smi (3719/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819231 in molecule.smi (3720/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819320 in molecule.smi (3721/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818992 in molecule.smi (3722/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3817981 in molecule.smi (3723/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819548 in molecule.smi (3724/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818689 in molecule.smi (3726/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819383 in molecule.smi (3725/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3819083 in molecule.smi (3728/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818512 in molecule.smi (3727/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818990 in molecule.smi (3730/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818332 in molecule.smi (3729/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818250 in molecule.smi (3732/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3818782 in molecule.smi (3731/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3824239 in molecule.smi (3734/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3822559 in molecule.smi (3733/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3823464 in molecule.smi (3736/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3823317 in molecule.smi (3735/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3822670 in molecule.smi (3738/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3823538 in molecule.smi (3737/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3824057 in molecule.smi (3740/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3822790 in molecule.smi (3739/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3822890 in molecule.smi (3742/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3824362 in molecule.smi (3741/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3823197 in molecule.smi (3744/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3823578 in molecule.smi (3743/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3824248 in molecule.smi (3745/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828259 in molecule.smi (3746/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827734 in molecule.smi (3747/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827064 in molecule.smi (3748/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828631 in molecule.smi (3749/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827589 in molecule.smi (3750/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828679 in molecule.smi (3751/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828028 in molecule.smi (3753/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827011 in molecule.smi (3752/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827624 in molecule.smi (3754/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827449 in molecule.smi (3756/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827347 in molecule.smi (3755/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828273 in molecule.smi (3757/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828739 in molecule.smi (3758/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828644 in molecule.smi (3759/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827856 in molecule.smi (3760/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827058 in molecule.smi (3761/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827189 in molecule.smi (3762/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL440526 in molecule.smi (3763/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827288 in molecule.smi (3764/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827432 in molecule.smi (3765/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828293 in molecule.smi (3766/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828693 in molecule.smi (3767/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827610 in molecule.smi (3768/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827044 in molecule.smi (3769/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828054 in molecule.smi (3770/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827220 in molecule.smi (3771/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828696 in molecule.smi (3772/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827284 in molecule.smi (3773/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827950 in molecule.smi (3774/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3826998 in molecule.smi (3776/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828281 in molecule.smi (3775/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827765 in molecule.smi (3778/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828443 in molecule.smi (3777/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828586 in molecule.smi (3779/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828385 in molecule.smi (3780/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3828381 in molecule.smi (3781/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3827575 in molecule.smi (3782/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3978533 in molecule.smi (3784/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3949439 in molecule.smi (3783/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3945872 in molecule.smi (3786/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3919086 in molecule.smi (3785/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3936989 in molecule.smi (3788/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3917990 in molecule.smi (3787/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3892044 in molecule.smi (3790/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3965783 in molecule.smi (3789/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3905695 in molecule.smi (3792/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3916769 in molecule.smi (3791/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3892118 in molecule.smi (3794/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3931599 in molecule.smi (3793/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3901082 in molecule.smi (3796/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3961103 in molecule.smi (3795/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3910142 in molecule.smi (3798/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3972214 in molecule.smi (3797/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL972 in molecule.smi (3800/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL8706 in molecule.smi (3799/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3960962 in molecule.smi (3802/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3291019 in molecule.smi (3801/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3931517 in molecule.smi (3804/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1903032 in molecule.smi (3803/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3969241 in molecule.smi (3805/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1485276 in molecule.smi (3807/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3939698 in molecule.smi (3806/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3926757 in molecule.smi (3809/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL577016 in molecule.smi (3808/4695). 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Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3943450 in molecule.smi (3820/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3933395 in molecule.smi (3823/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3985059 in molecule.smi (3822/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3960861 in molecule.smi (3824/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3926911 in molecule.smi (3825/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3890758 in molecule.smi (3826/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3961494 in molecule.smi (3827/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3892502 in molecule.smi (3828/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL145361 in molecule.smi (3829/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL435071 in molecule.smi (3830/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL171048 in molecule.smi (3831/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3910513 in molecule.smi (3832/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3891369 in molecule.smi (3833/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3919441 in molecule.smi (3834/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3900401 in molecule.smi (3835/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3928343 in molecule.smi (3836/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3944652 in molecule.smi (3837/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3973337 in molecule.smi (3838/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3889713 in molecule.smi (3839/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3889625 in molecule.smi (3840/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3934564 in molecule.smi (3841/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3950063 in molecule.smi (3842/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3984034 in molecule.smi (3843/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3955393 in molecule.smi (3844/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3976003 in molecule.smi (3845/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3928840 in molecule.smi (3846/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3983017 in molecule.smi (3847/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3901794 in molecule.smi (3848/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3972865 in molecule.smi (3849/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3910830 in molecule.smi (3850/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3945423 in molecule.smi (3851/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3930722 in molecule.smi (3852/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3958208 in molecule.smi (3853/4695). 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Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4094805 in molecule.smi (3952/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081805 in molecule.smi (3955/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4059887 in molecule.smi (3954/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4102562 in molecule.smi (3957/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4098196 in molecule.smi (3956/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072914 in molecule.smi (3958/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4069184 in molecule.smi (3959/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060687 in molecule.smi (3960/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081013 in molecule.smi (3961/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099046 in molecule.smi (3962/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070380 in molecule.smi (3963/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091317 in molecule.smi (3964/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4061746 in molecule.smi (3965/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4097374 in molecule.smi (3967/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083524 in molecule.smi (3966/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4105088 in molecule.smi (3969/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4079345 in molecule.smi (3968/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4067481 in molecule.smi (3971/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3262251 in molecule.smi (3970/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4078382 in molecule.smi (3973/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4094911 in molecule.smi (3972/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4086248 in molecule.smi (3975/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4102655 in molecule.smi (3974/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4064773 in molecule.smi (3976/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4061837 in molecule.smi (3977/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083601 in molecule.smi (3978/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070462 in molecule.smi (3979/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091385 in molecule.smi (3980/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4100046 in molecule.smi (3981/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4096673 in molecule.smi (3983/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4073193 in molecule.smi (3982/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060016 in molecule.smi (3985/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4069444 in molecule.smi (3984/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4085114 in molecule.smi (3986/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4098421 in molecule.smi (3987/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4077088 in molecule.smi (3988/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099790 in molecule.smi (3989/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1626455 in molecule.smi (3990/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4095445 in molecule.smi (3991/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL2337271 in molecule.smi (3992/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL215645 in molecule.smi (3993/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060617 in molecule.smi (3994/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081795 in molecule.smi (3995/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066309 in molecule.smi (3996/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4093419 in molecule.smi (3997/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083174 in molecule.smi (3998/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL521589 in molecule.smi (3999/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4096024 in molecule.smi (4000/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4098455 in molecule.smi (4001/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099598 in molecule.smi (4002/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4062146 in molecule.smi (4003/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083875 in molecule.smi (4004/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070769 in molecule.smi (4005/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091651 in molecule.smi (4006/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4092602 in molecule.smi (4007/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4064904 in molecule.smi (4008/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4100338 in molecule.smi (4009/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4073501 in molecule.smi (4010/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4074566 in molecule.smi (4011/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4095032 in molecule.smi (4012/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060800 in molecule.smi (4013/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4085222 in molecule.smi (4014/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4092048 in molecule.smi (4015/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4061527 in molecule.smi (4016/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081776 in molecule.smi (4017/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4105085 in molecule.smi (4019/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099794 in molecule.smi (4018/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083445 in molecule.smi (4020/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4093567 in molecule.smi (4021/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072865 in molecule.smi (4022/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4101303 in molecule.smi (4023/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4076930 in molecule.smi (4024/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4095535 in molecule.smi (4025/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4068149 in molecule.smi (4026/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4092123 in molecule.smi (4027/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4064413 in molecule.smi (4028/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099873 in molecule.smi (4029/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4074062 in molecule.smi (4031/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072999 in molecule.smi (4030/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4094591 in molecule.smi (4032/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4084371 in molecule.smi (4033/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4102333 in molecule.smi (4034/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066548 in molecule.smi (4035/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4094081 in molecule.smi (4036/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081915 in molecule.smi (4037/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4071377 in molecule.smi (4039/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060011 in molecule.smi (4038/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4098418 in molecule.smi (4040/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4077952 in molecule.smi (4042/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083306 in molecule.smi (4041/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070182 in molecule.smi (4044/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4076034 in molecule.smi (4043/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091851 in molecule.smi (4045/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4075061 in molecule.smi (4046/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066365 in molecule.smi (4047/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4093928 in molecule.smi (4048/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4097266 in molecule.smi (4049/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072457 in molecule.smi (4050/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL23838 in molecule.smi (4051/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060832 in molecule.smi (4052/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4089033 in molecule.smi (4053/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4088091 in molecule.smi (4054/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066805 in molecule.smi (4055/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4080216 in molecule.smi (4056/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4104459 in molecule.smi (4057/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4093532 in molecule.smi (4059/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072825 in molecule.smi (4058/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4090159 in molecule.smi (4061/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4069096 in molecule.smi (4060/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4097877 in molecule.smi (4063/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4079853 in molecule.smi (4062/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4071803 in molecule.smi (4065/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4098808 in molecule.smi (4064/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4082785 in molecule.smi (4067/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4085482 in molecule.smi (4066/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4101886 in molecule.smi (4069/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072478 in molecule.smi (4068/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4068410 in molecule.smi (4070/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4090842 in molecule.smi (4072/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4095755 in molecule.smi (4071/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4069847 in molecule.smi (4073/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091748 in molecule.smi (4075/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4063984 in molecule.smi (4074/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4072579 in molecule.smi (4076/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4068829 in molecule.smi (4078/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099486 in molecule.smi (4077/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4096126 in molecule.smi (4079/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081509 in molecule.smi (4081/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4059560 in molecule.smi (4080/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4098703 in molecule.smi (4082/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4095035 in molecule.smi (4084/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4076580 in molecule.smi (4083/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4073287 in molecule.smi (4085/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099391 in molecule.smi (4086/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4084842 in molecule.smi (4087/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4076986 in molecule.smi (4088/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4063583 in molecule.smi (4089/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4082048 in molecule.smi (4090/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083039 in molecule.smi (4091/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4101018 in molecule.smi (4092/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070471 in molecule.smi (4093/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1340607 in molecule.smi (4094/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4081932 in molecule.smi (4096/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1482486 in molecule.smi (4095/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4071394 in molecule.smi (4098/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099934 in molecule.smi (4097/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1543212 in molecule.smi (4100/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4092186 in molecule.smi (4099/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4095547 in molecule.smi (4102/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1416246 in molecule.smi (4101/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4087841 in molecule.smi (4104/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4075136 in molecule.smi (4103/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4079963 in molecule.smi (4106/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1301463 in molecule.smi (4105/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4092142 in molecule.smi (4108/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4104206 in molecule.smi (4107/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4064806 in molecule.smi (4110/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL179218 in molecule.smi (4109/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4073401 in molecule.smi (4112/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4100253 in molecule.smi (4111/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL14577 in molecule.smi (4114/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1311140 in molecule.smi (4113/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4068713 in molecule.smi (4116/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL451635 in molecule.smi (4115/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060058 in molecule.smi (4118/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4088294 in molecule.smi (4117/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4104406 in molecule.smi (4120/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4080917 in molecule.smi (4119/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4100057 in molecule.smi (4122/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4062826 in molecule.smi (4121/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4100639 in molecule.smi (4124/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4073838 in molecule.smi (4123/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4092905 in molecule.smi (4126/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4065242 in molecule.smi (4125/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4076907 in molecule.smi (4128/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091945 in molecule.smi (4127/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4062487 in molecule.smi (4130/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4084189 in molecule.smi (4129/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4061404 in molecule.smi (4131/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4102122 in molecule.smi (4133/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4089550 in molecule.smi (4132/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4064154 in molecule.smi (4135/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4077866 in molecule.smi (4134/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4086639 in molecule.smi (4137/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4085722 in molecule.smi (4136/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4094380 in molecule.smi (4139/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4104534 in molecule.smi (4138/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091001 in molecule.smi (4141/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066891 in molecule.smi (4140/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4064304 in molecule.smi (4143/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4063109 in molecule.smi (4142/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4080800 in molecule.smi (4144/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4090498 in molecule.smi (4146/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4094837 in molecule.smi (4145/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4104952 in molecule.smi (4148/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4069472 in molecule.smi (4147/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4097249 in molecule.smi (4150/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4080726 in molecule.smi (4149/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4078265 in molecule.smi (4152/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070057 in molecule.smi (4151/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4067342 in molecule.smi (4154/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4096311 in molecule.smi (4153/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4071110 in molecule.smi (4156/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4088594 in molecule.smi (4155/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070092 in molecule.smi (4158/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091935 in molecule.smi (4157/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4077169 in molecule.smi (4159/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4075825 in molecule.smi (4161/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4065259 in molecule.smi (4160/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4088659 in molecule.smi (4163/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4075624 in molecule.smi (4162/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4063635 in molecule.smi (4165/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4074993 in molecule.smi (4164/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4083288 in molecule.smi (4166/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4075667 in molecule.smi (4168/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066908 in molecule.smi (4167/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4069776 in molecule.smi (4170/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4091683 in molecule.smi (4169/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4089264 in molecule.smi (4172/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4096963 in molecule.smi (4171/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425895 in molecule.smi (4174/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL887 in molecule.smi (4173/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425894 in molecule.smi (4175/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425892 in molecule.smi (4177/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425893 in molecule.smi (4176/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1303143 in molecule.smi (4179/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425889 in molecule.smi (4178/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425891 in molecule.smi (4181/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425881 in molecule.smi (4180/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425885 in molecule.smi (4183/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL3425890 in molecule.smi (4182/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4061433 in molecule.smi (4185/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1242180 in molecule.smi (4184/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4070093 in molecule.smi (4187/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4097284 in molecule.smi (4186/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4066392 in molecule.smi (4189/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4093946 in molecule.smi (4188/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4104589 in molecule.smi (4191/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4080358 in molecule.smi (4190/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL597863 in molecule.smi (4192/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4096862 in molecule.smi (4193/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4060972 in molecule.smi (4194/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4089155 in molecule.smi (4195/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4099308 in molecule.smi (4196/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4086152 in molecule.smi (4197/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4065910 in molecule.smi (4198/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4093514 in molecule.smi (4199/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4086383 in molecule.smi (4200/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4064920 in molecule.smi (4201/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4078528 in molecule.smi (4202/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4102810 in molecule.smi (4203/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4067639 in molecule.smi (4204/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4095051 in molecule.smi (4205/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4105221 in molecule.smi (4206/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4087364 in molecule.smi (4207/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4072484 in molecule.smi (4208/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4099404 in molecule.smi (4209/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4065374 in molecule.smi (4210/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4086807 in molecule.smi (4211/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL140 in molecule.smi (4212/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL165 in molecule.smi (4213/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4079599 in molecule.smi (4215/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3425908 in molecule.smi (4214/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4071149 in molecule.smi (4216/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4094833 in molecule.smi (4217/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4067388 in molecule.smi (4218/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4102571 in molecule.smi (4219/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4078305 in molecule.smi (4220/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4064686 in molecule.smi (4221/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4086191 in molecule.smi (4222/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4073273 in molecule.smi (4223/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4093947 in molecule.smi (4224/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4059766 in molecule.smi (4225/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4081699 in molecule.smi (4226/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4076207 in molecule.smi (4227/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4080691 in molecule.smi (4228/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4098654 in molecule.smi (4229/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4068935 in molecule.smi (4230/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4100298 in molecule.smi (4231/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4100407 in molecule.smi (4233/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4092562 in molecule.smi (4232/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4073582 in molecule.smi (4234/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4074655 in molecule.smi (4235/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4100935 in molecule.smi (4237/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL511921 in molecule.smi (4236/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4062800 in molecule.smi (4239/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4082959 in molecule.smi (4238/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4085450 in molecule.smi (4241/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4090751 in molecule.smi (4240/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4068888 in molecule.smi (4243/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4063877 in molecule.smi (4242/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4094543 in molecule.smi (4245/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4077157 in molecule.smi (4244/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4065639 in molecule.smi (4247/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4067881 in molecule.smi (4246/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4128985 in molecule.smi (4249/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4125708 in molecule.smi (4248/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4127747 in molecule.smi (4250/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4129147 in molecule.smi (4251/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4128454 in molecule.smi (4252/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4127071 in molecule.smi (4253/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4130217 in molecule.smi (4254/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4125722 in molecule.smi (4255/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4129007 in molecule.smi (4256/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4129420 in molecule.smi (4257/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4128191 in molecule.smi (4259/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4128040 in molecule.smi (4258/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4126674 in molecule.smi (4261/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4129940 in molecule.smi (4260/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4128061 in molecule.smi (4262/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4163705 in molecule.smi (4263/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4164010 in molecule.smi (4264/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4176598 in molecule.smi (4265/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4172587 in molecule.smi (4266/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4164347 in molecule.smi (4267/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4174514 in molecule.smi (4268/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4166300 in molecule.smi (4269/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL1187434 in molecule.smi (4271/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4174269 in molecule.smi (4270/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4175159 in molecule.smi (4272/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4160912 in molecule.smi (4274/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4164525 in molecule.smi (4273/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4164315 in molecule.smi (4275/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4174963 in molecule.smi (4276/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4167757 in molecule.smi (4277/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4159837 in molecule.smi (4278/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4164387 in molecule.smi (4279/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4165897 in molecule.smi (4280/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4173815 in molecule.smi (4281/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4170372 in molecule.smi (4283/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4162452 in molecule.smi (4282/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4169326 in molecule.smi (4285/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162043 in molecule.smi (4284/4695). Average speed: 0.25 s/mol.\n", - "Processing CHEMBL4177164 in molecule.smi (4286/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168790 in molecule.smi (4288/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160856 in molecule.smi (4287/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167958 in molecule.smi (4289/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL469638 in molecule.smi (4290/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL586091 in molecule.smi (4291/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1172893 in molecule.smi (4292/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172271 in molecule.smi (4293/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4163781 in molecule.smi (4295/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172720 in molecule.smi (4294/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175914 in molecule.smi (4297/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165299 in molecule.smi (4296/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172510 in molecule.smi (4299/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4161860 in molecule.smi (4298/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168710 in molecule.smi (4301/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164186 in molecule.smi (4300/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164524 in molecule.smi (4303/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160790 in molecule.smi (4302/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168617 in molecule.smi (4305/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173258 in molecule.smi (4304/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL462946 in molecule.smi (4307/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160694 in molecule.smi (4306/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172401 in molecule.smi (4309/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL553245 in molecule.smi (4308/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1171719 in molecule.smi (4310/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176125 in molecule.smi (4312/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162070 in molecule.smi (4311/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL2314746 in molecule.smi (4314/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165513 in molecule.smi (4313/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168934 in molecule.smi (4315/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL453019 in molecule.smi (4317/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164418 in molecule.smi (4316/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL465678 in molecule.smi (4319/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173190 in molecule.smi (4318/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4163761 in molecule.smi (4321/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4171637 in molecule.smi (4320/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175494 in molecule.smi (4323/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164862 in molecule.smi (4322/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168299 in molecule.smi (4324/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160394 in molecule.smi (4325/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165471 in molecule.smi (4326/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173389 in molecule.smi (4327/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4170793 in molecule.smi (4328/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160151 in molecule.smi (4329/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167398 in molecule.smi (4330/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175274 in molecule.smi (4331/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4163678 in molecule.smi (4332/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175003 in molecule.smi (4333/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167127 in molecule.smi (4334/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159880 in molecule.smi (4335/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4170530 in molecule.smi (4336/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164981 in molecule.smi (4337/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172883 in molecule.smi (4338/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4166464 in molecule.smi (4339/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4174372 in molecule.smi (4340/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4163016 in molecule.smi (4341/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4170940 in molecule.smi (4342/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173313 in molecule.smi (4343/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162617 in molecule.smi (4344/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4169887 in molecule.smi (4345/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159243 in molecule.smi (4346/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173902 in molecule.smi (4347/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165973 in molecule.smi (4348/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165562 in molecule.smi (4349/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176179 in molecule.smi (4350/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162121 in molecule.smi (4351/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172797 in molecule.smi (4352/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172353 in molecule.smi (4353/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168987 in molecule.smi (4355/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173405 in molecule.smi (4354/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160115 in molecule.smi (4357/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4161065 in molecule.smi (4356/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168037 in molecule.smi (4358/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4166513 in molecule.smi (4360/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4177133 in molecule.smi (4359/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4169938 in molecule.smi (4362/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162015 in molecule.smi (4361/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159705 in molecule.smi (4364/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4170331 in molecule.smi (4363/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4163077 in molecule.smi (4366/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173784 in molecule.smi (4365/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165327 in molecule.smi (4368/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173239 in molecule.smi (4367/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160310 in molecule.smi (4370/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4171568 in molecule.smi (4369/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165631 in molecule.smi (4372/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168225 in molecule.smi (4371/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173562 in molecule.smi (4373/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4174452 in molecule.smi (4374/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4166141 in molecule.smi (4376/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160696 in molecule.smi (4375/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4174056 in molecule.smi (4377/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162692 in molecule.smi (4378/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4170614 in molecule.smi (4379/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4172950 in molecule.smi (4380/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162281 in molecule.smi (4381/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175814 in molecule.smi (4382/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4170921 in molecule.smi (4384/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167954 in molecule.smi (4383/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164078 in molecule.smi (4386/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162993 in molecule.smi (4385/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167524 in molecule.smi (4388/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4174729 in molecule.smi (4387/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159640 in molecule.smi (4390/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159609 in molecule.smi (4389/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175175 in molecule.smi (4392/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167973 in molecule.smi (4391/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4177242 in molecule.smi (4394/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164540 in molecule.smi (4393/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3091687 in molecule.smi (4396/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4169394 in molecule.smi (4395/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167058 in molecule.smi (4397/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4174290 in molecule.smi (4398/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159074 in molecule.smi (4399/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4177075 in molecule.smi (4400/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4162999 in molecule.smi (4401/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173704 in molecule.smi (4402/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4161805 in molecule.smi (4403/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4171947 in molecule.smi (4404/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4161343 in molecule.smi (4405/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4171941 in molecule.smi (4406/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176452 in molecule.smi (4407/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168588 in molecule.smi (4408/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168198 in molecule.smi (4409/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160280 in molecule.smi (4410/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164761 in molecule.smi (4411/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4175384 in molecule.smi (4412/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4166257 in molecule.smi (4413/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176868 in molecule.smi (4414/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4165100 in molecule.smi (4415/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4173000 in molecule.smi (4416/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168520 in molecule.smi (4417/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176380 in molecule.smi (4418/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4171284 in molecule.smi (4419/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4163376 in molecule.smi (4420/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4166003 in molecule.smi (4421/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176610 in molecule.smi (4422/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4171520 in molecule.smi (4423/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4160908 in molecule.smi (4424/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4168179 in molecule.smi (4425/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4176034 in molecule.smi (4426/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4159832 in molecule.smi (4427/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4167753 in molecule.smi (4428/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4174958 in molecule.smi (4429/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4164310 in molecule.smi (4430/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4207758 in molecule.smi (4431/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4207230 in molecule.smi (4432/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4212110 in molecule.smi (4433/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4209728 in molecule.smi (4434/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214643 in molecule.smi (4435/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213792 in molecule.smi (4436/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4208866 in molecule.smi (4437/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4218191 in molecule.smi (4438/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213253 in molecule.smi (4439/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205954 in molecule.smi (4440/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217663 in molecule.smi (4441/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210316 in molecule.smi (4442/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205425 in molecule.smi (4443/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214707 in molecule.smi (4444/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4209803 in molecule.smi (4445/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4209518 in molecule.smi (4446/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214430 in molecule.smi (4447/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205144 in molecule.smi (4448/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210041 in molecule.smi (4449/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4204315 in molecule.smi (4450/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4208641 in molecule.smi (4451/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213042 in molecule.smi (4452/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217466 in molecule.smi (4453/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205764 in molecule.smi (4454/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4206041 in molecule.smi (4455/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217755 in molecule.smi (4456/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210401 in molecule.smi (4457/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205520 in molecule.smi (4458/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213874 in molecule.smi (4459/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4208961 in molecule.smi (4460/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4218281 in molecule.smi (4461/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213345 in molecule.smi (4462/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205110 in molecule.smi (4463/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4216807 in molecule.smi (4464/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4218997 in molecule.smi (4465/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4208392 in molecule.smi (4466/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4207876 in molecule.smi (4467/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214951 in molecule.smi (4468/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4212229 in molecule.smi (4469/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217212 in molecule.smi (4470/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214755 in molecule.smi (4471/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4203538 in molecule.smi (4472/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4203653 in molecule.smi (4473/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4216806 in molecule.smi (4474/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL3891025 in molecule.smi (4475/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4202749 in molecule.smi (4476/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4218829 in molecule.smi (4477/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4207101 in molecule.smi (4478/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205651 in molecule.smi (4479/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210517 in molecule.smi (4480/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210020 in molecule.smi (4481/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214932 in molecule.smi (4482/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4212275 in molecule.smi (4483/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4207386 in molecule.smi (4484/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217666 in molecule.smi (4485/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4212713 in molecule.smi (4486/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205426 in molecule.smi (4487/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217139 in molecule.smi (4488/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4208869 in molecule.smi (4489/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4203988 in molecule.smi (4490/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217746 in molecule.smi (4491/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4208322 in molecule.smi (4492/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213147 in molecule.smi (4493/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4209420 in molecule.smi (4494/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4209795 in molecule.smi (4495/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4216066 in molecule.smi (4496/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4203672 in molecule.smi (4497/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4213591 in molecule.smi (4498/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217346 in molecule.smi (4499/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217660 in molecule.smi (4500/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4215154 in molecule.smi (4501/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210729 in molecule.smi (4502/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4215217 in molecule.smi (4503/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4204901 in molecule.smi (4504/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214900 in molecule.smi (4505/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4206484 in molecule.smi (4506/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4202784 in molecule.smi (4507/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4209755 in molecule.smi (4508/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214668 in molecule.smi (4509/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205374 in molecule.smi (4510/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210274 in molecule.smi (4511/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217617 in molecule.smi (4512/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4205910 in molecule.smi (4513/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4204913 in molecule.smi (4514/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4216601 in molecule.smi (4515/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4217126 in molecule.smi (4516/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4212151 in molecule.smi (4517/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL608112 in molecule.smi (4518/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4216666 in molecule.smi (4519/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL77510 in molecule.smi (4520/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4210949 in molecule.smi (4521/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4214417 in molecule.smi (4522/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL709 in molecule.smi (4523/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1201217 in molecule.smi (4524/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL623 in molecule.smi (4525/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4241840 in molecule.smi (4526/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4244803 in molecule.smi (4528/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4243474 in molecule.smi (4527/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4240861 in molecule.smi (4530/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4242359 in molecule.smi (4529/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4238937 in molecule.smi (4532/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4250314 in molecule.smi (4531/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4250143 in molecule.smi (4534/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4238512 in molecule.smi (4533/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4246032 in molecule.smi (4536/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4250287 in molecule.smi (4535/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4240365 in molecule.smi (4538/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4249829 in molecule.smi (4537/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4250510 in molecule.smi (4540/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4244573 in molecule.smi (4539/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4241100 in molecule.smi (4542/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4248378 in molecule.smi (4541/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4244773 in molecule.smi (4544/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4250950 in molecule.smi (4543/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285818 in molecule.smi (4546/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4294109 in molecule.smi (4545/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4282414 in molecule.smi (4548/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4277879 in molecule.smi (4547/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283651 in molecule.smi (4550/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4293015 in molecule.smi (4549/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285581 in molecule.smi (4552/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4278287 in molecule.smi (4551/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4282154 in molecule.smi (4554/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4293418 in molecule.smi (4553/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4278686 in molecule.smi (4556/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4290039 in molecule.smi (4555/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4286601 in molecule.smi (4557/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4278164 in molecule.smi (4558/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4288850 in molecule.smi (4559/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4293305 in molecule.smi (4560/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4282502 in molecule.smi (4562/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285481 in molecule.smi (4561/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283621 in molecule.smi (4563/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4289362 in molecule.smi (4564/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4287748 in molecule.smi (4565/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283298 in molecule.smi (4566/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4293086 in molecule.smi (4567/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4281409 in molecule.smi (4568/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4280241 in molecule.smi (4569/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4294640 in molecule.smi (4571/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4290877 in molecule.smi (4570/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4281440 in molecule.smi (4573/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4284047 in molecule.smi (4572/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4280612 in molecule.smi (4574/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283770 in molecule.smi (4575/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4277095 in molecule.smi (4576/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285471 in molecule.smi (4577/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4280935 in molecule.smi (4578/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4291591 in molecule.smi (4579/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283733 in molecule.smi (4580/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4288571 in molecule.smi (4581/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4281894 in molecule.smi (4582/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4289104 in molecule.smi (4583/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4278434 in molecule.smi (4584/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL1383041 in molecule.smi (4585/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4292192 in molecule.smi (4586/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4276675 in molecule.smi (4587/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4225157 in molecule.smi (4588/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4287962 in molecule.smi (4589/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4284580 in molecule.smi (4590/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4294389 in molecule.smi (4591/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283814 in molecule.smi (4592/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4279249 in molecule.smi (4593/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283463 in molecule.smi (4594/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4294028 in molecule.smi (4595/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4286806 in molecule.smi (4596/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4278883 in molecule.smi (4597/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285749 in molecule.smi (4598/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4294859 in molecule.smi (4599/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4284235 in molecule.smi (4600/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4286556 in molecule.smi (4601/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4294446 in molecule.smi (4602/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4283189 in molecule.smi (4603/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4291046 in molecule.smi (4604/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4293364 in molecule.smi (4605/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4282755 in molecule.smi (4606/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4289981 in molecule.smi (4607/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4279297 in molecule.smi (4608/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4289908 in molecule.smi (4609/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4282037 in molecule.smi (4610/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4287595 in molecule.smi (4611/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4292235 in molecule.smi (4612/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285714 in molecule.smi (4613/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4293179 in molecule.smi (4614/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4285972 in molecule.smi (4615/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4278046 in molecule.smi (4616/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4284506 in molecule.smi (4617/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4295165 in molecule.smi (4618/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4288000 in molecule.smi (4619/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4277357 in molecule.smi (4620/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4291400 in molecule.smi (4621/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4280753 in molecule.smi (4622/4695). Average speed: 0.24 s/mol.\n", - "Processing CHEMBL4281203 in molecule.smi (4623/4695). 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Average speed: 0.24 s/mol.\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "2p7rAVy_k_hH", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 170 - }, - "outputId": "fbd4553a-a474-49bd-afa2-625982aaf1a8" - }, - "source": [ - "! ls -l" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "text": [ - "total 34032\n", - "-rw-r--r-- 1 root root 385924 Jun 9 14:28 acetylcholinesterase_03_bioactivity_data_curated.csv\n", - "-rw-r--r-- 1 root root 8356701 Jun 9 14:49 descriptors_output.csv\n", - "drwxr-xr-x 3 root root 4096 Jun 9 14:27 __MACOSX\n", - "-rw-r--r-- 1 root root 310658 Jun 9 14:29 molecule.smi\n", - "drwxrwxr-x 4 root root 4096 May 30 10:10 PaDEL-Descriptor\n", - "-rw-r--r-- 1 root root 231 Jun 9 14:27 padel.sh\n", - "-rw-r--r-- 1 root root 25768637 Jun 9 14:27 padel.zip\n", - "drwxr-xr-x 1 root root 4096 May 29 18:19 sample_data\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gUMlPfFrxicj", - "colab_type": "text" - }, - "source": [ - "## **Preparing the X and Y Data Matrices**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "30aa4WP4ZA8M", - "colab_type": "text" - }, - "source": [ - "### **X data matrix**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "3g319qxVl7tY", - "colab_type": "code", - "colab": {} - }, - "source": [ - "df3_X = pd.read_csv('descriptors_output.csv')" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "hBp1PTObFQDd", - "colab_type": "code", - "outputId": "70f5dbde-55dc-446f-90e0-6057dcf0ad20", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 439 - } - }, - "source": [ - "df3_X" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " PubchemFP0 PubchemFP1 ... PubchemFP879 PubchemFP880\n", - "0 1 1 ... 0 0\n", - "1 1 1 ... 0 0\n", - "2 1 1 ... 0 0\n", - "3 1 1 ... 0 0\n", - "4 1 1 ... 0 0\n", - "... ... ... ... ... ...\n", - "4690 1 1 ... 0 0\n", - "4691 1 1 ... 0 0\n", - "4692 1 1 ... 0 0\n", - "4693 1 1 ... 0 0\n", - "4694 1 1 ... 0 0\n", - "\n", - "[4695 rows x 881 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6ePu814KZNBG", - "colab_type": "text" - }, - "source": [ - "## **Y variable**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9ssfxFCdr7ua", - "colab_type": "text" - }, - "source": [ - "### **Convert IC50 to pIC50**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "FlhsSEW5FXTG", - "colab_type": "code", - "outputId": "635522b9-62f9-4546-cfdf-6aa66d4094ad", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 221 - } - }, - "source": [ - "df3_Y = df3['pIC50']\n", - "df3_Y" - ], - "execution_count": 31, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 6.124939\n", - "1 7.000000\n", - "2 4.301030\n", - "3 6.522879\n", - "4 6.096910\n", - " ... \n", - "4690 5.612610\n", - "4691 5.595166\n", - "4692 5.419075\n", - "4693 5.460924\n", - "4694 5.555955\n", - "Name: pIC50, Length: 4695, dtype: float64" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 31 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eET6iZ1Aw3oe", - "colab_type": "text" - }, - "source": [ - "## **Combining X and Y variable**" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "FRBfBP3QxFJp", - "colab_type": "code", - "outputId": "2a6a3dba-ff09-4cd4-84db-885164ac817e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 439 - } - }, - "source": [ - "dataset3 = pd.concat([df3_X,df3_Y], axis=1)\n", - "dataset3" - ], - "execution_count": 32, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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PubchemFP879 PubchemFP880 pIC50\n", - "0 1 1 1 ... 0 0 6.124939\n", - "1 1 1 1 ... 0 0 7.000000\n", - "2 1 1 1 ... 0 0 4.301030\n", - "3 1 1 0 ... 0 0 6.522879\n", - "4 1 1 0 ... 0 0 6.096910\n", - "... ... ... ... ... ... ... ...\n", - "4690 1 1 1 ... 0 0 5.612610\n", - "4691 1 1 1 ... 0 0 5.595166\n", - "4692 1 1 1 ... 0 0 5.419075\n", - "4693 1 1 1 ... 0 0 5.460924\n", - "4694 1 1 1 ... 0 0 5.555955\n", - "\n", - "[4695 rows x 882 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 32 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "75npGyvhae0e", - "colab_type": "code", - "colab": {} - }, - "source": [ - "dataset3.to_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_aromatase_pubchem_fp.csv', index=False)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nFpLoNRHeRa6", - "colab_type": "text" - }, - "source": [ - "# **Let's download the CSV file to your local computer for the Part 3B (Model Building).**" - ] - } - ] -} \ No newline at end of file From 5bb4b1140be3c100005e65279264dda8c6e9060c Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 10 Jun 2020 00:25:09 +0700 Subject: [PATCH 09/73] Add files via upload --- ...erase_Descriptor_Dataset_Preparation.ipynb | 4656 +++++++++++++++++ 1 file changed, 4656 insertions(+) create mode 100644 python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb diff --git a/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb b/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb new file mode 100644 index 0000000..4e464d0 --- /dev/null +++ b/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb @@ -0,0 +1,4656 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "CDD-ML-Part-3-Acetylcholinesterase-Descriptor-Dataset-Preparation.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "l0Y7_lgN4jzM", + "colab_type": "text" + }, + "source": [ + "# **Bioinformatics Project - Computational Drug Discovery [Part 3] Descriptor Calculation and Dataset Preparation**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", + "\n", + "In this Jupyter notebook, we will be building a real-life **data science project** that you can include in your **data science portfolio**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", + "\n", + "In **Part 3**, we will be calculating molecular descriptors that are essentially quantitative description of the compounds in the dataset. Finally, we will be preparing this into a dataset for subsequent model building in Part 4.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o-4IOizard4P", + "colab_type": "text" + }, + "source": [ + "## **Download PaDEL-Descriptor**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H0mjQ2PcrSe5", + "colab_type": "code", + "outputId": "7cd24f88-a9b5-445e-90fc-a2bde1f2aef8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 572 + } + }, + "source": [ + "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n", + "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-06-09 16:58:49-- https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n", + "Resolving github.com (github.com)... 140.82.113.3\n", + "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip [following]\n", + "--2020-06-09 16:58:50-- https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 25768637 (25M) [application/zip]\n", + "Saving to: ‘padel.zip’\n", + "\n", + "padel.zip 100%[===================>] 24.57M 40.2MB/s in 0.6s \n", + "\n", + "2020-06-09 16:58:51 (40.2 MB/s) - ‘padel.zip’ saved [25768637/25768637]\n", + "\n", + "--2020-06-09 16:58:52-- https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh\n", + "Resolving github.com (github.com)... 140.82.113.3\n", + "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh [following]\n", + "--2020-06-09 16:58:52-- https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 231 [text/plain]\n", + "Saving to: ‘padel.sh’\n", + "\n", + "padel.sh 100%[===================>] 231 --.-KB/s in 0s \n", + "\n", + "2020-06-09 16:58:52 (11.1 MB/s) - ‘padel.sh’ saved [231/231]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "64HnTL4tS-nA", + "colab_type": "code", + "outputId": "b082c979-db9f-48dd-8050-0462b6cf4979", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "! unzip padel.zip" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Archive: padel.zip\n", + " creating: PaDEL-Descriptor/\n", + " inflating: __MACOSX/._PaDEL-Descriptor \n", + " inflating: PaDEL-Descriptor/MACCSFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._MACCSFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/AtomPairs2DFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._AtomPairs2DFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/EStateFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._EStateFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/Fingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._Fingerprinter.xml \n", + " inflating: PaDEL-Descriptor/.DS_Store \n", + " inflating: __MACOSX/PaDEL-Descriptor/._.DS_Store \n", + " creating: PaDEL-Descriptor/license/\n", + " inflating: __MACOSX/PaDEL-Descriptor/._license \n", + " inflating: PaDEL-Descriptor/KlekotaRothFingerprintCount.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._KlekotaRothFingerprintCount.xml \n", + " inflating: PaDEL-Descriptor/config \n", + " inflating: __MACOSX/PaDEL-Descriptor/._config \n", + " inflating: PaDEL-Descriptor/PubchemFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._PubchemFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/ExtendedFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._ExtendedFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/KlekotaRothFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._KlekotaRothFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/GraphOnlyFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._GraphOnlyFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/SubstructureFingerprinter.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._SubstructureFingerprinter.xml \n", + " inflating: PaDEL-Descriptor/Descriptors.xls \n", + " inflating: __MACOSX/PaDEL-Descriptor/._Descriptors.xls \n", + " creating: PaDEL-Descriptor/lib/\n", + " inflating: __MACOSX/PaDEL-Descriptor/._lib \n", + " inflating: PaDEL-Descriptor/PaDEL-Descriptor.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/._PaDEL-Descriptor.jar \n", + " inflating: PaDEL-Descriptor/SubstructureFingerprintCount.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._SubstructureFingerprintCount.xml \n", + " inflating: PaDEL-Descriptor/AtomPairs2DFingerprintCount.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._AtomPairs2DFingerprintCount.xml \n", + " inflating: PaDEL-Descriptor/descriptors.xml \n", + " inflating: __MACOSX/PaDEL-Descriptor/._descriptors.xml \n", + " inflating: PaDEL-Descriptor/license/lgpl-2.1.txt \n", + " inflating: __MACOSX/PaDEL-Descriptor/license/._lgpl-2.1.txt \n", + " inflating: PaDEL-Descriptor/license/LICENSE.txt \n", + " inflating: __MACOSX/PaDEL-Descriptor/license/._LICENSE.txt \n", + " inflating: PaDEL-Descriptor/license/README - CDK \n", + " inflating: __MACOSX/PaDEL-Descriptor/license/._README - CDK \n", + " inflating: PaDEL-Descriptor/license/lgpl.license \n", + " inflating: __MACOSX/PaDEL-Descriptor/license/._lgpl.license \n", + " inflating: PaDEL-Descriptor/lib/ambit2-core-2.4.7-SNAPSHOT(3).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-core-2.4.7-SNAPSHOT(3).jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL-Jobs(6).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Jobs(6).jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL.jar \n", + " inflating: PaDEL-Descriptor/lib/jgrapht-0.6.0(4).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jgrapht-0.6.0(4).jar \n", + " inflating: PaDEL-Descriptor/lib/commons-cli-1.2(2).jar \n", + " inflating: 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PaDEL-Descriptor/lib/vecmath1.2-1.14.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._vecmath1.2-1.14.jar \n", + " inflating: PaDEL-Descriptor/lib/ambit2-smarts-2.4.7-SNAPSHOT(6).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-smarts-2.4.7-SNAPSHOT(6).jar \n", + " inflating: PaDEL-Descriptor/lib/ambit2-core-2.4.7-SNAPSHOT(2).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-core-2.4.7-SNAPSHOT(2).jar \n", + " inflating: PaDEL-Descriptor/lib/jama(6).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jama(6).jar \n", + " inflating: PaDEL-Descriptor/lib/jgrapht-0.6.0(2).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jgrapht-0.6.0(2).jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL-Descriptor(3).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Descriptor(3).jar \n", + " inflating: PaDEL-Descriptor/lib/commons-cli-1.2(4).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._commons-cli-1.2(4).jar \n", + " inflating: 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inflating: PaDEL-Descriptor/lib/jama(2).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jama(2).jar \n", + " inflating: PaDEL-Descriptor/lib/jama(3).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._jama(3).jar \n", + " inflating: PaDEL-Descriptor/lib/commons-cli-1.2(1).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._commons-cli-1.2(1).jar \n", + " inflating: PaDEL-Descriptor/lib/guava-17.0.jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._guava-17.0.jar \n", + " inflating: PaDEL-Descriptor/lib/ambit2-smarts-2.4.7-SNAPSHOT(4).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._ambit2-smarts-2.4.7-SNAPSHOT(4).jar \n", + " inflating: PaDEL-Descriptor/lib/libPaDEL-Jobs(5).jar \n", + " inflating: __MACOSX/PaDEL-Descriptor/lib/._libPaDEL-Jobs(5).jar \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QmxXXFa4wTNG", + "colab_type": "text" + }, + "source": [ + "## **Load bioactivity data**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fcBvxkPWKFRV", + "colab_type": "text" + }, + "source": [ + "Download the curated ChEMBL bioactivity data that has been pre-processed from Parts 1 and 2 of this Bioinformatics Project series. Here we will be using the **bioactivity_data_3class_pIC50.csv** file that essentially contain the pIC50 values that we will be using for building a regression model." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JBs7QdJoubWC", + "colab_type": "code", + "outputId": "3d6bfdcd-1eb9-4930-8e43-45d217f7d22b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + } + }, + "source": [ + "! wget https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-06-09 17:00:26-- https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 655414 (640K) [text/plain]\n", + "Saving to: ‘acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv’\n", + "\n", + "\r acetylcho 0%[ ] 0 --.-KB/s \racetylcholinesteras 100%[===================>] 640.05K --.-KB/s in 0.07s \n", + "\n", + "2020-06-09 17:00:26 (9.21 MB/s) - ‘acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv’ saved [655414/655414]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Fpu5C7HlwV9s", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GCcE8J5XwjtB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df3 = pd.read_csv('acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "60z_N6egNiSJ", + "colab_type": "code", + "outputId": "4419acc9-ced7-4a45-b849-665ed5e0fd99", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + } + }, + "source": [ + "df3" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Unnamed: 0molecule_chembl_idcanonical_smilesclassMWLogPNumHDonorsNumHAcceptorspIC50
00CHEMBL133897CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1active312.3252.803200.06.06.124939
11CHEMBL336398O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1active376.9134.554600.05.07.000000
22CHEMBL131588CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1inactive426.8515.357400.05.04.301030
33CHEMBL130628O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)Factive404.8454.706900.05.06.522879
44CHEMBL130478CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)Cactive346.3343.095300.06.06.096910
..............................
46904690CHEMBL4293155CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...intermediate511.1507.072302.03.05.612610
46914691CHEMBL4282558CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...intermediate511.1507.072302.03.05.595166
46924692CHEMBL4281727CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...intermediate555.6017.181402.03.05.419075
46934693CHEMBL4292349CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...intermediate521.7026.327102.05.05.460924
46944694CHEMBL4278260CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...intermediate501.7156.290582.04.05.555955
\n", + "

4695 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 molecule_chembl_id ... NumHAcceptors pIC50\n", + "0 0 CHEMBL133897 ... 6.0 6.124939\n", + "1 1 CHEMBL336398 ... 5.0 7.000000\n", + "2 2 CHEMBL131588 ... 5.0 4.301030\n", + "3 3 CHEMBL130628 ... 5.0 6.522879\n", + "4 4 CHEMBL130478 ... 6.0 6.096910\n", + "... ... ... ... ... ...\n", + "4690 4690 CHEMBL4293155 ... 3.0 5.612610\n", + "4691 4691 CHEMBL4282558 ... 3.0 5.595166\n", + "4692 4692 CHEMBL4281727 ... 3.0 5.419075\n", + "4693 4693 CHEMBL4292349 ... 5.0 5.460924\n", + "4694 4694 CHEMBL4278260 ... 4.0 5.555955\n", + "\n", + "[4695 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BJDJkY43R-9F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "selection = ['canonical_smiles','molecule_chembl_id']\n", + "df3_selection = df3[selection]\n", + "df3_selection.to_csv('molecule.smi', sep='\\t', index=False, header=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nRSCoPVDSkf5", + "colab_type": "code", + "outputId": "2be43c1d-d8b8-42a8-93fc-4c1b29855a8a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104 + } + }, + "source": [ + "! cat molecule.smi | head -5" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1\tCHEMBL133897\n", + "O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1\tCHEMBL336398\n", + "CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1\tCHEMBL131588\n", + "O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F\tCHEMBL130628\n", + "CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C\tCHEMBL130478\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GlYaJ9pzUGjS", + "colab_type": "code", + "outputId": "6dab2be5-172a-4efb-8b42-7ceef402bfa3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "! cat molecule.smi | wc -l" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "4695\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YzN_S4Quro5S", + "colab_type": "text" + }, + "source": [ + "## **Calculate fingerprint descriptors**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JsgTV-ByxdMa", + "colab_type": "text" + }, + "source": [ + "### **Calculate PaDEL descriptors**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hSCopQvEiSMj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 55 + }, + "outputId": "77fe3614-69ef-4bd1-9dca-2a9b2c975fbb" + }, + "source": [ + "! cat padel.sh" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "java -Xms1G -Xmx1G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -fingerprints -descriptortypes ./PaDEL-Descriptor/PubchemFingerprinter.xml -dir ./ -file descriptors_output.csv\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6kN9jrGpS5nE", + "colab_type": "code", + "outputId": "456a4161-b3dd-44b9-bc82-2523976f25ea", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "! bash padel.sh" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Processing CHEMBL133897 in molecule.smi (1/4695). \n", + "Processing CHEMBL336398 in molecule.smi (2/4695). \n", + "Processing CHEMBL130628 in molecule.smi (4/4695). Average speed: 2.98 s/mol.\n", + "Processing CHEMBL131588 in molecule.smi (3/4695). Average speed: 5.85 s/mol.\n", + "Processing CHEMBL130478 in molecule.smi (5/4695). Average speed: 2.15 s/mol.\n", + "Processing CHEMBL130098 in molecule.smi (7/4695). Average speed: 1.40 s/mol.\n", + "Processing CHEMBL130112 in molecule.smi (6/4695). Average speed: 1.71 s/mol.\n", + "Processing CHEMBL337486 in molecule.smi (8/4695). Average speed: 1.25 s/mol.\n", + "Processing CHEMBL336538 in molecule.smi (9/4695). Average speed: 1.08 s/mol.\n", + "Processing CHEMBL131051 in molecule.smi (10/4695). Average speed: 1.01 s/mol.\n", + "Processing CHEMBL341437 in molecule.smi (11/4695). Average speed: 0.93 s/mol.\n", + "Processing CHEMBL335033 in molecule.smi (12/4695). Average speed: 0.88 s/mol.\n", + "Processing CHEMBL122983 in molecule.smi (13/4695). Average speed: 0.90 s/mol.\n", + "Processing CHEMBL338720 in molecule.smi (14/4695). Average speed: 0.80 s/mol.\n", + "Processing CHEMBL339995 in molecule.smi (15/4695). Average speed: 0.79 s/mol.\n", + "Processing CHEMBL131536 in molecule.smi (17/4695). Average speed: 0.71 s/mol.\n", + "Processing CHEMBL335158 in molecule.smi (16/4695). Average speed: 0.75 s/mol.\n", + "Processing CHEMBL106126 in molecule.smi (18/4695). Average speed: 0.70 s/mol.\n", + "Processing CHEMBL336625 in molecule.smi (20/4695). Average speed: 0.64 s/mol.\n", + "Processing CHEMBL334971 in molecule.smi (19/4695). Average speed: 0.68 s/mol.\n", + "Processing CHEMBL130666 in molecule.smi (21/4695). Average speed: 0.66 s/mol.\n", + "Processing CHEMBL134061 in molecule.smi (22/4695). Average speed: 0.63 s/mol.\n", + "Processing CHEMBL133388 in molecule.smi (23/4695). Average speed: 0.62 s/mol.\n", + "Processing CHEMBL130645 in molecule.smi (24/4695). Average speed: 0.60 s/mol.\n", + "Processing CHEMBL133580 in molecule.smi (25/4695). 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Average speed: 0.43 s/mol.\n", + "Processing CHEMBL1203537 in molecule.smi (49/4695). Average speed: 0.41 s/mol.\n", + "Processing CHEMBL154211 in molecule.smi (48/4695). Average speed: 0.42 s/mol.\n", + "Processing CHEMBL86 in molecule.smi (50/4695). Average speed: 0.41 s/mol.\n", + "Processing CHEMBL47375 in molecule.smi (51/4695). Average speed: 0.41 s/mol.\n", + "Processing CHEMBL542360 in molecule.smi (52/4695). Average speed: 0.40 s/mol.\n", + "Processing CHEMBL345849 in molecule.smi (53/4695). Average speed: 0.41 s/mol.\n", + "Processing CHEMBL154689 in molecule.smi (54/4695). Average speed: 0.39 s/mol.\n", + "Processing CHEMBL346733 in molecule.smi (55/4695). Average speed: 0.39 s/mol.\n", + "Processing CHEMBL416464 in molecule.smi (56/4695). Average speed: 0.39 s/mol.\n", + "Processing CHEMBL1203539 in molecule.smi (57/4695). Average speed: 0.38 s/mol.\n", + "Processing CHEMBL347729 in molecule.smi (58/4695). 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Average speed: 0.29 s/mol.\n", + "Processing CHEMBL29300 in molecule.smi (115/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL50608 in molecule.smi (114/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL27675 in molecule.smi (116/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL60167 in molecule.smi (118/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL435373 in molecule.smi (117/4695). Average speed: 0.29 s/mol.\n", + "Processing CHEMBL314800 in molecule.smi (120/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL89012 in molecule.smi (119/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL553212 in molecule.smi (121/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL156630 in molecule.smi (122/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL315013 in molecule.smi (123/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL155754 in molecule.smi (124/4695). 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Average speed: 0.28 s/mol.\n", + "Processing CHEMBL314502 in molecule.smi (136/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87320 in molecule.smi (137/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87280 in molecule.smi (138/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL348607 in molecule.smi (139/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL313809 in molecule.smi (140/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87849 in molecule.smi (141/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL314398 in molecule.smi (142/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87373 in molecule.smi (143/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL156931 in molecule.smi (144/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL83005 in molecule.smi (145/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL315515 in molecule.smi (146/4695). 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Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87980 in molecule.smi (158/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87441 in molecule.smi (159/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL158946 in molecule.smi (160/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL87550 in molecule.smi (161/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL609440 in molecule.smi (162/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL313271 in molecule.smi (164/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL350464 in molecule.smi (163/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL423313 in molecule.smi (165/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL309667 in molecule.smi (166/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL419470 in molecule.smi (167/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL80221 in molecule.smi (168/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL431844 in molecule.smi (169/4695). Average speed: 0.28 s/mol.\n", + "Processing CHEMBL80247 in molecule.smi (170/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL77896 in molecule.smi (172/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL418917 in molecule.smi (171/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL369661 in molecule.smi (173/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL74089 in molecule.smi (175/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL502 in molecule.smi (174/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL134057 in molecule.smi (176/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL76640 in molecule.smi (177/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL76241 in molecule.smi (178/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL431784 in molecule.smi (179/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL352500 in molecule.smi (181/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL51934 in molecule.smi (180/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL355740 in molecule.smi (183/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL292314 in molecule.smi (182/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL75919 in molecule.smi (185/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL54727 in molecule.smi (184/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL309351 in molecule.smi (186/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL75743 in molecule.smi (187/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL76135 in molecule.smi (188/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL440497 in molecule.smi (189/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL368196 in molecule.smi (190/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL62084 in molecule.smi (191/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL172118 in molecule.smi (192/4695). Average speed: 0.27 s/mol.\n", + "Processing CHEMBL1202875 in molecule.smi (193/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL436367 in molecule.smi (194/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL132365 in molecule.smi (195/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL3215535 in molecule.smi (196/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL1202865 in molecule.smi (197/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL133520 in molecule.smi (198/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL1202884 in molecule.smi (200/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL130373 in molecule.smi (199/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL263198 in molecule.smi (201/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL133906 in molecule.smi (202/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL134240 in molecule.smi (204/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL1204162 in molecule.smi (203/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL132030 in molecule.smi (205/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL133448 in molecule.smi (206/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL130880 in molecule.smi (207/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL1202874 in molecule.smi (208/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL133441 in molecule.smi (209/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL130427 in molecule.smi (210/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL132260 in molecule.smi (212/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL134154 in molecule.smi (211/4695). Average speed: 0.26 s/mol.\n", + "Processing CHEMBL1204296 in molecule.smi (214/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1204299 in molecule.smi (213/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL1202885 in molecule.smi (215/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL337453 in molecule.smi (216/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL335922 in molecule.smi (217/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL132619 in molecule.smi (218/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL61515 in molecule.smi (220/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL293316 in molecule.smi (219/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL58034 in molecule.smi (222/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL442361 in molecule.smi (221/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL61965 in molecule.smi (223/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL62210 in molecule.smi (225/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL59790 in molecule.smi (224/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL57003 in molecule.smi (226/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL291783 in molecule.smi (227/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL294498 in molecule.smi (228/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL300170 in molecule.smi (229/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL61312 in molecule.smi (230/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL293268 in molecule.smi (231/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL293751 in molecule.smi (232/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL59192 in molecule.smi (233/4695). Average speed: 0.25 s/mol.\n", + "Processing CHEMBL300176 in molecule.smi (234/4695). 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Average speed: 0.23 s/mol.\n", + "Processing CHEMBL345905 in molecule.smi (301/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL128172 in molecule.smi (302/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL157017 in molecule.smi (303/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL155128 in molecule.smi (304/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL345847 in molecule.smi (305/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL158252 in molecule.smi (306/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL156874 in molecule.smi (307/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL341251 in molecule.smi (308/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL445124 in molecule.smi (309/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92142 in molecule.smi (310/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL130956 in molecule.smi (312/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL553187 in molecule.smi (311/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL93936 in molecule.smi (313/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL335167 in molecule.smi (314/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL130270 in molecule.smi (316/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92629 in molecule.smi (315/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL330004 in molecule.smi (318/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL340867 in molecule.smi (317/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL93123 in molecule.smi (319/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL420625 in molecule.smi (320/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92460 in molecule.smi (321/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL340391 in molecule.smi (322/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92955 in molecule.smi (323/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL131210 in molecule.smi (324/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL329531 in molecule.smi (326/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL126939 in molecule.smi (325/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92663 in molecule.smi (328/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL418955 in molecule.smi (327/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL543711 in molecule.smi (330/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL131598 in molecule.smi (329/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL335962 in molecule.smi (331/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL131825 in molecule.smi (332/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL93619 in molecule.smi (333/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL134251 in molecule.smi (334/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL337675 in molecule.smi (335/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL94186 in molecule.smi (336/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92463 in molecule.smi (337/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL130363 in molecule.smi (339/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL335220 in molecule.smi (338/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL130672 in molecule.smi (341/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL92958 in molecule.smi (340/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL132531 in molecule.smi (343/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL94059 in molecule.smi (342/4695). Average speed: 0.23 s/mol.\n", + "Processing CHEMBL433379 in molecule.smi (345/4695). 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Average speed: 0.22 s/mol.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2p7rAVy_k_hH", + "colab_type": "code", + "outputId": "7404029f-4d69-486c-97a2-faf6d4574971", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + } + }, + "source": [ + "! ls -l" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "total 34296\n", + "-rw-r--r-- 1 root root 655414 Jun 9 17:00 acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv\n", + "-rw-r--r-- 1 root root 8356701 Jun 9 17:08 descriptors_output.csv\n", + "drwxr-xr-x 3 root root 4096 Jun 9 17:00 __MACOSX\n", + "-rw-r--r-- 1 root root 310658 Jun 9 17:01 molecule.smi\n", + "drwxrwxr-x 4 root root 4096 May 30 10:10 PaDEL-Descriptor\n", + "-rw-r--r-- 1 root root 231 Jun 9 16:58 padel.sh\n", + "-rw-r--r-- 1 root root 25768637 Jun 9 16:58 padel.zip\n", + "drwxr-xr-x 1 root root 4096 May 29 18:19 sample_data\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gUMlPfFrxicj", + "colab_type": "text" + }, + "source": [ + "## **Preparing the X and Y Data Matrices**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "30aa4WP4ZA8M", + "colab_type": "text" + }, + "source": [ + "### **X data matrix**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3g319qxVl7tY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df3_X = pd.read_csv('descriptors_output.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hBp1PTObFQDd", + "colab_type": "code", + "outputId": "26a4603a-d1b2-4d64-e1c8-cf5fd0612bbf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + } + }, + "source": [ + "df3_X" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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4695 rows × 881 columns

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" + ], + "text/plain": [ + " PubchemFP0 PubchemFP1 ... PubchemFP879 PubchemFP880\n", + "0 1 1 ... 0 0\n", + "1 1 1 ... 0 0\n", + "2 1 1 ... 0 0\n", + "3 1 1 ... 0 0\n", + "4 1 1 ... 0 0\n", + "... ... ... ... ... ...\n", + "4690 1 1 ... 0 0\n", + "4691 1 1 ... 0 0\n", + "4692 1 1 ... 0 0\n", + "4693 1 1 ... 0 0\n", + "4694 1 1 ... 0 0\n", + "\n", + "[4695 rows x 881 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6ePu814KZNBG", + "colab_type": "text" + }, + "source": [ + "## **Y variable**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9ssfxFCdr7ua", + "colab_type": "text" + }, + "source": [ + "### **Convert IC50 to pIC50**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FlhsSEW5FXTG", + "colab_type": "code", + "outputId": "ef224d6b-4cf3-4bde-fe83-d2e43f6e246c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + } + }, + "source": [ + "df3_Y = df3['pIC50']\n", + "df3_Y" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 6.124939\n", + "1 7.000000\n", + "2 4.301030\n", + "3 6.522879\n", + "4 6.096910\n", + " ... \n", + "4690 5.612610\n", + "4691 5.595166\n", + "4692 5.419075\n", + "4693 5.460924\n", + "4694 5.555955\n", + "Name: pIC50, Length: 4695, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eET6iZ1Aw3oe", + "colab_type": "text" + }, + "source": [ + "## **Combining X and Y variable**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FRBfBP3QxFJp", + "colab_type": "code", + "outputId": "ee900c26-ff5c-4e20-a3fb-7b1f9367db66", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + } + }, + "source": [ + "dataset3 = pd.concat([df3_X,df3_Y], axis=1)\n", + "dataset3" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PubchemFP0 PubchemFP1 PubchemFP2 ... PubchemFP879 PubchemFP880 pIC50\n", + "0 1 1 1 ... 0 0 6.124939\n", + "1 1 1 1 ... 0 0 7.000000\n", + "2 1 1 1 ... 0 0 4.301030\n", + "3 1 1 0 ... 0 0 6.522879\n", + "4 1 1 0 ... 0 0 6.096910\n", + "... ... ... ... ... ... ... ...\n", + "4690 1 1 1 ... 0 0 5.612610\n", + "4691 1 1 1 ... 0 0 5.595166\n", + "4692 1 1 1 ... 0 0 5.419075\n", + "4693 1 1 1 ... 0 0 5.460924\n", + "4694 1 1 1 ... 0 0 5.555955\n", + "\n", + "[4695 rows x 882 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "75npGyvhae0e", + "colab_type": "code", + "colab": {} + }, + "source": [ + "dataset3.to_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv', index=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nFpLoNRHeRa6", + "colab_type": "text" + }, + "source": [ + "# **Let's download the CSV file to your local computer for the Part 3B (Model Building).**" + ] + } + ] +} \ No newline at end of file From a9dfda10f0e8cf8c60c5baed4bba10eff2ae66c8 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Fri, 12 Jun 2020 14:40:46 +0700 Subject: [PATCH 10/73] Add files via upload --- streamlit/iris-ml-app.py | 49 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 streamlit/iris-ml-app.py diff --git a/streamlit/iris-ml-app.py b/streamlit/iris-ml-app.py new file mode 100644 index 0000000..14739dc --- /dev/null +++ b/streamlit/iris-ml-app.py @@ -0,0 +1,49 @@ +import streamlit as st +import pandas as pd +from sklearn import datasets +from sklearn.ensemble import RandomForestClassifier + +st.write(""" +# Simple Iris Flower Prediction App + +This app predicts the **Iris flower** type! +""") + +st.sidebar.header('User Input Parameters') + +def user_input_features(): + sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4) + sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4) + petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3) + petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2) + data = {'sepal_length': sepal_length, + 'sepal_width': sepal_width, + 'petal_length': petal_length, + 'petal_width': petal_width} + features = pd.DataFrame(data, index=[0]) + return features + +df = user_input_features() + +st.subheader('User Input parameters') +st.write(df) + +iris = datasets.load_iris() +X = iris.data +Y = iris.target + +clf = RandomForestClassifier() +clf.fit(X, Y) + +prediction = clf.predict(df) +prediction_proba = clf.predict_proba(df) + +st.subheader('Class labels and their corresponding index number') +st.write(iris.target_names) + +st.subheader('Prediction') +st.write(iris.target_names[prediction]) +#st.write(prediction) + +st.subheader('Prediction Probability') +st.write(prediction_proba) From 05857730e40fa93f40fa8578e7df7eb1f0602020 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 13 Jun 2020 09:21:57 +0700 Subject: [PATCH 11/73] Delete myapp.py --- python/myapp.py | 22 ---------------------- 1 file changed, 22 deletions(-) delete mode 100644 python/myapp.py diff --git a/python/myapp.py b/python/myapp.py deleted file mode 100644 index 1f8aa4f..0000000 --- a/python/myapp.py +++ /dev/null @@ -1,22 +0,0 @@ -import yfinance as yf -import streamlit as st -import pandas as pd - -st.write(""" -# Simple Stock Price App - -Shown are the stock closing price and volume of Google! - -""") - -# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75 -#define the ticker symbol -tickerSymbol = 'GOOGL' -#get data on this ticker -tickerData = yf.Ticker(tickerSymbol) -#get the historical prices for this ticker -tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31') -# Open High Low Close Volume Dividends Stock Splits - -st.line_chart(tickerDf.Close) -st.line_chart(tickerDf.Volume) From ce419817b8dd52035c5b954b8e212ae437ab10e9 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 13 Jun 2020 09:22:09 +0700 Subject: [PATCH 12/73] Delete myapp2.py --- python/myapp2.py | 28 ---------------------------- 1 file changed, 28 deletions(-) delete mode 100644 python/myapp2.py diff --git a/python/myapp2.py b/python/myapp2.py deleted file mode 100644 index 70ba571..0000000 --- a/python/myapp2.py +++ /dev/null @@ -1,28 +0,0 @@ -import yfinance as yf -import streamlit as st -import pandas as pd - -st.write(""" -# Simple Stock Price App - -Shown are the stock **closing price** and ***volume*** of Google! - -""") - -# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75 -#define the ticker symbol -tickerSymbol = 'GOOGL' -#get data on this ticker -tickerData = yf.Ticker(tickerSymbol) -#get the historical prices for this ticker -tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31') -# Open High Low Close Volume Dividends Stock Splits - -st.write(""" -## Closing Price -""") -st.line_chart(tickerDf.Close) -st.write(""" -## Volume Price -""") -st.line_chart(tickerDf.Volume) From 7ab7c3c116c521cbf84201374d0acf6789969947 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 25 Jun 2020 18:44:08 +0700 Subject: [PATCH 13/73] Add files via upload --- streamlit/penguins-app.py | 80 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 streamlit/penguins-app.py diff --git a/streamlit/penguins-app.py b/streamlit/penguins-app.py new file mode 100644 index 0000000..4f1e78f --- /dev/null +++ b/streamlit/penguins-app.py @@ -0,0 +1,80 @@ +import streamlit as st +import pandas as pd +import numpy as np +import pickle +from sklearn.ensemble import RandomForestClassifier + +st.write(""" +# Penguin Prediction App + +This app predicts the **Palmer Penguin** species! + +Data obtained from the [palmerpenguins library](https://github.com/allisonhorst/palmerpenguins) in R by Allison Horst. +""") + +st.sidebar.header('User Input Features') + +st.sidebar.markdown(""" +[Example CSV input file](https://raw.githubusercontent.com/dataprofessor/data/master/penguins_example.csv) +""") + +# Collects user input features into dataframe +uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) +if uploaded_file is not None: + input_df = pd.read_csv(uploaded_file) +else: + def user_input_features(): + island = st.sidebar.selectbox('Island',('Biscoe','Dream','Torgersen')) + sex = st.sidebar.selectbox('Sex',('male','female')) + bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9) + bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2) + flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0) + body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0) + data = {'island': island, + 'bill_length_mm': bill_length_mm, + 'bill_depth_mm': bill_depth_mm, + 'flipper_length_mm': flipper_length_mm, + 'body_mass_g': body_mass_g, + 'sex': sex} + features = pd.DataFrame(data, index=[0]) + return features + input_df = user_input_features() + +# Combines user input features with entire penguins dataset +# This will be useful for the encoding phase +penguins_raw = pd.read_csv('penguins_cleaned.csv') +penguins = penguins_raw.drop(columns=['species']) +df = pd.concat([input_df,penguins],axis=0) + +# Encoding of ordinal features +# https://www.kaggle.com/pratik1120/penguin-dataset-eda-classification-and-clustering +encode = ['sex','island'] +for col in encode: + dummy = pd.get_dummies(df[col], prefix=col) + df = pd.concat([df,dummy], axis=1) + del df[col] +df = df[:1] # Selects only the first row (the user input data) + +# Displays the user input features +st.subheader('User Input features') + +if uploaded_file is not None: + st.write(df) +else: + st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).') + st.write(df) + +# Reads in saved classification model +load_clf = pickle.load(open('penguins_clf.pkl', 'rb')) + +# Apply model to make predictions +prediction = load_clf.predict(df) +prediction_proba = load_clf.predict_proba(df) + + +st.subheader('Prediction') +penguins_species = np.array(['Adelie','Chinstrap','Gentoo']) +st.write(penguins_species[prediction]) + +st.subheader('Prediction Probability') +st.write(prediction_proba) From 273eb518704f4ad9c6e8f2faad95e6b88889f307 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 25 Jun 2020 18:45:15 +0700 Subject: [PATCH 14/73] Rename streamlit/penguins-app.py to streamlit/part3/penguins-app.py --- streamlit/{ => part3}/penguins-app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part3}/penguins-app.py (100%) diff --git a/streamlit/penguins-app.py b/streamlit/part3/penguins-app.py similarity index 100% rename from streamlit/penguins-app.py rename to streamlit/part3/penguins-app.py From a558a95a36b163e9641ac3fb47476a108a003104 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 25 Jun 2020 18:45:54 +0700 Subject: [PATCH 15/73] Add files via upload --- streamlit/part3/penguins-model-building.py | 32 ++ streamlit/part3/penguins_cleaned.csv | 334 +++++++++++++++++++++ streamlit/part3/penguins_clf.pkl | Bin 0 -> 271320 bytes streamlit/part3/penguins_example.csv | 2 + 4 files changed, 368 insertions(+) create mode 100644 streamlit/part3/penguins-model-building.py create mode 100644 streamlit/part3/penguins_cleaned.csv create mode 100644 streamlit/part3/penguins_clf.pkl create mode 100644 streamlit/part3/penguins_example.csv diff --git a/streamlit/part3/penguins-model-building.py b/streamlit/part3/penguins-model-building.py new file mode 100644 index 0000000..c0e8cdd --- /dev/null +++ b/streamlit/part3/penguins-model-building.py @@ -0,0 +1,32 @@ +import pandas as pd +penguins = pd.read_csv('penguins_cleaned.csv') + +# Ordinal feature encoding +# https://www.kaggle.com/pratik1120/penguin-dataset-eda-classification-and-clustering +df = penguins.copy() +target = 'species' +encode = ['sex','island'] + +for col in encode: + dummy = pd.get_dummies(df[col], prefix=col) + df = pd.concat([df,dummy], axis=1) + del df[col] + +target_mapper = {'Adelie':0, 'Chinstrap':1, 'Gentoo':2} +def target_encode(val): + return target_mapper[val] + +df['species'] = df['species'].apply(target_encode) + +# Separating X and y +X = df.drop('species', axis=1) +Y = df['species'] + +# Build random forest model +from sklearn.ensemble import RandomForestClassifier +clf = RandomForestClassifier() +clf.fit(X, Y) + +# Saving the model +import pickle +pickle.dump(clf, open('penguins_clf.pkl', 'wb')) diff --git a/streamlit/part3/penguins_cleaned.csv b/streamlit/part3/penguins_cleaned.csv new file mode 100644 index 0000000..432be8f --- /dev/null +++ b/streamlit/part3/penguins_cleaned.csv @@ -0,0 +1,334 @@ +"species","island","bill_length_mm","bill_depth_mm","flipper_length_mm","body_mass_g","sex" +"Adelie","Torgersen",39.1,18.7,181,3750,"male" +"Adelie","Torgersen",39.5,17.4,186,3800,"female" +"Adelie","Torgersen",40.3,18,195,3250,"female" +"Adelie","Torgersen",36.7,19.3,193,3450,"female" +"Adelie","Torgersen",39.3,20.6,190,3650,"male" +"Adelie","Torgersen",38.9,17.8,181,3625,"female" +"Adelie","Torgersen",39.2,19.6,195,4675,"male" +"Adelie","Torgersen",41.1,17.6,182,3200,"female" +"Adelie","Torgersen",38.6,21.2,191,3800,"male" +"Adelie","Torgersen",34.6,21.1,198,4400,"male" +"Adelie","Torgersen",36.6,17.8,185,3700,"female" +"Adelie","Torgersen",38.7,19,195,3450,"female" +"Adelie","Torgersen",42.5,20.7,197,4500,"male" +"Adelie","Torgersen",34.4,18.4,184,3325,"female" +"Adelie","Torgersen",46,21.5,194,4200,"male" +"Adelie","Biscoe",37.8,18.3,174,3400,"female" 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z)}}@~e%{Fn@`HQJjp{42f868i@2rXLNsmo#2PqGZQe#|te2XAR4{~dhe{)mz&-Akp zqTEnur}k?ZuBST0EjaH*QAHXFXt^m z4Z}VsV4!=s4^F)V{3$T(a{`(rpw+$HuRZm7P|DnOGnI)B+g}FbI)TMAEY6N{xSPd_ zHg_UAj$LQwk96zGV5Y$S8tJg3`~8M~Rp{i4am>$Yen=EgqjT3s^akhd8PTil?~tC# zpSi!7pYB-KltuYqc+#u!yY}$x{86A+i=P1^|FO=^{F3b;M~`(ttoNAC@G+h7#dPx1 zpSWJ}m&9yGN!8n&mO@N_rdN$lz8J^+Y?qji{c?hee4KlHOm}RfV!Cq=kLb*QHM)zg zfKvH=Ka0|3giN1q@>ipiFUApna2CjNy814SQ*lV=j_AI9B6^k3>>u7fLd?hbSUxu% zGMv}B7TV24L^|V%`!(rtx^&0q01~IM?@c4YO_7K+v_mH}K#=eUGocZ%g})-NJBLIp1``yPrLF`r7;} zA-%nF#%fPTmuqdQ+pq%vrFnJ@So7uhp%K-jSMExO*sNMVwFGwdM22 z3|?CEJ4?)$t)7am6-{dX*q(5hJDCG}>~Q0G`#ln!1skKfJAUd;gK*~U-F}ashyQiC zs4omMsWNL2A5uz}A!WF9og2$o8)f_g*yjZ~*!q!&leMYgqudR;5`t7ZGn@p}mJdmiZR-}ZLp4$^~-x+Z1G zDJAD()&x0e`Gzqf6DJUeg9mPdn?x9GF-auQ}&m1yA;$loA!hd zLU+Aw;4B<>OIT~R?!8UtLpLmr?wFtIZE_88;bgxr=gal*(yue>pA}teoeLMVlUajX za_u@(S)YIZ@l5Uk)g^!LEw20)!C2AL7~x>w7cf{$KMSXx;-3SCeP2K`#oM*?3)&NF z=~lK`$4q6S!&aifFjHf2+f0i~q#G*EGEUnvoond=f3xF1+F} Date: Thu, 25 Jun 2020 18:46:34 +0700 Subject: [PATCH 16/73] Rename streamlit/iris-ml-app.py to streamlit/part2/iris-ml-app.py --- streamlit/{ => part2}/iris-ml-app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part2}/iris-ml-app.py (100%) diff --git a/streamlit/iris-ml-app.py b/streamlit/part2/iris-ml-app.py similarity index 100% rename from streamlit/iris-ml-app.py rename to streamlit/part2/iris-ml-app.py From b7cdbace0f72eebcded0d0cd4fd0076ae517292a Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 25 Jun 2020 18:48:44 +0700 Subject: [PATCH 17/73] Rename streamlit/myapp.py to streamlit/part1/myapp.py --- streamlit/{ => part1}/myapp.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part1}/myapp.py (100%) diff --git a/streamlit/myapp.py b/streamlit/part1/myapp.py similarity index 100% rename from streamlit/myapp.py rename to streamlit/part1/myapp.py From 695dd30087b34cf1bdbdc82c7b7788f1b20cd387 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 25 Jun 2020 18:49:01 +0700 Subject: [PATCH 18/73] Rename streamlit/myapp2.py to streamlit/part1/myapp2.py --- streamlit/{ => part1}/myapp2.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part1}/myapp2.py (100%) diff --git a/streamlit/myapp2.py b/streamlit/part1/myapp2.py similarity index 100% rename from streamlit/myapp2.py rename to streamlit/part1/myapp2.py From f8051923727c0a29b3e1723b7fa2d0ac2994a182 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:41:16 +0700 Subject: [PATCH 19/73] Create Procfile --- streamlit/part4/Procfile | 1 + 1 file changed, 1 insertion(+) create mode 100644 streamlit/part4/Procfile diff --git a/streamlit/part4/Procfile b/streamlit/part4/Procfile new file mode 100644 index 0000000..265de86 --- /dev/null +++ b/streamlit/part4/Procfile @@ -0,0 +1 @@ +web: sh setup.sh && streamlit run app.py From 622e04c4ab0f16db949281c9a4f09bdfcf428532 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:42:04 +0700 Subject: [PATCH 20/73] Create setup.sh --- streamlit/part4/setup.sh | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 streamlit/part4/setup.sh diff --git a/streamlit/part4/setup.sh b/streamlit/part4/setup.sh new file mode 100644 index 0000000..5af714d --- /dev/null +++ b/streamlit/part4/setup.sh @@ -0,0 +1,11 @@ +mkdir -p ~/.streamlit/ +echo “\ +[general]\n\ +email = \”your-email@domain.com\”\n\ +“ > ~/.streamlit/credentials.toml +echo “\ +[server]\n\ +headless = true\n\ +enableCORS=false\n\ +port = $PORT\n\ +“ > ~/.streamlit/config.toml From 9bce787c9c854aea2b68499026249187a8a2219a Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:48:24 +0700 Subject: [PATCH 21/73] Create requirements.txt --- streamlit/part4/requirements.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 streamlit/part4/requirements.txt diff --git a/streamlit/part4/requirements.txt b/streamlit/part4/requirements.txt new file mode 100644 index 0000000..cd38519 --- /dev/null +++ b/streamlit/part4/requirements.txt @@ -0,0 +1,4 @@ +streamlit==0.61.0 +pandas==0.25.3 +numpy==1.18.1 +scikit-learn==0.22.1 From 94d5ad74f8413f1ae721e4d5d347c56cb9a6a395 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:49:32 +0700 Subject: [PATCH 22/73] Add files via upload --- streamlit/part4/penguins-app.py | 80 +++++++ streamlit/part4/penguins_cleaned.csv | 334 +++++++++++++++++++++++++++ streamlit/part4/penguins_clf.pkl | Bin 0 -> 271320 bytes streamlit/part4/penguins_example.csv | 2 + 4 files changed, 416 insertions(+) create mode 100644 streamlit/part4/penguins-app.py create mode 100644 streamlit/part4/penguins_cleaned.csv create mode 100644 streamlit/part4/penguins_clf.pkl create mode 100644 streamlit/part4/penguins_example.csv diff --git a/streamlit/part4/penguins-app.py b/streamlit/part4/penguins-app.py new file mode 100644 index 0000000..4f1e78f --- /dev/null +++ b/streamlit/part4/penguins-app.py @@ -0,0 +1,80 @@ +import streamlit as st +import pandas as pd +import numpy as np +import pickle +from sklearn.ensemble import RandomForestClassifier + +st.write(""" +# Penguin Prediction App + +This app predicts the **Palmer Penguin** species! + +Data obtained from the [palmerpenguins library](https://github.com/allisonhorst/palmerpenguins) in R by Allison Horst. +""") + +st.sidebar.header('User Input Features') + +st.sidebar.markdown(""" +[Example CSV input file](https://raw.githubusercontent.com/dataprofessor/data/master/penguins_example.csv) +""") + +# Collects user input features into dataframe +uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) +if uploaded_file is not None: + input_df = pd.read_csv(uploaded_file) +else: + def user_input_features(): + island = st.sidebar.selectbox('Island',('Biscoe','Dream','Torgersen')) + sex = st.sidebar.selectbox('Sex',('male','female')) + bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9) + bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2) + flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0) + body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0) + data = {'island': island, + 'bill_length_mm': bill_length_mm, + 'bill_depth_mm': bill_depth_mm, + 'flipper_length_mm': flipper_length_mm, + 'body_mass_g': body_mass_g, + 'sex': sex} + features = pd.DataFrame(data, index=[0]) + return features + input_df = user_input_features() + +# Combines user input features with entire penguins dataset +# This will be useful for the encoding phase +penguins_raw = pd.read_csv('penguins_cleaned.csv') +penguins = penguins_raw.drop(columns=['species']) +df = pd.concat([input_df,penguins],axis=0) + +# Encoding of ordinal features +# https://www.kaggle.com/pratik1120/penguin-dataset-eda-classification-and-clustering +encode = ['sex','island'] +for col in encode: + dummy = pd.get_dummies(df[col], prefix=col) + df = pd.concat([df,dummy], axis=1) + del df[col] +df = df[:1] # Selects only the first row (the user input data) + +# Displays the user input features +st.subheader('User Input features') + +if uploaded_file is not None: + st.write(df) +else: + st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).') + st.write(df) + +# Reads in saved classification model +load_clf = pickle.load(open('penguins_clf.pkl', 'rb')) + +# Apply model to make predictions +prediction = load_clf.predict(df) +prediction_proba = load_clf.predict_proba(df) + + +st.subheader('Prediction') +penguins_species = np.array(['Adelie','Chinstrap','Gentoo']) +st.write(penguins_species[prediction]) + +st.subheader('Prediction Probability') +st.write(prediction_proba) diff --git a/streamlit/part4/penguins_cleaned.csv b/streamlit/part4/penguins_cleaned.csv new file mode 100644 index 0000000..432be8f --- /dev/null +++ b/streamlit/part4/penguins_cleaned.csv @@ -0,0 +1,334 @@ +"species","island","bill_length_mm","bill_depth_mm","flipper_length_mm","body_mass_g","sex" +"Adelie","Torgersen",39.1,18.7,181,3750,"male" +"Adelie","Torgersen",39.5,17.4,186,3800,"female" +"Adelie","Torgersen",40.3,18,195,3250,"female" +"Adelie","Torgersen",36.7,19.3,193,3450,"female" +"Adelie","Torgersen",39.3,20.6,190,3650,"male" +"Adelie","Torgersen",38.9,17.8,181,3625,"female" +"Adelie","Torgersen",39.2,19.6,195,4675,"male" +"Adelie","Torgersen",41.1,17.6,182,3200,"female" +"Adelie","Torgersen",38.6,21.2,191,3800,"male" +"Adelie","Torgersen",34.6,21.1,198,4400,"male" +"Adelie","Torgersen",36.6,17.8,185,3700,"female" +"Adelie","Torgersen",38.7,19,195,3450,"female" +"Adelie","Torgersen",42.5,20.7,197,4500,"male" +"Adelie","Torgersen",34.4,18.4,184,3325,"female" +"Adelie","Torgersen",46,21.5,194,4200,"male" +"Adelie","Biscoe",37.8,18.3,174,3400,"female" +"Adelie","Biscoe",37.7,18.7,180,3600,"male" +"Adelie","Biscoe",35.9,19.2,189,3800,"female" +"Adelie","Biscoe",38.2,18.1,185,3950,"male" +"Adelie","Biscoe",38.8,17.2,180,3800,"male" +"Adelie","Biscoe",35.3,18.9,187,3800,"female" +"Adelie","Biscoe",40.6,18.6,183,3550,"male" +"Adelie","Biscoe",40.5,17.9,187,3200,"female" +"Adelie","Biscoe",37.9,18.6,172,3150,"female" +"Adelie","Biscoe",40.5,18.9,180,3950,"male" +"Adelie","Dream",39.5,16.7,178,3250,"female" +"Adelie","Dream",37.2,18.1,178,3900,"male" +"Adelie","Dream",39.5,17.8,188,3300,"female" +"Adelie","Dream",40.9,18.9,184,3900,"male" +"Adelie","Dream",36.4,17,195,3325,"female" +"Adelie","Dream",39.2,21.1,196,4150,"male" +"Adelie","Dream",38.8,20,190,3950,"male" +"Adelie","Dream",42.2,18.5,180,3550,"female" +"Adelie","Dream",37.6,19.3,181,3300,"female" +"Adelie","Dream",39.8,19.1,184,4650,"male" +"Adelie","Dream",36.5,18,182,3150,"female" +"Adelie","Dream",40.8,18.4,195,3900,"male" +"Adelie","Dream",36,18.5,186,3100,"female" +"Adelie","Dream",44.1,19.7,196,4400,"male" +"Adelie","Dream",37,16.9,185,3000,"female" +"Adelie","Dream",39.6,18.8,190,4600,"male" +"Adelie","Dream",41.1,19,182,3425,"male" +"Adelie","Dream",36,17.9,190,3450,"female" +"Adelie","Dream",42.3,21.2,191,4150,"male" +"Adelie","Biscoe",39.6,17.7,186,3500,"female" +"Adelie","Biscoe",40.1,18.9,188,4300,"male" +"Adelie","Biscoe",35,17.9,190,3450,"female" +"Adelie","Biscoe",42,19.5,200,4050,"male" +"Adelie","Biscoe",34.5,18.1,187,2900,"female" +"Adelie","Biscoe",41.4,18.6,191,3700,"male" +"Adelie","Biscoe",39,17.5,186,3550,"female" +"Adelie","Biscoe",40.6,18.8,193,3800,"male" +"Adelie","Biscoe",36.5,16.6,181,2850,"female" +"Adelie","Biscoe",37.6,19.1,194,3750,"male" +"Adelie","Biscoe",35.7,16.9,185,3150,"female" +"Adelie","Biscoe",41.3,21.1,195,4400,"male" +"Adelie","Biscoe",37.6,17,185,3600,"female" +"Adelie","Biscoe",41.1,18.2,192,4050,"male" +"Adelie","Biscoe",36.4,17.1,184,2850,"female" +"Adelie","Biscoe",41.6,18,192,3950,"male" +"Adelie","Biscoe",35.5,16.2,195,3350,"female" +"Adelie","Biscoe",41.1,19.1,188,4100,"male" +"Adelie","Torgersen",35.9,16.6,190,3050,"female" +"Adelie","Torgersen",41.8,19.4,198,4450,"male" +"Adelie","Torgersen",33.5,19,190,3600,"female" +"Adelie","Torgersen",39.7,18.4,190,3900,"male" +"Adelie","Torgersen",39.6,17.2,196,3550,"female" +"Adelie","Torgersen",45.8,18.9,197,4150,"male" +"Adelie","Torgersen",35.5,17.5,190,3700,"female" +"Adelie","Torgersen",42.8,18.5,195,4250,"male" +"Adelie","Torgersen",40.9,16.8,191,3700,"female" +"Adelie","Torgersen",37.2,19.4,184,3900,"male" +"Adelie","Torgersen",36.2,16.1,187,3550,"female" +"Adelie","Torgersen",42.1,19.1,195,4000,"male" +"Adelie","Torgersen",34.6,17.2,189,3200,"female" +"Adelie","Torgersen",42.9,17.6,196,4700,"male" +"Adelie","Torgersen",36.7,18.8,187,3800,"female" +"Adelie","Torgersen",35.1,19.4,193,4200,"male" +"Adelie","Dream",37.3,17.8,191,3350,"female" +"Adelie","Dream",41.3,20.3,194,3550,"male" +"Adelie","Dream",36.3,19.5,190,3800,"male" +"Adelie","Dream",36.9,18.6,189,3500,"female" +"Adelie","Dream",38.3,19.2,189,3950,"male" +"Adelie","Dream",38.9,18.8,190,3600,"female" +"Adelie","Dream",35.7,18,202,3550,"female" +"Adelie","Dream",41.1,18.1,205,4300,"male" +"Adelie","Dream",34,17.1,185,3400,"female" +"Adelie","Dream",39.6,18.1,186,4450,"male" +"Adelie","Dream",36.2,17.3,187,3300,"female" +"Adelie","Dream",40.8,18.9,208,4300,"male" +"Adelie","Dream",38.1,18.6,190,3700,"female" +"Adelie","Dream",40.3,18.5,196,4350,"male" +"Adelie","Dream",33.1,16.1,178,2900,"female" +"Adelie","Dream",43.2,18.5,192,4100,"male" +"Adelie","Biscoe",35,17.9,192,3725,"female" +"Adelie","Biscoe",41,20,203,4725,"male" +"Adelie","Biscoe",37.7,16,183,3075,"female" +"Adelie","Biscoe",37.8,20,190,4250,"male" +"Adelie","Biscoe",37.9,18.6,193,2925,"female" +"Adelie","Biscoe",39.7,18.9,184,3550,"male" +"Adelie","Biscoe",38.6,17.2,199,3750,"female" +"Adelie","Biscoe",38.2,20,190,3900,"male" +"Adelie","Biscoe",38.1,17,181,3175,"female" +"Adelie","Biscoe",43.2,19,197,4775,"male" +"Adelie","Biscoe",38.1,16.5,198,3825,"female" +"Adelie","Biscoe",45.6,20.3,191,4600,"male" +"Adelie","Biscoe",39.7,17.7,193,3200,"female" +"Adelie","Biscoe",42.2,19.5,197,4275,"male" +"Adelie","Biscoe",39.6,20.7,191,3900,"female" +"Adelie","Biscoe",42.7,18.3,196,4075,"male" +"Adelie","Torgersen",38.6,17,188,2900,"female" +"Adelie","Torgersen",37.3,20.5,199,3775,"male" +"Adelie","Torgersen",35.7,17,189,3350,"female" +"Adelie","Torgersen",41.1,18.6,189,3325,"male" +"Adelie","Torgersen",36.2,17.2,187,3150,"female" +"Adelie","Torgersen",37.7,19.8,198,3500,"male" +"Adelie","Torgersen",40.2,17,176,3450,"female" +"Adelie","Torgersen",41.4,18.5,202,3875,"male" +"Adelie","Torgersen",35.2,15.9,186,3050,"female" +"Adelie","Torgersen",40.6,19,199,4000,"male" +"Adelie","Torgersen",38.8,17.6,191,3275,"female" +"Adelie","Torgersen",41.5,18.3,195,4300,"male" +"Adelie","Torgersen",39,17.1,191,3050,"female" +"Adelie","Torgersen",44.1,18,210,4000,"male" +"Adelie","Torgersen",38.5,17.9,190,3325,"female" +"Adelie","Torgersen",43.1,19.2,197,3500,"male" +"Adelie","Dream",36.8,18.5,193,3500,"female" +"Adelie","Dream",37.5,18.5,199,4475,"male" +"Adelie","Dream",38.1,17.6,187,3425,"female" +"Adelie","Dream",41.1,17.5,190,3900,"male" +"Adelie","Dream",35.6,17.5,191,3175,"female" +"Adelie","Dream",40.2,20.1,200,3975,"male" +"Adelie","Dream",37,16.5,185,3400,"female" +"Adelie","Dream",39.7,17.9,193,4250,"male" +"Adelie","Dream",40.2,17.1,193,3400,"female" +"Adelie","Dream",40.6,17.2,187,3475,"male" +"Adelie","Dream",32.1,15.5,188,3050,"female" +"Adelie","Dream",40.7,17,190,3725,"male" +"Adelie","Dream",37.3,16.8,192,3000,"female" +"Adelie","Dream",39,18.7,185,3650,"male" +"Adelie","Dream",39.2,18.6,190,4250,"male" +"Adelie","Dream",36.6,18.4,184,3475,"female" +"Adelie","Dream",36,17.8,195,3450,"female" +"Adelie","Dream",37.8,18.1,193,3750,"male" +"Adelie","Dream",36,17.1,187,3700,"female" +"Adelie","Dream",41.5,18.5,201,4000,"male" +"Gentoo","Biscoe",46.1,13.2,211,4500,"female" +"Gentoo","Biscoe",50,16.3,230,5700,"male" +"Gentoo","Biscoe",48.7,14.1,210,4450,"female" +"Gentoo","Biscoe",50,15.2,218,5700,"male" +"Gentoo","Biscoe",47.6,14.5,215,5400,"male" +"Gentoo","Biscoe",46.5,13.5,210,4550,"female" +"Gentoo","Biscoe",45.4,14.6,211,4800,"female" +"Gentoo","Biscoe",46.7,15.3,219,5200,"male" +"Gentoo","Biscoe",43.3,13.4,209,4400,"female" +"Gentoo","Biscoe",46.8,15.4,215,5150,"male" +"Gentoo","Biscoe",40.9,13.7,214,4650,"female" +"Gentoo","Biscoe",49,16.1,216,5550,"male" +"Gentoo","Biscoe",45.5,13.7,214,4650,"female" +"Gentoo","Biscoe",48.4,14.6,213,5850,"male" +"Gentoo","Biscoe",45.8,14.6,210,4200,"female" +"Gentoo","Biscoe",49.3,15.7,217,5850,"male" +"Gentoo","Biscoe",42,13.5,210,4150,"female" +"Gentoo","Biscoe",49.2,15.2,221,6300,"male" +"Gentoo","Biscoe",46.2,14.5,209,4800,"female" +"Gentoo","Biscoe",48.7,15.1,222,5350,"male" +"Gentoo","Biscoe",50.2,14.3,218,5700,"male" +"Gentoo","Biscoe",45.1,14.5,215,5000,"female" +"Gentoo","Biscoe",46.5,14.5,213,4400,"female" +"Gentoo","Biscoe",46.3,15.8,215,5050,"male" +"Gentoo","Biscoe",42.9,13.1,215,5000,"female" +"Gentoo","Biscoe",46.1,15.1,215,5100,"male" +"Gentoo","Biscoe",47.8,15,215,5650,"male" +"Gentoo","Biscoe",48.2,14.3,210,4600,"female" +"Gentoo","Biscoe",50,15.3,220,5550,"male" +"Gentoo","Biscoe",47.3,15.3,222,5250,"male" +"Gentoo","Biscoe",42.8,14.2,209,4700,"female" +"Gentoo","Biscoe",45.1,14.5,207,5050,"female" +"Gentoo","Biscoe",59.6,17,230,6050,"male" +"Gentoo","Biscoe",49.1,14.8,220,5150,"female" +"Gentoo","Biscoe",48.4,16.3,220,5400,"male" +"Gentoo","Biscoe",42.6,13.7,213,4950,"female" +"Gentoo","Biscoe",44.4,17.3,219,5250,"male" +"Gentoo","Biscoe",44,13.6,208,4350,"female" +"Gentoo","Biscoe",48.7,15.7,208,5350,"male" +"Gentoo","Biscoe",42.7,13.7,208,3950,"female" +"Gentoo","Biscoe",49.6,16,225,5700,"male" +"Gentoo","Biscoe",45.3,13.7,210,4300,"female" +"Gentoo","Biscoe",49.6,15,216,4750,"male" +"Gentoo","Biscoe",50.5,15.9,222,5550,"male" +"Gentoo","Biscoe",43.6,13.9,217,4900,"female" +"Gentoo","Biscoe",45.5,13.9,210,4200,"female" +"Gentoo","Biscoe",50.5,15.9,225,5400,"male" +"Gentoo","Biscoe",44.9,13.3,213,5100,"female" +"Gentoo","Biscoe",45.2,15.8,215,5300,"male" +"Gentoo","Biscoe",46.6,14.2,210,4850,"female" 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+"Gentoo","Biscoe",46.4,15,216,4700,"female" +"Gentoo","Biscoe",48.6,16,230,5800,"male" +"Gentoo","Biscoe",47.5,14.2,209,4600,"female" +"Gentoo","Biscoe",51.1,16.3,220,6000,"male" +"Gentoo","Biscoe",45.2,13.8,215,4750,"female" +"Gentoo","Biscoe",45.2,16.4,223,5950,"male" +"Gentoo","Biscoe",49.1,14.5,212,4625,"female" +"Gentoo","Biscoe",52.5,15.6,221,5450,"male" +"Gentoo","Biscoe",47.4,14.6,212,4725,"female" +"Gentoo","Biscoe",50,15.9,224,5350,"male" +"Gentoo","Biscoe",44.9,13.8,212,4750,"female" +"Gentoo","Biscoe",50.8,17.3,228,5600,"male" +"Gentoo","Biscoe",43.4,14.4,218,4600,"female" +"Gentoo","Biscoe",51.3,14.2,218,5300,"male" +"Gentoo","Biscoe",47.5,14,212,4875,"female" +"Gentoo","Biscoe",52.1,17,230,5550,"male" +"Gentoo","Biscoe",47.5,15,218,4950,"female" +"Gentoo","Biscoe",52.2,17.1,228,5400,"male" +"Gentoo","Biscoe",45.5,14.5,212,4750,"female" +"Gentoo","Biscoe",49.5,16.1,224,5650,"male" +"Gentoo","Biscoe",44.5,14.7,214,4850,"female" +"Gentoo","Biscoe",50.8,15.7,226,5200,"male" 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[PATCH 23/73] Delete Procfile --- streamlit/part4/Procfile | 1 - 1 file changed, 1 deletion(-) delete mode 100644 streamlit/part4/Procfile diff --git a/streamlit/part4/Procfile b/streamlit/part4/Procfile deleted file mode 100644 index 265de86..0000000 --- a/streamlit/part4/Procfile +++ /dev/null @@ -1 +0,0 @@ -web: sh setup.sh && streamlit run app.py From 46d1bbad477d3309a85c73465c4dd199793880e2 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:57:02 +0700 Subject: [PATCH 24/73] Delete penguins-app.py --- streamlit/part4/penguins-app.py | 80 --------------------------------- 1 file changed, 80 deletions(-) delete mode 100644 streamlit/part4/penguins-app.py diff --git a/streamlit/part4/penguins-app.py b/streamlit/part4/penguins-app.py deleted file mode 100644 index 4f1e78f..0000000 --- a/streamlit/part4/penguins-app.py +++ /dev/null @@ -1,80 +0,0 @@ -import streamlit as st -import pandas as pd -import numpy as np -import pickle -from sklearn.ensemble import RandomForestClassifier - -st.write(""" -# Penguin Prediction App - -This app predicts the **Palmer Penguin** species! - -Data obtained from the [palmerpenguins library](https://github.com/allisonhorst/palmerpenguins) in R by Allison Horst. -""") - -st.sidebar.header('User Input Features') - -st.sidebar.markdown(""" -[Example CSV input file](https://raw.githubusercontent.com/dataprofessor/data/master/penguins_example.csv) -""") - -# Collects user input features into dataframe -uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"]) -if uploaded_file is not None: - input_df = pd.read_csv(uploaded_file) -else: - def user_input_features(): - island = st.sidebar.selectbox('Island',('Biscoe','Dream','Torgersen')) - sex = st.sidebar.selectbox('Sex',('male','female')) - bill_length_mm = st.sidebar.slider('Bill length (mm)', 32.1,59.6,43.9) - bill_depth_mm = st.sidebar.slider('Bill depth (mm)', 13.1,21.5,17.2) - flipper_length_mm = st.sidebar.slider('Flipper length (mm)', 172.0,231.0,201.0) - body_mass_g = st.sidebar.slider('Body mass (g)', 2700.0,6300.0,4207.0) - data = {'island': island, - 'bill_length_mm': bill_length_mm, - 'bill_depth_mm': bill_depth_mm, - 'flipper_length_mm': flipper_length_mm, - 'body_mass_g': body_mass_g, - 'sex': sex} - features = pd.DataFrame(data, index=[0]) - return features - input_df = user_input_features() - -# Combines user input features with entire penguins dataset -# This will be useful for the encoding phase -penguins_raw = pd.read_csv('penguins_cleaned.csv') -penguins = penguins_raw.drop(columns=['species']) -df = pd.concat([input_df,penguins],axis=0) - -# Encoding of ordinal features -# https://www.kaggle.com/pratik1120/penguin-dataset-eda-classification-and-clustering -encode = ['sex','island'] -for col in encode: - dummy = pd.get_dummies(df[col], prefix=col) - df = pd.concat([df,dummy], axis=1) - del df[col] -df = df[:1] # Selects only the first row (the user input data) - -# Displays the user input features -st.subheader('User Input features') - -if uploaded_file is not None: - st.write(df) -else: - st.write('Awaiting CSV file to be uploaded. Currently using example input parameters (shown below).') - st.write(df) - -# Reads in saved classification model -load_clf = pickle.load(open('penguins_clf.pkl', 'rb')) - -# Apply model to make predictions -prediction = load_clf.predict(df) -prediction_proba = load_clf.predict_proba(df) - - -st.subheader('Prediction') -penguins_species = np.array(['Adelie','Chinstrap','Gentoo']) -st.write(penguins_species[prediction]) - -st.subheader('Prediction Probability') -st.write(prediction_proba) From b5c1f15acfb17ccb16f75bb1b69a84538dd827c2 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:57:12 +0700 Subject: [PATCH 25/73] Delete penguins_cleaned.csv --- streamlit/part4/penguins_cleaned.csv | 334 --------------------------- 1 file changed, 334 deletions(-) delete mode 100644 streamlit/part4/penguins_cleaned.csv diff --git a/streamlit/part4/penguins_cleaned.csv b/streamlit/part4/penguins_cleaned.csv deleted file mode 100644 index 432be8f..0000000 --- a/streamlit/part4/penguins_cleaned.csv +++ /dev/null @@ -1,334 +0,0 @@ -"species","island","bill_length_mm","bill_depth_mm","flipper_length_mm","body_mass_g","sex" -"Adelie","Torgersen",39.1,18.7,181,3750,"male" -"Adelie","Torgersen",39.5,17.4,186,3800,"female" -"Adelie","Torgersen",40.3,18,195,3250,"female" -"Adelie","Torgersen",36.7,19.3,193,3450,"female" -"Adelie","Torgersen",39.3,20.6,190,3650,"male" -"Adelie","Torgersen",38.9,17.8,181,3625,"female" -"Adelie","Torgersen",39.2,19.6,195,4675,"male" -"Adelie","Torgersen",41.1,17.6,182,3200,"female" -"Adelie","Torgersen",38.6,21.2,191,3800,"male" -"Adelie","Torgersen",34.6,21.1,198,4400,"male" -"Adelie","Torgersen",36.6,17.8,185,3700,"female" -"Adelie","Torgersen",38.7,19,195,3450,"female" -"Adelie","Torgersen",42.5,20.7,197,4500,"male" -"Adelie","Torgersen",34.4,18.4,184,3325,"female" -"Adelie","Torgersen",46,21.5,194,4200,"male" -"Adelie","Biscoe",37.8,18.3,174,3400,"female" -"Adelie","Biscoe",37.7,18.7,180,3600,"male" -"Adelie","Biscoe",35.9,19.2,189,3800,"female" -"Adelie","Biscoe",38.2,18.1,185,3950,"male" -"Adelie","Biscoe",38.8,17.2,180,3800,"male" -"Adelie","Biscoe",35.3,18.9,187,3800,"female" -"Adelie","Biscoe",40.6,18.6,183,3550,"male" -"Adelie","Biscoe",40.5,17.9,187,3200,"female" -"Adelie","Biscoe",37.9,18.6,172,3150,"female" -"Adelie","Biscoe",40.5,18.9,180,3950,"male" -"Adelie","Dream",39.5,16.7,178,3250,"female" -"Adelie","Dream",37.2,18.1,178,3900,"male" -"Adelie","Dream",39.5,17.8,188,3300,"female" -"Adelie","Dream",40.9,18.9,184,3900,"male" -"Adelie","Dream",36.4,17,195,3325,"female" -"Adelie","Dream",39.2,21.1,196,4150,"male" -"Adelie","Dream",38.8,20,190,3950,"male" -"Adelie","Dream",42.2,18.5,180,3550,"female" -"Adelie","Dream",37.6,19.3,181,3300,"female" -"Adelie","Dream",39.8,19.1,184,4650,"male" -"Adelie","Dream",36.5,18,182,3150,"female" -"Adelie","Dream",40.8,18.4,195,3900,"male" -"Adelie","Dream",36,18.5,186,3100,"female" -"Adelie","Dream",44.1,19.7,196,4400,"male" -"Adelie","Dream",37,16.9,185,3000,"female" -"Adelie","Dream",39.6,18.8,190,4600,"male" -"Adelie","Dream",41.1,19,182,3425,"male" -"Adelie","Dream",36,17.9,190,3450,"female" -"Adelie","Dream",42.3,21.2,191,4150,"male" -"Adelie","Biscoe",39.6,17.7,186,3500,"female" -"Adelie","Biscoe",40.1,18.9,188,4300,"male" -"Adelie","Biscoe",35,17.9,190,3450,"female" -"Adelie","Biscoe",42,19.5,200,4050,"male" -"Adelie","Biscoe",34.5,18.1,187,2900,"female" -"Adelie","Biscoe",41.4,18.6,191,3700,"male" -"Adelie","Biscoe",39,17.5,186,3550,"female" -"Adelie","Biscoe",40.6,18.8,193,3800,"male" -"Adelie","Biscoe",36.5,16.6,181,2850,"female" -"Adelie","Biscoe",37.6,19.1,194,3750,"male" -"Adelie","Biscoe",35.7,16.9,185,3150,"female" -"Adelie","Biscoe",41.3,21.1,195,4400,"male" -"Adelie","Biscoe",37.6,17,185,3600,"female" -"Adelie","Biscoe",41.1,18.2,192,4050,"male" -"Adelie","Biscoe",36.4,17.1,184,2850,"female" -"Adelie","Biscoe",41.6,18,192,3950,"male" -"Adelie","Biscoe",35.5,16.2,195,3350,"female" -"Adelie","Biscoe",41.1,19.1,188,4100,"male" -"Adelie","Torgersen",35.9,16.6,190,3050,"female" -"Adelie","Torgersen",41.8,19.4,198,4450,"male" -"Adelie","Torgersen",33.5,19,190,3600,"female" -"Adelie","Torgersen",39.7,18.4,190,3900,"male" -"Adelie","Torgersen",39.6,17.2,196,3550,"female" -"Adelie","Torgersen",45.8,18.9,197,4150,"male" -"Adelie","Torgersen",35.5,17.5,190,3700,"female" -"Adelie","Torgersen",42.8,18.5,195,4250,"male" -"Adelie","Torgersen",40.9,16.8,191,3700,"female" -"Adelie","Torgersen",37.2,19.4,184,3900,"male" -"Adelie","Torgersen",36.2,16.1,187,3550,"female" -"Adelie","Torgersen",42.1,19.1,195,4000,"male" -"Adelie","Torgersen",34.6,17.2,189,3200,"female" -"Adelie","Torgersen",42.9,17.6,196,4700,"male" -"Adelie","Torgersen",36.7,18.8,187,3800,"female" -"Adelie","Torgersen",35.1,19.4,193,4200,"male" -"Adelie","Dream",37.3,17.8,191,3350,"female" -"Adelie","Dream",41.3,20.3,194,3550,"male" -"Adelie","Dream",36.3,19.5,190,3800,"male" -"Adelie","Dream",36.9,18.6,189,3500,"female" -"Adelie","Dream",38.3,19.2,189,3950,"male" -"Adelie","Dream",38.9,18.8,190,3600,"female" -"Adelie","Dream",35.7,18,202,3550,"female" -"Adelie","Dream",41.1,18.1,205,4300,"male" -"Adelie","Dream",34,17.1,185,3400,"female" -"Adelie","Dream",39.6,18.1,186,4450,"male" -"Adelie","Dream",36.2,17.3,187,3300,"female" -"Adelie","Dream",40.8,18.9,208,4300,"male" -"Adelie","Dream",38.1,18.6,190,3700,"female" -"Adelie","Dream",40.3,18.5,196,4350,"male" -"Adelie","Dream",33.1,16.1,178,2900,"female" -"Adelie","Dream",43.2,18.5,192,4100,"male" -"Adelie","Biscoe",35,17.9,192,3725,"female" -"Adelie","Biscoe",41,20,203,4725,"male" -"Adelie","Biscoe",37.7,16,183,3075,"female" -"Adelie","Biscoe",37.8,20,190,4250,"male" -"Adelie","Biscoe",37.9,18.6,193,2925,"female" -"Adelie","Biscoe",39.7,18.9,184,3550,"male" -"Adelie","Biscoe",38.6,17.2,199,3750,"female" -"Adelie","Biscoe",38.2,20,190,3900,"male" -"Adelie","Biscoe",38.1,17,181,3175,"female" -"Adelie","Biscoe",43.2,19,197,4775,"male" -"Adelie","Biscoe",38.1,16.5,198,3825,"female" -"Adelie","Biscoe",45.6,20.3,191,4600,"male" -"Adelie","Biscoe",39.7,17.7,193,3200,"female" -"Adelie","Biscoe",42.2,19.5,197,4275,"male" -"Adelie","Biscoe",39.6,20.7,191,3900,"female" -"Adelie","Biscoe",42.7,18.3,196,4075,"male" -"Adelie","Torgersen",38.6,17,188,2900,"female" -"Adelie","Torgersen",37.3,20.5,199,3775,"male" -"Adelie","Torgersen",35.7,17,189,3350,"female" -"Adelie","Torgersen",41.1,18.6,189,3325,"male" -"Adelie","Torgersen",36.2,17.2,187,3150,"female" -"Adelie","Torgersen",37.7,19.8,198,3500,"male" -"Adelie","Torgersen",40.2,17,176,3450,"female" -"Adelie","Torgersen",41.4,18.5,202,3875,"male" -"Adelie","Torgersen",35.2,15.9,186,3050,"female" -"Adelie","Torgersen",40.6,19,199,4000,"male" -"Adelie","Torgersen",38.8,17.6,191,3275,"female" -"Adelie","Torgersen",41.5,18.3,195,4300,"male" -"Adelie","Torgersen",39,17.1,191,3050,"female" -"Adelie","Torgersen",44.1,18,210,4000,"male" -"Adelie","Torgersen",38.5,17.9,190,3325,"female" -"Adelie","Torgersen",43.1,19.2,197,3500,"male" -"Adelie","Dream",36.8,18.5,193,3500,"female" -"Adelie","Dream",37.5,18.5,199,4475,"male" -"Adelie","Dream",38.1,17.6,187,3425,"female" -"Adelie","Dream",41.1,17.5,190,3900,"male" -"Adelie","Dream",35.6,17.5,191,3175,"female" -"Adelie","Dream",40.2,20.1,200,3975,"male" -"Adelie","Dream",37,16.5,185,3400,"female" -"Adelie","Dream",39.7,17.9,193,4250,"male" -"Adelie","Dream",40.2,17.1,193,3400,"female" -"Adelie","Dream",40.6,17.2,187,3475,"male" -"Adelie","Dream",32.1,15.5,188,3050,"female" -"Adelie","Dream",40.7,17,190,3725,"male" -"Adelie","Dream",37.3,16.8,192,3000,"female" -"Adelie","Dream",39,18.7,185,3650,"male" -"Adelie","Dream",39.2,18.6,190,4250,"male" -"Adelie","Dream",36.6,18.4,184,3475,"female" -"Adelie","Dream",36,17.8,195,3450,"female" -"Adelie","Dream",37.8,18.1,193,3750,"male" -"Adelie","Dream",36,17.1,187,3700,"female" 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[PATCH 27/73] Delete penguins_example.csv --- streamlit/part4/penguins_example.csv | 2 -- 1 file changed, 2 deletions(-) delete mode 100644 streamlit/part4/penguins_example.csv diff --git a/streamlit/part4/penguins_example.csv b/streamlit/part4/penguins_example.csv deleted file mode 100644 index 17d6a31..0000000 --- a/streamlit/part4/penguins_example.csv +++ /dev/null @@ -1,2 +0,0 @@ -island,bill_length_mm,bill_depth_mm,flipper_length_mm,body_mass_g,sex -Biscoe,43.9,17.2,201.0,4207.0,male From 228576dcc4601b8ec88d08e806aafca05efbbcf3 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:57:41 +0700 Subject: [PATCH 28/73] Delete requirements.txt --- streamlit/part4/requirements.txt | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 streamlit/part4/requirements.txt diff --git a/streamlit/part4/requirements.txt b/streamlit/part4/requirements.txt deleted file mode 100644 index cd38519..0000000 --- a/streamlit/part4/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -streamlit==0.61.0 -pandas==0.25.3 -numpy==1.18.1 -scikit-learn==0.22.1 From 2cfbf6e3238878248857a3f928cbb84cd9fe8b8b Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 27 Jun 2020 09:57:49 +0700 Subject: [PATCH 29/73] Delete setup.sh --- streamlit/part4/setup.sh | 11 ----------- 1 file changed, 11 deletions(-) delete mode 100644 streamlit/part4/setup.sh diff --git a/streamlit/part4/setup.sh b/streamlit/part4/setup.sh deleted file mode 100644 index 5af714d..0000000 --- a/streamlit/part4/setup.sh +++ /dev/null @@ -1,11 +0,0 @@ -mkdir -p ~/.streamlit/ -echo “\ -[general]\n\ -email = \”your-email@domain.com\”\n\ -“ > ~/.streamlit/credentials.toml -echo “\ -[server]\n\ -headless = true\n\ -enableCORS=false\n\ -port = $PORT\n\ -“ > ~/.streamlit/config.toml From d9b6df555d08e1bf228e53e33cb5cd4ec73d5122 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 5 Jul 2020 14:19:45 +0700 Subject: [PATCH 30/73] Add files via upload --- ...linesterase_Regression_Random_Forest.ipynb | 1617 +++++++++++++++++ 1 file changed, 1617 insertions(+) create mode 100644 python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb diff --git a/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb b/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb new file mode 100644 index 0000000..edd3a3d --- /dev/null +++ b/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb @@ -0,0 +1,1617 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "colab": { + "name": "CDD-ML-Part-4-Acetylcholinesterase-Regression-Random-Forest.ipynb", + "provenance": [], + "collapsed_sections": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "7AAfh_j7hSwQ", + "colab_type": "text" + }, + "source": [ + "# **Bioinformatics Project - Computational Drug Discovery [Part 4] Comparing Classifiers for Building Classification Models**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", + "\n", + "In this Jupyter notebook, we will be building a real-life **data science project** that you can include in your **data science portfolio**. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", + "\n", + "In **Part 4**, we will be building a regression model of acetylcholinesterase inhibitors using the random forest algorithm.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jw7MqTMphSwR", + "colab_type": "text" + }, + "source": [ + "## **1. Import libraries**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "D3rFTNAIhSwS", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0GvT3PArhSwX", + "colab_type": "text" + }, + "source": [ + "## **2. Load the data set**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "043mRJZIhSwY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + }, + "outputId": "96bc783e-89f4-4013-dc58-376965a73425" + }, + "source": [ + "! wget https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-07-01 12:24:14-- https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv\n", + "Resolving github.com (github.com)... 140.82.112.4\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv [following]\n", + "--2020-07-01 12:24:15-- https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 8363909 (8.0M) [text/plain]\n", + "Saving to: ‘acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv’\n", + "\n", + "acetylcholinesteras 100%[===================>] 7.98M 18.2MB/s in 0.4s \n", + "\n", + "2020-07-01 12:24:15 (18.2 MB/s) - ‘acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv’ saved [8363909/8363909]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cSgppzqPiR0G", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df = pd.read_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kuiiC0xthSwb", + "colab_type": "text" + }, + "source": [ + "## **3. Input features**\n", + "The ***Acetylcholinesterase*** data set contains 881 input features and 1 output variable (pIC50 values)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iCeQQn0uhSwb", + "colab_type": "text" + }, + "source": [ + "### **3.1. Input features**" + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "li32nAPohSwc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 439 + }, + "outputId": "e3cbf177-e82b-4e57-c32a-af9ab924dda8" + }, + "source": [ + "X = df.drop('pIC50', axis=1)\n", + "X" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + " PubchemFP0 PubchemFP1 ... PubchemFP879 PubchemFP880\n", + "0 1 1 ... 0 0\n", + "1 1 1 ... 0 0\n", + "2 1 1 ... 0 0\n", + "3 1 1 ... 0 0\n", + "4 1 1 ... 0 0\n", + "... ... ... ... ... ...\n", + "4690 1 1 ... 0 0\n", + "4691 1 1 ... 0 0\n", + "4692 1 1 ... 0 0\n", + "4693 1 1 ... 0 0\n", + "4694 1 1 ... 0 0\n", + "\n", + "[4695 rows x 881 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sGQjCQtfhSwg", + "colab_type": "text" + }, + "source": [ + "### **3.2. Output features**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OWylAtAVhSwh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "5747e12a-3abb-4e43-fc1c-c8dbe42f3992" + }, + "source": [ + "Y = df.pIC50\n", + "Y" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 6.124939\n", + "1 7.000000\n", + "2 4.301030\n", + "3 6.522879\n", + "4 6.096910\n", + " ... \n", + "4690 5.612610\n", + "4691 5.595166\n", + "4692 5.419075\n", + "4693 5.460924\n", + "4694 5.555955\n", + "Name: pIC50, Length: 4695, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E-zGSqXohSwx", + "colab_type": "text" + }, + "source": [ + "### **3.3. Let's examine the data dimension**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nhT04XtLhSwx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "0e36315c-a8c1-4d11-8e99-8a92038ff80d" + }, + "source": [ + "X.shape" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4695, 881)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uzQlK8gNhSw0", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6526f2b6-8875-49f4-bd38-dc2db7225e52" + }, + "source": [ + "Y.shape" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4695,)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0qQCpX097qf_", + "colab_type": "text" + }, + "source": [ + "### **3.4. Remove low variance features**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rjHK2SoI7tXI", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.feature_selection import VarianceThreshold\n", + "selection = VarianceThreshold(threshold=(.8 * (1 - .8))) \n", + "X = selection.fit_transform(X)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nXcpQh_s8nx7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "cb3a135a-9b96-4f69-bb28-538ae4be7d5d" + }, + "source": [ + "X.shape" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4695, 137)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AjhOlkOVhSxR", + "colab_type": "text" + }, + "source": [ + "## **4. Data split (80/20 ratio)**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "M1Bmg1HWhSxR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uz1o3c1LhSxU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "dc0b23f3-eebc-4159-ae71-6d9e27ce9e26" + }, + "source": [ + "X_train.shape, Y_train.shape" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((3756, 137), (3756,))" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4tnwDASChSxW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "15c12f57-053b-4e28-afe1-b403510fd6ea" + }, + "source": [ + "X_test.shape, Y_test.shape" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((939, 137), (939,))" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PYsE-tIAhSw3", + "colab_type": "text" + }, + "source": [ + "## **5. Building a Regression Model using Random Forest**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YHM3DCD5wuNe", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "42a4752a-c4ea-414d-ac19-dff9c64cfbcb" + }, + "source": [ + "model = RandomForestRegressor(n_estimators=100)\n", + "model.fit(X_train, Y_train)\n", + "r2 = model.score(X_test, Y_test)\n", + "r2" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5404672128298216" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "A4Rv5wdQ1M6H", + "colab_type": "code", + "colab": {} + }, + "source": [ + "Y_pred = model.predict(X_test)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LoLgjePyUXcv", + "colab_type": "text" + }, + "source": [ + "## **6. Scatter Plot of Experimental vs Predicted pIC50 Values**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hfqpfjxw3IAK", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "7206a3c7-eff9-4617-e9d9-d54dd0cfd29c" + }, + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "sns.set(color_codes=True)\n", + "sns.set_style(\"white\")\n", + "\n", + "ax = sns.regplot(Y_test, Y_pred, scatter_kws={'alpha':0.4})\n", + "ax.set_xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n", + "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n", + "ax.set_xlim(0, 12)\n", + "ax.set_ylim(0, 12)\n", + "ax.figure.set_size_inches(5, 5)\n", + "plt.show" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From c860a556c8b4bc6ba483dc06734f5553ce540cca Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 7 Jul 2020 16:45:55 +0700 Subject: [PATCH 31/73] Add files via upload --- streamlit/basketball_app.py | 68 +++++++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 streamlit/basketball_app.py diff --git a/streamlit/basketball_app.py b/streamlit/basketball_app.py new file mode 100644 index 0000000..9dc7283 --- /dev/null +++ b/streamlit/basketball_app.py @@ -0,0 +1,68 @@ +import streamlit as st +import pandas as pd +import base64 +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np + +st.title('NBA Player Stats Explorer') + +st.markdown(""" +This app performs simple webscraping of NBA player stats data! +* **Python libraries:** base64, pandas, streamlit +* **Data source:** [Basketball-reference.com](https://www.basketball-reference.com/). +""") + +st.sidebar.header('User Input Features') +selected_year = st.sidebar.selectbox('Year', list(reversed(range(1950,2020)))) + +# Web scraping of NBA player stats +@st.cache +def load_data(year): + url = "https://www.basketball-reference.com/leagues/NBA_" + str(year) + "_per_game.html" + html = pd.read_html(url, header = 0) + df = html[0] + raw = df.drop(df[df.Age == 'Age'].index) # Deletes repeating headers in content + raw = raw.fillna(0) + playerstats = raw.drop(['Rk'], axis=1) + return playerstats +playerstats = load_data(selected_year) + +# Sidebar - Team selection +sorted_unique_team = sorted(playerstats.Tm.unique()) +selected_team = st.sidebar.multiselect('Team', sorted_unique_team, sorted_unique_team) + +# Sidebar - Position selection +unique_pos = ['C','PF','SF','PG','SG'] +selected_pos = st.sidebar.multiselect('Position', unique_pos, unique_pos) + +# Filtering data +df_selected_team = playerstats[(playerstats.Tm.isin(selected_team)) & (playerstats.Pos.isin(selected_pos))] + +st.header('Display Player Stats of Selected Team(s)') +st.write('Data Dimension: ' + str(df_selected_team.shape[0]) + ' rows and ' + str(df_selected_team.shape[1]) + ' columns.') +st.dataframe(df_selected_team) + +# Download NBA player stats data +# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806 +def filedownload(df): + csv = df.to_csv(index=False) + b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions + href = f'Download CSV File' + return href + +st.markdown(filedownload(df_selected_team), unsafe_allow_html=True) + +# Heatmap +if st.button('Intercorrelation Heatmap'): + st.header('Intercorrelation Matrix Heatmap') + df_selected_team.to_csv('output.csv',index=False) + df = pd.read_csv('output.csv') + + corr = df.corr() + mask = np.zeros_like(corr) + mask[np.triu_indices_from(mask)] = True + with sns.axes_style("white"): + f, ax = plt.subplots(figsize=(7, 5)) + ax = sns.heatmap(corr, mask=mask, vmax=1, square=True) + st.pyplot() From 87f2698fa3fa236a060f6e48ec5ce0a5dc3bb5ba Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 7 Jul 2020 16:46:37 +0700 Subject: [PATCH 32/73] Rename streamlit/basketball_app.py to streamlit/part-5/basketball_app.py --- streamlit/{ => part-5}/basketball_app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part-5}/basketball_app.py (100%) diff --git a/streamlit/basketball_app.py b/streamlit/part-5/basketball_app.py similarity index 100% rename from streamlit/basketball_app.py rename to streamlit/part-5/basketball_app.py From c100977a8a15255b9654e12a2f81b5eca8ad410d Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 7 Jul 2020 16:46:58 +0700 Subject: [PATCH 33/73] Rename streamlit/part-5/basketball_app.py to streamlit/part5/basketball_app.py --- streamlit/{part-5 => part5}/basketball_app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{part-5 => part5}/basketball_app.py (100%) diff --git a/streamlit/part-5/basketball_app.py b/streamlit/part5/basketball_app.py similarity index 100% rename from streamlit/part-5/basketball_app.py rename to streamlit/part5/basketball_app.py From 13876b6ae82a0c4daa908436be0d107b9757434e Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 9 Aug 2020 17:00:33 +0700 Subject: [PATCH 34/73] Add files via upload --- ...cs_predicting_solubility_2_1_PyCaret.ipynb | 1306 +++++ ...cs_predicting_solubility_2_2_PyCaret.ipynb | 4747 +++++++++++++++++ 2 files changed, 6053 insertions(+) create mode 100644 python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb create mode 100644 python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb diff --git a/python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb b/python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb new file mode 100644 index 0000000..c01bb58 --- /dev/null +++ b/python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb @@ -0,0 +1,1306 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "2.1-Cheminformatics-PyCaret-predicting-solubility.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OQi3X7TNUl5Y", + "colab_type": "text" + }, + "source": [ + "# **Cheminformatics in Python [PART 2.1] Predicting Solubility of Molecules using PyCaret | End-to-End Data Science Project** \n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor \n", + "\n", + "In this Jupyter notebook, we will continue our journey into the world of Cheminformatics (i.e. lies at the interface of Informatics and Chemistry) by simplifying this notebook via the use of the low-code machine learning library PyCaret.\n", + "\n", + "\n", + "**Information from the previous notebook:**\n", + "\n", + "We will be reproducing a research article (by John S. Delaney$^1$) by applying Linear Regression to predict the solubility of molecules (i.e. solubility of drugs is an important physicochemical property in Drug discovery, design and development).\n", + "\n", + "This idea for this notebook was inspired by the excellent blog post by Pat Walters$^2$ where he reproduced the linear regression model with similar degree of performance as that of Delaney. This example is also briefly described in the book ***Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More***.$^3$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AQW_Ts66R4Ms", + "colab_type": "text" + }, + "source": [ + "## **1. Install conda and libraries**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-jNwdYoBR8ea", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "844c8e71-85af-48ea-99ea-1b5b01216feb" + }, + "source": [ + "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n", + "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n", + "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n", + "! conda install -c rdkit rdkit -y\n", + "import sys\n", + "sys.path.append('/usr/local/lib/python3.7/site-packages/')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2020-07-28 02:18:57-- https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n", + "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n", + "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 85055499 (81M) [application/x-sh]\n", + "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’\n", + "\n", + "\r Miniconda 0%[ ] 0 --.-KB/s \r Miniconda3 16%[==> ] 13.65M 68.3MB/s \r Miniconda3- 80%[===============> ] 65.22M 163MB/s \rMiniconda3-py37_4.8 100%[===================>] 81.12M 181MB/s in 0.4s \n", + "\n", + "2020-07-28 02:18:58 (181 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’ saved [85055499/85055499]\n", + "\n", + "PREFIX=/usr/local\n", + "Unpacking payload ...\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n", + "Solving environment: - \b\b\\ \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - _libgcc_mutex==0.1=main\n", + " - asn1crypto==1.3.0=py37_0\n", + " - ca-certificates==2020.1.1=0\n", + " - certifi==2019.11.28=py37_0\n", + " - cffi==1.14.0=py37h2e261b9_0\n", + " - chardet==3.0.4=py37_1003\n", + " - conda-package-handling==1.6.0=py37h7b6447c_0\n", + " - conda==4.8.2=py37_0\n", + " - cryptography==2.8=py37h1ba5d50_0\n", + " - idna==2.8=py37_0\n", + " - ld_impl_linux-64==2.33.1=h53a641e_7\n", + " - libedit==3.1.20181209=hc058e9b_0\n", + " - libffi==3.2.1=hd88cf55_4\n", + " - libgcc-ng==9.1.0=hdf63c60_0\n", + " - libstdcxx-ng==9.1.0=hdf63c60_0\n", + " - ncurses==6.2=he6710b0_0\n", + " - openssl==1.1.1d=h7b6447c_4\n", + " - pip==20.0.2=py37_1\n", + " - pycosat==0.6.3=py37h7b6447c_0\n", + " - pycparser==2.19=py37_0\n", + " - pyopenssl==19.1.0=py37_0\n", + " - pysocks==1.7.1=py37_0\n", + " - python==3.7.6=h0371630_2\n", + " - readline==7.0=h7b6447c_5\n", + " - requests==2.22.0=py37_1\n", + " - ruamel_yaml==0.15.87=py37h7b6447c_0\n", + " - setuptools==45.2.0=py37_0\n", + " - six==1.14.0=py37_0\n", + " - sqlite==3.31.1=h7b6447c_0\n", + " - tk==8.6.8=hbc83047_0\n", + " - tqdm==4.42.1=py_0\n", + " - urllib3==1.25.8=py37_0\n", + " - wheel==0.34.2=py37_0\n", + " - xz==5.2.4=h14c3975_4\n", + " - yaml==0.1.7=had09818_2\n", + " - zlib==1.2.11=h7b6447c_3\n", + "\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n", + " asn1crypto pkgs/main/linux-64::asn1crypto-1.3.0-py37_0\n", + " ca-certificates pkgs/main/linux-64::ca-certificates-2020.1.1-0\n", + " certifi pkgs/main/linux-64::certifi-2019.11.28-py37_0\n", + " cffi pkgs/main/linux-64::cffi-1.14.0-py37h2e261b9_0\n", + " chardet pkgs/main/linux-64::chardet-3.0.4-py37_1003\n", + " conda pkgs/main/linux-64::conda-4.8.2-py37_0\n", + " conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n", + " cryptography pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n", + " idna pkgs/main/linux-64::idna-2.8-py37_0\n", + " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n", + " libedit pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n", + " libffi pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n", + " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n", + " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n", + " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_0\n", + " openssl pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_4\n", + " pip pkgs/main/linux-64::pip-20.0.2-py37_1\n", + " pycosat pkgs/main/linux-64::pycosat-0.6.3-py37h7b6447c_0\n", + " pycparser pkgs/main/linux-64::pycparser-2.19-py37_0\n", + " pyopenssl pkgs/main/linux-64::pyopenssl-19.1.0-py37_0\n", + " pysocks pkgs/main/linux-64::pysocks-1.7.1-py37_0\n", + " python pkgs/main/linux-64::python-3.7.6-h0371630_2\n", + " readline pkgs/main/linux-64::readline-7.0-h7b6447c_5\n", + " requests pkgs/main/linux-64::requests-2.22.0-py37_1\n", + " ruamel_yaml pkgs/main/linux-64::ruamel_yaml-0.15.87-py37h7b6447c_0\n", + " setuptools pkgs/main/linux-64::setuptools-45.2.0-py37_0\n", + " six pkgs/main/linux-64::six-1.14.0-py37_0\n", + " sqlite pkgs/main/linux-64::sqlite-3.31.1-h7b6447c_0\n", + " tk pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n", + " tqdm pkgs/main/noarch::tqdm-4.42.1-py_0\n", + " urllib3 pkgs/main/linux-64::urllib3-1.25.8-py37_0\n", + " wheel pkgs/main/linux-64::wheel-0.34.2-py37_0\n", + " xz pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n", + " yaml pkgs/main/linux-64::yaml-0.1.7-had09818_2\n", + " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n", + "\n", + "\n", + "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n", + "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", + "installation finished.\n", + "WARNING:\n", + " You currently have a PYTHONPATH environment variable set. This may cause\n", + " unexpected behavior when running the Python interpreter in Miniconda3.\n", + " For best results, please verify that your PYTHONPATH only points to\n", + " directories of packages that are compatible with the Python interpreter\n", + " in Miniconda3: /usr/local\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n", + "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n", + "Collecting package metadata (repodata.json): \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", + "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - rdkit\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " blas-1.0 | mkl 6 KB\n", + " bzip2-1.0.8 | h7b6447c_0 78 KB\n", + " ca-certificates-2020.6.24 | 0 125 KB\n", + " cairo-1.14.12 | h8948797_3 906 KB\n", + " certifi-2020.6.20 | py37_0 156 KB\n", + " conda-4.8.3 | py37_0 2.8 MB\n", + " fontconfig-2.13.0 | h9420a91_0 227 KB\n", + " freetype-2.10.2 | h5ab3b9f_0 608 KB\n", + " glib-2.63.1 | h5a9c865_0 2.9 MB\n", + " icu-58.2 | he6710b0_3 10.5 MB\n", + " intel-openmp-2020.1 | 217 780 KB\n", + " jpeg-9b | h024ee3a_2 214 KB\n", + " libboost-1.67.0 | h46d08c1_4 13.0 MB\n", + " libgfortran-ng-7.3.0 | hdf63c60_0 1006 KB\n", + " libpng-1.6.37 | hbc83047_0 278 KB\n", + " libtiff-4.1.0 | h2733197_0 447 KB\n", + " libuuid-1.0.3 | h1bed415_2 15 KB\n", + " libxcb-1.14 | h7b6447c_0 505 KB\n", + " libxml2-2.9.9 | hea5a465_1 1.6 MB\n", + " mkl-2020.1 | 217 129.0 MB\n", + " mkl-service-2.3.0 | py37he904b0f_0 218 KB\n", + " mkl_fft-1.1.0 | py37h23d657b_0 143 KB\n", + " mkl_random-1.1.1 | py37h0573a6f_0 322 KB\n", + " numpy-1.18.5 | py37ha1c710e_0 5 KB\n", + " numpy-base-1.18.5 | py37hde5b4d6_0 4.1 MB\n", + " olefile-0.46 | py37_0 50 KB\n", + " openssl-1.1.1g | h7b6447c_0 2.5 MB\n", + " pandas-1.0.5 | py37h0573a6f_0 7.8 MB\n", + " pcre-8.44 | he6710b0_0 212 KB\n", + " pillow-7.1.2 | py37hb39fc2d_0 603 KB\n", + " pixman-0.40.0 | h7b6447c_0 370 KB\n", + " py-boost-1.67.0 | py37h04863e7_4 278 KB\n", + " python-dateutil-2.8.1 | py_0 215 KB\n", + " pytz-2020.1 | py_0 184 KB\n", + " rdkit-2020.03.3.0 | py37hc20afe1_1 24.8 MB rdkit\n", + " zstd-1.3.7 | h0b5b093_0 401 KB\n", + " ------------------------------------------------------------\n", + " Total: 207.1 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " blas pkgs/main/linux-64::blas-1.0-mkl\n", + " bzip2 pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0\n", + " cairo pkgs/main/linux-64::cairo-1.14.12-h8948797_3\n", + " fontconfig pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0\n", + " freetype pkgs/main/linux-64::freetype-2.10.2-h5ab3b9f_0\n", + " glib pkgs/main/linux-64::glib-2.63.1-h5a9c865_0\n", + " icu pkgs/main/linux-64::icu-58.2-he6710b0_3\n", + " intel-openmp pkgs/main/linux-64::intel-openmp-2020.1-217\n", + " jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n", + " libboost pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4\n", + " libgfortran-ng pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0\n", + " libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n", + " libtiff pkgs/main/linux-64::libtiff-4.1.0-h2733197_0\n", + " libuuid pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2\n", + " libxcb pkgs/main/linux-64::libxcb-1.14-h7b6447c_0\n", + " libxml2 pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1\n", + " mkl pkgs/main/linux-64::mkl-2020.1-217\n", + " mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0\n", + " mkl_fft pkgs/main/linux-64::mkl_fft-1.1.0-py37h23d657b_0\n", + " mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py37h0573a6f_0\n", + " numpy pkgs/main/linux-64::numpy-1.18.5-py37ha1c710e_0\n", + " numpy-base pkgs/main/linux-64::numpy-base-1.18.5-py37hde5b4d6_0\n", + " olefile pkgs/main/linux-64::olefile-0.46-py37_0\n", + " pandas pkgs/main/linux-64::pandas-1.0.5-py37h0573a6f_0\n", + " pcre pkgs/main/linux-64::pcre-8.44-he6710b0_0\n", + " pillow pkgs/main/linux-64::pillow-7.1.2-py37hb39fc2d_0\n", + " pixman pkgs/main/linux-64::pixman-0.40.0-h7b6447c_0\n", + " py-boost pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4\n", + " python-dateutil pkgs/main/noarch::python-dateutil-2.8.1-py_0\n", + " pytz pkgs/main/noarch::pytz-2020.1-py_0\n", + " rdkit rdkit/linux-64::rdkit-2020.03.3.0-py37hc20afe1_1\n", + " zstd pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0\n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates 2020.1.1-0 --> 2020.6.24-0\n", + " certifi 2019.11.28-py37_0 --> 2020.6.20-py37_0\n", + " conda 4.8.2-py37_0 --> 4.8.3-py37_0\n", + " openssl 1.1.1d-h7b6447c_4 --> 1.1.1g-h7b6447c_0\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "bzip2-1.0.8 | 78 KB | : 100% 1.0/1 [00:00<00:00, 10.01it/s]\n", + "pandas-1.0.5 | 7.8 MB | : 100% 1.0/1 [00:00<00:00, 2.22it/s] \n", + "mkl-service-2.3.0 | 218 KB | : 100% 1.0/1 [00:00<00:00, 19.17it/s]\n", + "conda-4.8.3 | 2.8 MB | : 100% 1.0/1 [00:00<00:00, 18.58s/it] \n", + "mkl_random-1.1.1 | 322 KB | : 100% 1.0/1 [00:00<00:00, 15.12it/s]\n", + "pcre-8.44 | 212 KB | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n", + "fontconfig-2.13.0 | 227 KB | : 100% 1.0/1 [00:00<00:00, 16.10it/s]\n", + "libxml2-2.9.9 | 1.6 MB | : 100% 1.0/1 [00:00<00:00, 5.63it/s]\n", + "pillow-7.1.2 | 603 KB | : 100% 1.0/1 [00:00<00:00, 10.25it/s]\n", + "libgfortran-ng-7.3.0 | 1006 KB | : 100% 1.0/1 [00:00<00:00, 11.04it/s]\n", + "py-boost-1.67.0 | 278 KB | : 100% 1.0/1 [00:00<00:00, 12.08it/s]\n", + "python-dateutil-2.8. | 215 KB | : 100% 1.0/1 [00:00<00:00, 16.41it/s]\n", + "libpng-1.6.37 | 278 KB | : 100% 1.0/1 [00:00<00:00, 16.63it/s]\n", + 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Delaney's solubility dataset**\n", + "\n", + "The original [Delaney's dataset](https://pubs.acs.org/doi/10.1021/ci034243x) available as a [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x)$^4$. The full paper is entitled [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x).$^1$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s6o9QzQnNRVx", + "colab_type": "text" + }, + "source": [ + "### **2.1. Download the dataset**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6KKvV74LM1it", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# ! wget https://pubs.acs.org/doi/suppl/10.1021/ci034243x/suppl_file/ci034243xsi20040112_053635.txt" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PJGp_xenNYKy", + "colab_type": "text" + }, + "source": [ + "### **2.2. Read in the dataset**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0ufiOpEbNooH", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nLS6bwiRNtuV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 415 + }, + "outputId": "cfe35c3f-dabf-4629-fe89-0634a7e693ad" + }, + "source": [ + "delaney_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv'\n", + "sol = pd.read_csv(delaney_url)\n", + "sol" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Compound IDmeasured log(solubility:mol/L)ESOL predicted log(solubility:mol/L)SMILES
01,1,1,2-Tetrachloroethane-2.180-2.794ClCC(Cl)(Cl)Cl
11,1,1-Trichloroethane-2.000-2.232CC(Cl)(Cl)Cl
21,1,2,2-Tetrachloroethane-1.740-2.549ClC(Cl)C(Cl)Cl
31,1,2-Trichloroethane-1.480-1.961ClCC(Cl)Cl
41,1,2-Trichlorotrifluoroethane-3.040-3.077FC(F)(Cl)C(F)(Cl)Cl
...............
1139vamidothion1.144-1.446CNC(=O)C(C)SCCSP(=O)(OC)(OC)
1140Vinclozolin-4.925-4.377CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C
1141Warfarin-3.893-3.913CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O
1142Xipamide-3.790-3.642Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O
1143XMC-2.581-2.688CNC(=O)Oc1cc(C)cc(C)c1
\n", + "

1144 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Compound ID ... SMILES\n", + "0 1,1,1,2-Tetrachloroethane ... ClCC(Cl)(Cl)Cl\n", + "1 1,1,1-Trichloroethane ... CC(Cl)(Cl)Cl\n", + "2 1,1,2,2-Tetrachloroethane ... ClC(Cl)C(Cl)Cl\n", + "3 1,1,2-Trichloroethane ... ClCC(Cl)Cl\n", + "4 1,1,2-Trichlorotrifluoroethane ... FC(F)(Cl)C(F)(Cl)Cl\n", + "... ... ... ...\n", + "1139 vamidothion ... CNC(=O)C(C)SCCSP(=O)(OC)(OC)\n", + "1140 Vinclozolin ... CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\n", + "1141 Warfarin ... CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O \n", + "1142 Xipamide ... Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\n", + "1143 XMC ... CNC(=O)Oc1cc(C)cc(C)c1\n", + "\n", + "[1144 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uqQLXGKQQAvX", + "colab_type": "text" + }, + "source": [ + "## **3. Calculate molecular descriptors in rdkit**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iD_6apg8kYDy", + "colab_type": "text" + }, + "source": [ + "### **3.1. Convert list of molecules to rdkit object**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bQjMv-wLOlmg", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from rdkit import Chem" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AaHAVM2yFm3J", + "colab_type": "code", + "colab": {} + }, + "source": [ + "mol_list = [Chem.MolFromSmiles(element) for element in sol.SMILES]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Fw5BCeh7F2c9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "15c6d713-80e4-4d22-f7da-7887392def9c" + }, + "source": [ + "len(mol_list)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1144" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uASSo7ZMF5iv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104 + }, + "outputId": "4c6a6f8a-5eef-4cf7-f4a5-dd5f6b0e7729" + }, + "source": [ + "mol_list[:5]" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "olyPX1TjQMvr", + "colab_type": "text" + }, + "source": [ + "### **3.2. Calculate molecular descriptors**\n", + "\n", + "To predict **LogS** (log of the aqueous solubility), the study by Delaney makes use of 4 molecular descriptors:\n", + "1. **cLogP** *(Octanol-water partition coefficient)*\n", + "2. **MW** *(Molecular weight)*\n", + "3. **RB** *(Number of rotatable bonds)*\n", + "4. **AP** *(Aromatic proportion = number of aromatic atoms / total number of heavy atoms)*\n", + "\n", + "Unfortunately, rdkit readily computes the first 3. As for the AP descriptor, we will calculate this by manually computing the ratio of the *number of aromatic atoms* to the *total number of heavy atoms* which rdkit can compute." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iS4w5r5ocxT8", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "from rdkit.Chem import Descriptors" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nIF7IrIlcGPD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def AromaticProportion(m):\n", + " aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())]\n", + " aa_count = []\n", + " for i in aromatic_atoms:\n", + " if i==True:\n", + " aa_count.append(1)\n", + " AromaticAtom = sum(aa_count)\n", + " HeavyAtom = Descriptors.HeavyAtomCount(m)\n", + " AR = AromaticAtom/HeavyAtom\n", + " return AR" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WkNMPVu_giw8", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n", + "\n", + "def generate(smiles, verbose=False):\n", + "\n", + " moldata= []\n", + " for elem in smiles:\n", + " mol=Chem.MolFromSmiles(elem) \n", + " moldata.append(mol)\n", + " \n", + " baseData= np.arange(1,1)\n", + " i=0 \n", + " for mol in moldata: \n", + " \n", + " desc_MolLogP = Descriptors.MolLogP(mol)\n", + " desc_MolWt = Descriptors.MolWt(mol)\n", + " desc_NumRotatableBonds = Descriptors.NumRotatableBonds(mol)\n", + " desc_AromaticProportion = AromaticProportion(mol)\n", + " \n", + " row = np.array([desc_MolLogP,\n", + " desc_MolWt,\n", + " desc_NumRotatableBonds,\n", + " desc_AromaticProportion]) \n", + " \n", + " if(i==0):\n", + " baseData=row\n", + " else:\n", + " baseData=np.vstack([baseData, row])\n", + " i=i+1 \n", + " \n", + " columnNames=[\"MolLogP\",\"MolWt\",\"NumRotatableBonds\",\"AromaticProportion\"] \n", + " descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n", + " \n", + " return descriptors" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MzMulCVvcf59", + "colab_type": "code", + "colab": {} + }, + "source": [ + "X = generate(sol.SMILES)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JfVfpGa9wGaD", + "colab_type": "text" + }, + "source": [ + "## **4. Preparing the X and Y Data Matrices**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3ZKZKPOuCVTY", + "colab_type": "text" + }, + "source": [ + "### **4.1. X matrix (the computed descriptors)**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6VFAdZwbCg0T", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 415 + }, + "outputId": "738ceff2-b929-48c8-8f42-a412e5c960b7" + }, + "source": [ + "X" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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22.59380167.8501.00.000000
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...............
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1144 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion\n", + "0 2.59540 167.850 0.0 0.000000\n", + "1 2.37650 133.405 0.0 0.000000\n", + "2 2.59380 167.850 1.0 0.000000\n", + "3 2.02890 133.405 1.0 0.000000\n", + "4 2.91890 187.375 1.0 0.000000\n", + "... ... ... ... ...\n", + "1139 1.98820 287.343 8.0 0.000000\n", + "1140 3.42130 286.114 2.0 0.333333\n", + "1141 3.60960 308.333 4.0 0.695652\n", + "1142 2.56214 354.815 3.0 0.521739\n", + "1143 2.02164 179.219 1.0 0.461538\n", + "\n", + "[1144 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zZI9k4h6FsPF", + "colab_type": "text" + }, + "source": [ + "### **4.2. Y matrix**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6m4Akv3rHG3E", + "colab_type": "text" + }, + "source": [ + "Assigning the second column (index 1) to the Y matrix" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fcvXs7R7FrbC", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "outputId": "fc95e058-bfdb-412c-d3e5-383f2db9e535" + }, + "source": [ + "Y = sol.iloc[:,1]\n", + "Y = Y.rename(\"logS\")\n", + "Y" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 -2.180\n", + "1 -2.000\n", + "2 -1.740\n", + "3 -1.480\n", + "4 -3.040\n", + " ... \n", + "1139 1.144\n", + "1140 -4.925\n", + "1141 -3.893\n", + "1142 -3.790\n", + "1143 -2.581\n", + "Name: logS, Length: 1144, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a7Y7XBrAnGHs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "outputId": "b45437e5-83eb-4302-edbe-ab171680b856" + }, + "source": [ + "Y" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 -2.180\n", + "1 -2.000\n", + "2 -1.740\n", + "3 -1.480\n", + "4 -3.040\n", + " ... \n", + "1139 1.144\n", + "1140 -4.925\n", + "1141 -3.893\n", + "1142 -3.790\n", + "1143 -2.581\n", + "Name: logS, Length: 1144, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Eabg-PCIuCoY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 285 + }, + "outputId": "f9890ad3-c5d3-4b84-a976-39a4c70aaef1" + }, + "source": [ + "Y.hist()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n", + "0 2.59540 167.850 0.0 0.000000 -2.180\n", + "1 2.37650 133.405 0.0 0.000000 -2.000\n", + "2 2.59380 167.850 1.0 0.000000 -1.740\n", + "3 2.02890 133.405 1.0 0.000000 -1.480\n", + "4 2.91890 187.375 1.0 0.000000 -3.040\n", + "... ... ... ... ... ...\n", + "1139 1.98820 287.343 8.0 0.000000 1.144\n", + "1140 3.42130 286.114 2.0 0.333333 -4.925\n", + "1141 3.60960 308.333 4.0 0.695652 -3.893\n", + "1142 2.56214 354.815 3.0 0.521739 -3.790\n", + "1143 2.02164 179.219 1.0 0.461538 -2.581\n", + "\n", + "[1144 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AZ0setezmBbD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "dataset.to_csv('delaney_solubility_with_descriptors.csv', index=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ARiv3f1iC565", + "colab_type": "text" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EICKN2Hn5X_L", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jwM1QHeLbxJl", + "colab_type": "text" + }, + "source": [ + "## **Reference**\n", + "\n", + "1. John S. Delaney. [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n", + "\n", + "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n", + "\n", + "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n", + "\n", + "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure.\n", + "\n", + "5. Scott M. Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), A Unified Approach to Interpreting Model Predictions, ***Advances in Neural Information Processing Systems 30 (NIPS 2017)***, 2017." + ] + } + ] +} \ No newline at end of file diff --git a/python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb b/python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb new file mode 100644 index 0000000..2c7bbb8 --- /dev/null +++ b/python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb @@ -0,0 +1,4747 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "2.2-Cheminformatics-PyCaret-predicting-solubility.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "eced8f77156e439aa934f67a011e84b6": { + "model_module": 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"cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OQi3X7TNUl5Y", + "colab_type": "text" + }, + "source": [ + "# **Cheminformatics in Python [PART 2.2] Predicting Solubility of Molecules using PyCaret | End-to-End Data Science Project** \n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor \n", + "\n", + "In this Jupyter notebook, we will continue our journey into the world of Cheminformatics (i.e. lies at the interface of Informatics and Chemistry) by simplifying this notebook via the use of the low-code machine learning library PyCaret.\n", + "\n", + "\n", + "**Information from the previous notebook:**\n", + "\n", + "We will be reproducing a research article (by John S. Delaney$^1$) by applying Linear Regression to predict the solubility of molecules (i.e. solubility of drugs is an important physicochemical property in Drug discovery, design and development).\n", + "\n", + "This idea for this notebook was inspired by the excellent blog post by Pat Walters$^2$ where he reproduced the linear regression model with similar degree of performance as that of Delaney. This example is also briefly described in the book ***Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More***.$^3$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xWbQX5dWh5Il", + "colab_type": "text" + }, + "source": [ + "## **1. Install PyCaret**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NoN9ejGKh896", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "968276bc-e9e0-4288-bd7d-f33cf9d35a9a" + }, + "source": [ + "! pip install pycaret" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting pycaret\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/c7/41/f7fa05b6ce3cb3096a35fb5ac6dc0f2bb23e8304f068618fb2501be0a562/pycaret-1.0.0-py3-none-any.whl (188kB)\n", + "\r\u001b[K |█▊ | 10kB 15.6MB/s eta 0:00:01\r\u001b[K |███▌ | 20kB 2.1MB/s eta 0:00:01\r\u001b[K |█████▏ | 30kB 2.6MB/s eta 0:00:01\r\u001b[K |███████ | 40kB 3.1MB/s eta 0:00:01\r\u001b[K |████████▊ | 51kB 2.5MB/s eta 0:00:01\r\u001b[K |██████████▍ | 61kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████▏ | 71kB 3.0MB/s eta 0:00:01\r\u001b[K |██████████████ | 81kB 3.2MB/s eta 0:00:01\r\u001b[K |███████████████▋ | 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sha256=9ae622483a47091ce78f4f9e332a02d4b49d50e5108fe148245aded18ca68470\n", + " Stored in directory: /root/.cache/pip/wheels/44/d7/dc/e830ab00bc2dd3b2731295103baa070f8cbdda8891f71a7a8d\n", + " Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pyLDAvis: filename=pyLDAvis-2.1.2-py2.py3-none-any.whl size=97711 sha256=622f74afa59b45d175334d29d8308e776f7c2b9cdac28b4399f6dadf84443acf\n", + " Stored in directory: /root/.cache/pip/wheels/98/71/24/513a99e58bb6b8465bae4d2d5e9dba8f0bef8179e3051ac414\n", + " Building wheel for pandas-profiling (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pandas-profiling: filename=pandas_profiling-2.3.0-py2.py3-none-any.whl size=145035 sha256=0f4df88af247c98a2b95a0fe6267a0678ad4e461be9eded1bb5e77ae52776e83\n", + " Stored in directory: /root/.cache/pip/wheels/ce/c7/f1/dbfef4848ebb048cb1d4a22d1ed0c62d8ff2523747235e19fe\n", + " Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pyod: filename=pyod-0.8.1-cp36-none-any.whl size=105653 sha256=cafc7f9fa6b8d305defedf9bb171e377ccf4dda34efb8af5a8166ad38aefe420\n", + " Stored in directory: /root/.cache/pip/wheels/2e/ca/18/727e9d98a41f5f4385a97d5b429f3a9c8fbee13f9780c18642\n", + " Building wheel for funcy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for funcy: filename=funcy-1.14-py2.py3-none-any.whl size=32042 sha256=20a28c3cd9b51696ebd56632cc9b9aabcf247a7eb504896d8126edcb4bd3a125\n", + " Stored in directory: /root/.cache/pip/wheels/20/5a/d8/1d875df03deae6f178dfdf70238cca33f948ef8a6f5209f2eb\n", + " Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for htmlmin: filename=htmlmin-0.1.12-cp36-none-any.whl size=27084 sha256=a87d7be937c79e420ceede5c388b54d053596ed71182949feaaacadf117bd2a5\n", + " Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n", + " Building wheel for combo (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for combo: filename=combo-0.1.1-cp36-none-any.whl size=42111 sha256=dc2b4716ad6142dfb372060a7fbafdd77772c9b90b2740bb4655ff68ef72535a\n", + " Stored in directory: /root/.cache/pip/wheels/55/ec/e5/a2331372c676c467e70c6646e646edf6997d5c4905b8c0f5e6\n", + " Building wheel for suod (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for suod: filename=suod-0.0.4-cp36-none-any.whl size=2167157 sha256=bf710bb64f0daa27634af8891b68083ad7ff011b5d110e2e3130e6a842670335\n", + " Stored in directory: /root/.cache/pip/wheels/57/55/e5/a4fca65bba231f6d0115059b589148774b41faea25b3f2aa27\n", + "Successfully built shap cufflinks pyLDAvis pandas-profiling pyod funcy htmlmin combo suod\n", + "Installing collected packages: scikit-learn, shap, datefinder, chart-studio, cufflinks, rsa, colorama, botocore, awscli, yellowbrick, zope.interface, DateTime, kmodes, catboost, funcy, pyLDAvis, lightgbm, htmlmin, phik, confuse, pandas-profiling, combo, suod, pyod, pycaret\n", + " Found existing installation: scikit-learn 0.22.2.post1\n", + " Uninstalling scikit-learn-0.22.2.post1:\n", + " Successfully uninstalled scikit-learn-0.22.2.post1\n", + " Found existing installation: cufflinks 0.17.3\n", + " Uninstalling cufflinks-0.17.3:\n", + " Successfully uninstalled cufflinks-0.17.3\n", + " Found existing installation: rsa 4.6\n", + " Uninstalling rsa-4.6:\n", + " Successfully uninstalled rsa-4.6\n", + " Found existing installation: botocore 1.17.24\n", + " Uninstalling botocore-1.17.24:\n", + " Successfully uninstalled botocore-1.17.24\n", + " Found existing installation: yellowbrick 0.9.1\n", + " Uninstalling yellowbrick-0.9.1:\n", + " Successfully uninstalled yellowbrick-0.9.1\n", + " Found existing installation: lightgbm 2.2.3\n", + " Uninstalling lightgbm-2.2.3:\n", + " Successfully uninstalled lightgbm-2.2.3\n", + " Found existing installation: pandas-profiling 1.4.1\n", + " Uninstalling pandas-profiling-1.4.1:\n", + " Successfully uninstalled pandas-profiling-1.4.1\n", + "Successfully installed DateTime-4.3 awscli-1.18.106 botocore-1.17.29 catboost-0.20.2 chart-studio-1.1.0 colorama-0.4.3 combo-0.1.1 confuse-1.3.0 cufflinks-0.17.0 datefinder-0.7.0 funcy-1.14 htmlmin-0.1.12 kmodes-0.10.1 lightgbm-2.3.1 pandas-profiling-2.3.0 phik-0.10.0 pyLDAvis-2.1.2 pycaret-1.0.0 pyod-0.8.1 rsa-4.5 scikit-learn-0.22 shap-0.32.1 suod-0.0.4 yellowbrick-1.0.1 zope.interface-5.1.0\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "rsa" + ] + } + } + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LMkrqvALhtcu", + "colab_type": "text" + }, + "source": [ + "## **2. Read in dataset**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BWzXZuPMhbvs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yfBVayaJhgGg", + "colab_type": "code", + "colab": {} + }, + "source": [ + "delaney_with_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n", + "dataset = pd.read_csv(delaney_with_descriptors_url)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "DSLOSdoFh-3k", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 415 + }, + "outputId": "702e8120-df0f-4e0d-fe83-f3cca19ae045" + }, + "source": [ + "dataset" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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MolLogPMolWtNumRotatableBondsAromaticProportionlogS
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" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n", + "0 2.59540 167.850 0.0 0.000000 -2.180\n", + "1 2.37650 133.405 0.0 0.000000 -2.000\n", + "2 2.59380 167.850 1.0 0.000000 -1.740\n", + "3 2.02890 133.405 1.0 0.000000 -1.480\n", + "4 2.91890 187.375 1.0 0.000000 -3.040\n", + "... ... ... ... ... ...\n", + "1139 1.98820 287.343 8.0 0.000000 1.144\n", + "1140 3.42130 286.114 2.0 0.333333 -4.925\n", + "1141 3.60960 308.333 4.0 0.695652 -3.893\n", + "1142 2.56214 354.815 3.0 0.521739 -3.790\n", + "1143 2.02164 179.219 1.0 0.461538 -2.581\n", + "\n", + "[1144 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bay6QrUHkCyx", + "colab_type": "text" + }, + "source": [ + "## **3. Model Building**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Un3wYDLOivW3", + "colab_type": "text" + }, + "source": [ + "### **3.1. Model Setup**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0urRTofi6RSY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from pycaret.regression import *" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uxYbQPaDp4Ai", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 943, + "referenced_widgets": [ + "eced8f77156e439aa934f67a011e84b6", + "a14f8fbf57a24a748e69c6fd90a33520", + "2b2bc1ae050c4dc69d1acb9cc20df29a" + ] + }, + "outputId": "e40b1807-a2cf-46c2-afe2-55ae0a6238a1" + }, + "source": [ + "model = setup(data = dataset, target = 'logS', train_size=0.8, silent=True)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + " \n", + "Setup Succesfully Completed!\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Description Value
0session_id7903
1Transform Target False
2Transform Target MethodNone
3Original Data(1144, 5)
4Missing Values False
5Numeric Features 4
6Categorical Features 0
7Ordinal Features False
8High Cardinality Features False
9High Cardinality Method None
10Sampled Data(1144, 5)
11Transformed Train Set(915, 4)
12Transformed Test Set(229, 4)
13Numeric Imputer mean
14Categorical Imputer constant
15Normalize False
16Normalize Method None
17Transformation False
18Transformation Method None
19PCA False
20PCA Method None
21PCA Components None
22Ignore Low Variance False
23Combine Rare Levels False
24Rare Level Threshold None
25Numeric Binning False
26Remove Outliers False
27Outliers Threshold None
28Remove Multicollinearity False
29Multicollinearity Threshold None
30Clustering False
31Clustering Iteration None
32Polynomial Features False
33Polynomial Degree None
34Trignometry Features False
35Polynomial Threshold None
36Group Features False
37Feature Selection False
38Features Selection Threshold None
39Feature Interaction False
40Feature Ratio False
41Interaction Threshold None
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Model MAE MSE RMSE R2 RMSLE MAPE
0Extra Trees Regressor0.5182000.5313000.7253000.8793000.2068000.130500
1Random Forest0.5268000.5424000.7318000.8768000.2069000.119400
2CatBoost Regressor0.5435000.5472000.7357000.8759000.2103000.117100
3Light Gradient Boosting Machine0.5635000.5887000.7620000.8669000.2184000.079700
4Gradient Boosting Regressor0.5958000.6174000.7817000.8594000.2283000.059900
5Extreme Gradient Boosting0.5978000.6199000.7830000.8588000.2283000.063700
6AdaBoost Regressor0.6743000.7645000.8695000.8265000.2401000.137500
7Decision Tree0.6485000.8877000.9385000.7958000.251700-0.054400
8Linear Regression0.7647001.0139001.0016000.7670000.289000-0.212100
9Ridge Regression0.7648001.0139001.0016000.7670000.288900-0.212700
10Least Angle Regression0.7647001.0139001.0016000.7670000.289000-0.212100
11Bayesian Ridge0.7653001.0138001.0015000.7670000.288800-0.214800
12Huber Regressor0.7601001.0168001.0026000.7662000.286900-0.190400
13Random Sample Consensus0.7626001.0273001.0073000.7641000.287100-0.198000
14TheilSen Regressor0.7822001.1066001.0482000.7454000.303000-0.269000
15Elastic Net0.8618001.2584001.1130000.7132000.300000-0.189100
16Orthogonal Matching Pursuit0.8912001.3344001.1538000.6924000.347300-0.451400
17Lasso Regression0.9154001.4275001.1858000.6753000.307000-0.164900
18K Neighbors Regressor0.9286001.6480001.2753000.6250000.325400-0.053200
19Support Vector Machine1.1917002.5596001.5925000.4167000.387900-0.040700
20Lasso Least Angle Regression1.6677004.4832002.109000-0.0138000.527000-0.372900
21Passive Aggressive Regressor1.5357004.2403001.823000-0.0825000.443400-0.486800
" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X73jXIg0rmlc", + "colab_type": "text" + }, + "source": [ + "### **3.3. Model Creation**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hNtwkPitkpQM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 417, + "referenced_widgets": [ + "c7e52175a25e42aa81cea9c946cea881", + "9fed28513370419f9074fc5b22bfb314", + "94dc7fd2ad49439aad65251c47fa4108" + ] + }, + "outputId": "a0cd6a39-4a7e-4d96-85a2-eaf4bc45cfac" + }, + "source": [ + "et = create_model('et')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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MAEMSERMSER2RMSLEMAPE
00.56650.61180.78220.87640.1978-0.1375
10.51830.51340.71650.88190.2300-0.0383
20.48120.49120.70090.88950.2159-0.1843
30.51500.46970.68530.91060.1997-0.1160
40.38440.30170.54930.89740.1836-0.1667
50.54290.64080.80050.86290.20020.7580
60.50520.47600.68990.86150.1939-0.2687
70.58370.62790.79240.88070.20781.8027
80.54310.58350.76390.87520.2025-0.0889
90.54180.59650.77230.85690.2368-0.2551
Mean0.51820.53130.72530.87930.20680.1305
SD0.05280.09810.07180.01590.01560.6241
\n", + "
" + ], + "text/plain": [ + " MAE MSE RMSE R2 RMSLE MAPE\n", + "0 0.5665 0.6118 0.7822 0.8764 0.1978 -0.1375\n", + "1 0.5183 0.5134 0.7165 0.8819 0.2300 -0.0383\n", + "2 0.4812 0.4912 0.7009 0.8895 0.2159 -0.1843\n", + "3 0.5150 0.4697 0.6853 0.9106 0.1997 -0.1160\n", + "4 0.3844 0.3017 0.5493 0.8974 0.1836 -0.1667\n", + "5 0.5429 0.6408 0.8005 0.8629 0.2002 0.7580\n", + "6 0.5052 0.4760 0.6899 0.8615 0.1939 -0.2687\n", + "7 0.5837 0.6279 0.7924 0.8807 0.2078 1.8027\n", + "8 0.5431 0.5835 0.7639 0.8752 0.2025 -0.0889\n", + "9 0.5418 0.5965 0.7723 0.8569 0.2368 -0.2551\n", + "Mean 0.5182 0.5313 0.7253 0.8793 0.2068 0.1305\n", + "SD 0.0528 0.0981 0.0718 0.0159 0.0156 0.6241" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GWtYt-kcriye", + "colab_type": "text" + }, + "source": [ + "### **3.4. Model Tuning**\n", + "\n", + "The learning parameters are subjected to optimization at this phase. Here, 50 iterations is used for the optimization process and the fitness function is the Mean Absolute Error (MAE) which is the performance metric used to judge at which learning parameter settings are optimal. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aXbz_Nfyk6pw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 417, + "referenced_widgets": [ + "6bc793b5047d421fa198c5365243b141", + "d35e09ab77c04c5694ceb707d88ba915", + "4d4291ef170e4a1caf00559c3604c2b8" + ] + }, + "outputId": "8e7d30a1-ef80-4c7f-ae76-562170d56dff" + }, + "source": [ + "tuned_et = tune_model('et', n_iter = 50, optimize = 'mae')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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MAEMSERMSER2RMSLEMAPE
00.55430.54410.73760.89010.1807-0.1316
10.51610.46140.67930.89390.2226-0.1244
20.49410.52410.72390.88210.2313-0.1848
30.49170.41730.64600.92060.1973-0.0913
40.39210.27220.52170.90740.1681-0.1948
50.56240.63880.79920.86330.19340.6988
60.49510.42160.64930.87730.1902-0.2450
70.59660.65750.81080.87510.20821.8479
80.53200.54080.73540.88430.1966-0.0753
90.53330.58370.76400.85990.2327-0.2082
Mean0.51680.50610.70670.88540.20210.1291
SD0.05250.11010.08170.01770.02030.6292
\n", + "
" + ], + "text/plain": [ + " MAE MSE RMSE R2 RMSLE MAPE\n", + "0 0.5543 0.5441 0.7376 0.8901 0.1807 -0.1316\n", + "1 0.5161 0.4614 0.6793 0.8939 0.2226 -0.1244\n", + "2 0.4941 0.5241 0.7239 0.8821 0.2313 -0.1848\n", + "3 0.4917 0.4173 0.6460 0.9206 0.1973 -0.0913\n", + "4 0.3921 0.2722 0.5217 0.9074 0.1681 -0.1948\n", + "5 0.5624 0.6388 0.7992 0.8633 0.1934 0.6988\n", + "6 0.4951 0.4216 0.6493 0.8773 0.1902 -0.2450\n", + "7 0.5966 0.6575 0.8108 0.8751 0.2082 1.8479\n", + "8 0.5320 0.5408 0.7354 0.8843 0.1966 -0.0753\n", + "9 0.5333 0.5837 0.7640 0.8599 0.2327 -0.2082\n", + "Mean 0.5168 0.5061 0.7067 0.8854 0.2021 0.1291\n", + "SD 0.0525 0.1101 0.0817 0.0177 0.0203 0.6292" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "V5c1tF3rVn64", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "85d21102-2134-44f8-c15c-097e17ba9b1c" + }, + "source": [ + "print(tuned_et)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "ExtraTreesRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mae',\n", + " max_depth=40, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=280, n_jobs=None, oob_score=False,\n", + " random_state=7903, verbose=0, warm_start=False)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "if_60MOUkOtU", + "colab_type": "text" + }, + "source": [ + "### **4. Model Analysis**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dj0npLNfsR45", + "colab_type": "text" + }, + "source": [ + "#### **4.1. Plot Models**\n", + "In this tutorial, we are performing regression and so further details of the regression plots are available at https://pycaret.org/plot-model/." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eKfsk-Pdt6S7", + "colab_type": "text" + }, + "source": [ + "**Residuals Plot**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8HE-M4truI7S", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376, + "referenced_widgets": [ + "85381a580de0430d89e671b451b555d9" + ] + }, + "outputId": "f9ff095f-adcb-4d5d-cc57-8cd3a5da06d7" + }, + "source": [ + "plot_model(et, 'residuals')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7CVq2BBqmHNC", + "colab_type": "text" + }, + "source": [ + "**Model Hyperparameter**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zDblppAzlOeV", + "colab_type": "text" + }, + "source": [ + "The hyperparameter of the learning model is displayed using the ``parameter`` argument in inside the ``plot_model()`` function." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PInxfiozmKkG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 601 + }, + "outputId": "bc2815aa-666c-48eb-8f90-dbb785f706ce" + }, + "source": [ + "plot_model(et, 'parameter')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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Parameters
bootstrapFalse
ccp_alpha0
criterionmse
max_depthNone
max_featuresauto
max_leaf_nodesNone
max_samplesNone
min_impurity_decrease0
min_impurity_splitNone
min_samples_leaf1
min_samples_split2
min_weight_fraction_leaf0
n_estimators100
n_jobsNone
oob_scoreFalse
random_state7903
verbose0
warm_startFalse
\n", + "
" + ], + "text/plain": [ + " Parameters\n", + "bootstrap False\n", + "ccp_alpha 0\n", + "criterion mse\n", + "max_depth None\n", + "max_features auto\n", + "max_leaf_nodes None\n", + "max_samples None\n", + "min_impurity_decrease 0\n", + "min_impurity_split None\n", + "min_samples_leaf 1\n", + "min_samples_split 2\n", + "min_weight_fraction_leaf 0\n", + "n_estimators 100\n", + "n_jobs None\n", + "oob_score False\n", + "random_state 7903\n", + "verbose 0\n", + "warm_start False" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lsMAelymlq4O", + "colab_type": "text" + }, + "source": [ + "Here, the hyperparameter of the tuned model is displayed below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6rxAiyGclIHZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 601 + }, + "outputId": "3c703199-852c-4ebb-ff55-02ad1ec26b6a" + }, + "source": [ + "plot_model(tuned_et, 'parameter')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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Parameters
bootstrapTrue
ccp_alpha0
criterionmae
max_depth40
max_featuresauto
max_leaf_nodesNone
max_samplesNone
min_impurity_decrease0
min_impurity_splitNone
min_samples_leaf1
min_samples_split2
min_weight_fraction_leaf0
n_estimators280
n_jobsNone
oob_scoreFalse
random_state7903
verbose0
warm_startFalse
\n", + "
" + ], + "text/plain": [ + " Parameters\n", + "bootstrap True\n", + "ccp_alpha 0\n", + "criterion mae\n", + "max_depth 40\n", + "max_features auto\n", + "max_leaf_nodes None\n", + "max_samples None\n", + "min_impurity_decrease 0\n", + "min_impurity_split None\n", + "min_samples_leaf 1\n", + "min_samples_split 2\n", + "min_weight_fraction_leaf 0\n", + "n_estimators 280\n", + "n_jobs None\n", + "oob_score False\n", + "random_state 7903\n", + "verbose 0\n", + "warm_start False" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c_AUpuWCYdAR", + "colab_type": "text" + }, + "source": [ + "**Show all plots**\n", + "\n", + "The ``evaluate_model()`` displays all available plots here." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LaY-68C2XZfD", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 436, + "referenced_widgets": [ + "af0e4a8ae3d84f119be17835e7a27fcd", + "b8552ed1dcd34ec7bbcc827a406c24a0", + "e8e45aba8b454323925fe2f0dfc9474f", + "c5766cd6a91a4b9e900ffbffcba90ad6", + "cb179ca7dcb14cb8b3f80f35571c7a30", + "e6524751856d4c43b59fc04819581985", + "f42ba058176e45f6bc6d704c5a7a91b7", + "9b121cd8373c48619e0c3b31310a2d9a", + "785e9061103e49aeabd9a6faa250bd15", + "1e01cf803d4b4ba3894819a410c35ca0", + "31771bd45bc64bb1b32efa89744cc306", + "2c3119e4da3649b3a540b1a1fd70d8f8", + "6fde7b807dcf4d96ac68569b38e96e5e", + "1c00fdd68f3941d6b4fdf939e32fdafc", + "d1e8902478194513a3a4bfe922f5a534", + "dc8befe0a84d49c897b6a3afeaab2553", + "123a5da00d644a0ab4cbb298732a7467", + "976eec94a1ec4c63b6463130589f7190", + "b25f61af8f114e609d2743b28bd391a8", + "03688f0ab5a14002a66b734636b20a1d", + "49bbf31fdaf24aa7bc7f2188ed538a4c", + "162cd7de94f74e748108c9814453c8d2" + ] + }, + "outputId": "0d52ad57-2d57-4d6c-cedb-7be17039edb4" + }, + "source": [ + "evaluate_model(tuned_et)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "af0e4a8ae3d84f119be17835e7a27fcd", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y99TDL3wnEPp", + "colab_type": "text" + }, + "source": [ + "#### **4.2. Model Interpretaion**\n", + "\n", + "The ``interpret_model()`` function of PyCaret leverages the use of the SHAP library to produce stunning plots for depicting the **SHAP (SHapley Additive exPlanations)** values that was originally proposed by Lundberg and Lee in 2016.$^5$ In a nutshell, SHAP plots adds interpretability to constructed models so that the contribution of each features to the prediction can be elucidated." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OZjQ6zhcnLb1", + "colab_type": "text" + }, + "source": [ + "**Summary Plot**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J9lw_MnonNkb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236 + }, + "outputId": "ac4b2e57-7a57-4131-ed0d-c6d06d894e91" + }, + "source": [ + "interpret_model(et)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
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" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion logS Label\n", + "0 4.87188 293.414 4.0 0.545455 -5.47 -5.0870\n", + "1 2.59540 167.850 0.0 0.000000 -2.18 -1.9772\n", + "2 3.53712 162.276 0.0 0.500000 -5.23 -4.1647\n", + "3 1.34960 116.160 3.0 0.000000 -1.36 -1.3400\n", + "4 3.51410 215.362 3.0 0.000000 -3.40 -3.7009" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ARiv3f1iC565", + "colab_type": "text" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vxYjQZf7VgYz", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jwM1QHeLbxJl", + "colab_type": "text" + }, + "source": [ + "## **Reference**\n", + "\n", + "1. John S. Delaney. [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n", + "\n", + "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n", + "\n", + "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n", + "\n", + "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure.\n", + "\n", + "5. Scott M. Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), A Unified Approach to Interpreting Model Predictions, ***Advances in Neural Information Processing Systems 30 (NIPS 2017)***, 2017." + ] + } + ] +} \ No newline at end of file From c3ba93c5c2edcdd80985c5e219398f71777aa08a Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 9 Aug 2020 22:43:08 +0700 Subject: [PATCH 35/73] Add files via upload --- ...le_linear_regression_model_in_python.ipynb | 1349 +++++++++++++++++ 1 file changed, 1349 insertions(+) create mode 100644 python/How_to_build_a_simple_linear_regression_model_in_python.ipynb diff --git a/python/How_to_build_a_simple_linear_regression_model_in_python.ipynb b/python/How_to_build_a_simple_linear_regression_model_in_python.ipynb new file mode 100644 index 0000000..4049574 --- /dev/null +++ b/python/How_to_build_a_simple_linear_regression_model_in_python.ipynb @@ -0,0 +1,1349 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "How_to_build_a_simple_linear_regression_model_in_python.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OQi3X7TNUl5Y", + "colab_type": "text" + }, + "source": [ + "# **How to Build a Simple Linear Regression Model in Python** \n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor \n", + "\n", + "In this Jupyter notebook, we will building a simple linear regression model using the **Delaney Molecular Solubility** dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H661uGwCNFMC", + "colab_type": "text" + }, + "source": [ + "## **1. Retrieving the Dataset**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZkglmVcwpoXG", + "colab_type": "text" + }, + "source": [ + "### **1.1. Original dataset**\n", + "\n", + "The original [Delaney's dataset](https://pubs.acs.org/doi/10.1021/ci034243x) available as a [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x)$^4$. The full paper is entitled [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x).$^1$" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mupO58ZfpiqE", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "5NSgd-6Mol_T", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "26d12350-2f85-4955-fdcf-c7ca8366f2b4" + }, + "source": [ + "delaney_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv'\n", + "delaney_df = pd.read_csv(delaney_url)\n", + "delaney_df" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Compound IDmeasured log(solubility:mol/L)ESOL predicted log(solubility:mol/L)SMILES
01,1,1,2-Tetrachloroethane-2.180-2.794ClCC(Cl)(Cl)Cl
11,1,1-Trichloroethane-2.000-2.232CC(Cl)(Cl)Cl
21,1,2,2-Tetrachloroethane-1.740-2.549ClC(Cl)C(Cl)Cl
31,1,2-Trichloroethane-1.480-1.961ClCC(Cl)Cl
41,1,2-Trichlorotrifluoroethane-3.040-3.077FC(F)(Cl)C(F)(Cl)Cl
...............
1139vamidothion1.144-1.446CNC(=O)C(C)SCCSP(=O)(OC)(OC)
1140Vinclozolin-4.925-4.377CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C
1141Warfarin-3.893-3.913CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O
1142Xipamide-3.790-3.642Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O
1143XMC-2.581-2.688CNC(=O)Oc1cc(C)cc(C)c1
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1144 rows × 4 columns

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" + ], + "text/plain": [ + " Compound ID ... SMILES\n", + "0 1,1,1,2-Tetrachloroethane ... ClCC(Cl)(Cl)Cl\n", + "1 1,1,1-Trichloroethane ... CC(Cl)(Cl)Cl\n", + "2 1,1,2,2-Tetrachloroethane ... ClC(Cl)C(Cl)Cl\n", + "3 1,1,2-Trichloroethane ... ClCC(Cl)Cl\n", + "4 1,1,2-Trichlorotrifluoroethane ... FC(F)(Cl)C(F)(Cl)Cl\n", + "... ... ... ...\n", + "1139 vamidothion ... CNC(=O)C(C)SCCSP(=O)(OC)(OC)\n", + "1140 Vinclozolin ... CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\n", + "1141 Warfarin ... CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O \n", + "1142 Xipamide ... Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\n", + "1143 XMC ... CNC(=O)Oc1cc(C)cc(C)c1\n", + "\n", + "[1144 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "biqQ78_hqdb6", + "colab_type": "text" + }, + "source": [ + "### **1.2. Delaney dataset with computed molecular descriptors**\n", + "\n", + "As demonstrated in a previous YouTube video [Data Science for Computational Drug Discovery using Python](https://www.youtube.com/watch?v=VXFFHHoE1wk) on the Data Professor YouTube channel, SMILES notation from the Delaney dataset was used as *input* for molecular descriptor calculation using the **rdkit** Python library. This produced the 4 molecular descriptors as used by the authors in their published research article." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "olyPX1TjQMvr", + "colab_type": "text" + }, + "source": [ + "#### **Definition of variables**\n", + "\n", + "The **Y** variable (response variable) is **LogS** (log of the aqueous solubility).\n", + "\n", + "The **X** variables are comprised of 4 molecular descriptors:\n", + "1. **cLogP** *(Octanol-water partition coefficient)*\n", + "2. **MW** *(Molecular weight)*\n", + "3. **RB** *(Number of rotatable bonds)*\n", + "4. **AP** *(Aromatic proportion = number of aromatic atoms / total number of heavy atoms)*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "q9kna0SWkamZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "3d948275-2329-4cc0-b465-0198efafb183" + }, + "source": [ + "delaney_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n", + "delaney_descriptors_df = pd.read_csv(delaney_descriptors_url)\n", + "delaney_descriptors_df" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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MolLogPMolWtNumRotatableBondsAromaticProportionlogS
02.59540167.8500.00.000000-2.180
12.37650133.4050.00.000000-2.000
22.59380167.8501.00.000000-1.740
32.02890133.4051.00.000000-1.480
42.91890187.3751.00.000000-3.040
..................
11391.98820287.3438.00.0000001.144
11403.42130286.1142.00.333333-4.925
11413.60960308.3334.00.695652-3.893
11422.56214354.8153.00.521739-3.790
11432.02164179.2191.00.461538-2.581
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" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n", + "0 2.59540 167.850 0.0 0.000000 -2.180\n", + "1 2.37650 133.405 0.0 0.000000 -2.000\n", + "2 2.59380 167.850 1.0 0.000000 -1.740\n", + "3 2.02890 133.405 1.0 0.000000 -1.480\n", + "4 2.91890 187.375 1.0 0.000000 -3.040\n", + "... ... ... ... ... ...\n", + "1139 1.98820 287.343 8.0 0.000000 1.144\n", + "1140 3.42130 286.114 2.0 0.333333 -4.925\n", + "1141 3.60960 308.333 4.0 0.695652 -3.893\n", + "1142 2.56214 354.815 3.0 0.521739 -3.790\n", + "1143 2.02164 179.219 1.0 0.461538 -2.581\n", + "\n", + "[1144 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qQYE-jCRSmCn", + "colab_type": "text" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WRXEmm941h13", + "colab_type": "text" + }, + "source": [ + "## **2. Create X and Y variables**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JeSbdwYA1l3K", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "5e139655-ff97-4f95-947a-c6ecbc9c8861" + }, + "source": [ + "X = delaney_descriptors_df.drop('logS', axis=1)\n", + "X" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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MolLogPMolWtNumRotatableBondsAromaticProportion
02.59540167.8500.00.000000
12.37650133.4050.00.000000
22.59380167.8501.00.000000
32.02890133.4051.00.000000
42.91890187.3751.00.000000
...............
11391.98820287.3438.00.000000
11403.42130286.1142.00.333333
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11422.56214354.8153.00.521739
11432.02164179.2191.00.461538
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" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion\n", + "0 2.59540 167.850 0.0 0.000000\n", + "1 2.37650 133.405 0.0 0.000000\n", + "2 2.59380 167.850 1.0 0.000000\n", + "3 2.02890 133.405 1.0 0.000000\n", + "4 2.91890 187.375 1.0 0.000000\n", + "... ... ... ... ...\n", + "1139 1.98820 287.343 8.0 0.000000\n", + "1140 3.42130 286.114 2.0 0.333333\n", + "1141 3.60960 308.333 4.0 0.695652\n", + "1142 2.56214 354.815 3.0 0.521739\n", + "1143 2.02164 179.219 1.0 0.461538\n", + "\n", + "[1144 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VGwuoNKs2ReN", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "27773f04-d07f-4d6f-80b8-323944ddac7a" + }, + "source": [ + "Y = delaney_descriptors_df.logS\n", + "Y" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 -2.180\n", + "1 -2.000\n", + "2 -1.740\n", + "3 -1.480\n", + "4 -3.040\n", + " ... \n", + "1139 1.144\n", + "1140 -4.925\n", + "1141 -3.893\n", + "1142 -3.790\n", + "1143 -2.581\n", + "Name: logS, Length: 1144, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SzrfuUZNFg_X", + "colab_type": "text" + }, + "source": [ + "## **3. Data split**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dMRn8EVjFlrT", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn.model_selection import train_test_split" + ], + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "aOIAljc1FmXb", + "colab_type": "code", + "colab": {} + }, + "source": [ + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)" + ], + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "39nTAc3UFUMW", + "colab_type": "text" + }, + "source": [ + "## **4. Linear Regression Model**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "K0MokzGBCimk", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn import linear_model\n", + "from sklearn.metrics import mean_squared_error, r2_score" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vkR1siPuFZ6X", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "e20472e7-0179-419f-8dc8-8710cfd008cc" + }, + "source": [ + "model = linear_model.LinearRegression()\n", + "model.fit(X_train, Y_train)" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aG4DMzc5Rks9", + "colab_type": "text" + }, + "source": [ + "### **Predicts the X_train**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tZr9CBGvRp1F", + "colab_type": "code", + "colab": {} + }, + "source": [ + "Y_pred_train = model.predict(X_train)" + ], + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0x3saPCyRtJP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "68235d8c-e66a-4acb-e4d3-83ef26e92629" + }, + "source": [ + "print('Coefficients:', model.coef_)\n", + "print('Intercept:', model.intercept_)\n", + "print('Mean squared error (MSE): %.2f'\n", + " % mean_squared_error(Y_train, Y_pred_train))\n", + "print('Coefficient of determination (R^2): %.2f'\n", + " % r2_score(Y_train, Y_pred_train))" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Coefficients: [-0.76779153 -0.00668131 0.00654032 -0.36959403]\n", + "Intercept: 0.3108998121270652\n", + "Mean squared error (MSE): 1.01\n", + "Coefficient of determination (R^2): 0.77\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M6evZTPNRecd", + "colab_type": "text" + }, + "source": [ + "### **Predicts the X_test**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I_eFbrlaHhPU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "Y_pred_test = model.predict(X_test)" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "TQnDfyl5HkUr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "32b22212-424a-458f-8f0f-57095f5903ec" + }, + "source": [ + "print('Coefficients:', model.coef_)\n", + "print('Intercept:', model.intercept_)\n", + "print('Mean squared error (MSE): %.2f'\n", + " % mean_squared_error(Y_test, Y_pred_test))\n", + "print('Coefficient of determination (R^2): %.2f'\n", + " % r2_score(Y_test, Y_pred_test))" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Coefficients: [-0.76779153 -0.00668131 0.00654032 -0.36959403]\n", + "Intercept: 0.3108998121270652\n", + "Mean squared error (MSE): 1.00\n", + "Coefficient of determination (R^2): 0.74\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nERFfdQBRFF5", + "colab_type": "text" + }, + "source": [ + "### **Linear Regression Equation**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j3xLiGWHFiY1", + "colab_type": "text" + }, + "source": [ + "The work of Delaney$^1$ provided the following linear regression equation:\n", + "\n", + "> LogS = 0.16 - 0.63 cLogP - 0.0062 MW + 0.066 RB - 0.74 AP\n", + "\n", + "The reproduction by Pat Walters$^2$ provided the following:\n", + "\n", + "> LogS = 0.26 - 0.74 LogP - 0.0066 MW + 0.0034 RB - 0.42 AP\n", + "\n", + "This notebook's reproduction gave the following equation:\n", + "\n", + "* Based on the Train set\n", + "> LogS = 0.30 -0.75 LogP - .0066 MW -0.0041 RB - 0.36 AP\n", + "\n", + "* Based on the Full dataset\n", + "> LogS = 0.26 -0.74 LogP - 0.0066 + MW 0.0032 RB - 0.42 AP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FaWyYnMbWtYu", + "colab_type": "text" + }, + "source": [ + "#### **Our linear regression equation**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0TH6J9evHIIE", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9c960b7b-d2cf-4309-d187-60fa783a9529" + }, + "source": [ + "print('LogS = %.2f %.2f LogP %.4f MW + %.4f RB %.2f AP' % (model.intercept_, model.coef_[0], model.coef_[1], model.coef_[2], model.coef_[3] ) )" + ], + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "text": [ + "LogS = 0.31 -0.77 LogP -0.0067 MW + 0.0065 RB -0.37 AP\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VcJyUzsLSz2A", + "colab_type": "text" + }, + "source": [ + "The same equation can also be produced with the following code (which breaks up the previous one-line code into several comprehensible lines." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "byUbJ9QqK5gA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "yintercept = '%.2f' % model.intercept_\n", + "LogP = '%.2f LogP' % model.coef_[0]\n", + "MW = '%.4f MW' % model.coef_[1]\n", + "RB = '%.4f RB' % model.coef_[2]\n", + "AP = '%.2f AP' % model.coef_[3]" + ], + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QY-9rh--S-6g", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "095cefae-a92c-466d-8fb2-67abe856494f" + }, + "source": [ + "print('LogS = ' + \n", + " ' ' + \n", + " yintercept + \n", + " ' ' + \n", + " LogP + \n", + " ' ' + \n", + " MW + \n", + " ' + ' + \n", + " RB + \n", + " ' ' + \n", + " AP)" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "text": [ + "LogS = 0.31 -0.77 LogP -0.0067 MW + 0.0065 RB -0.37 AP\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R3lRkSOJRm1q", + "colab_type": "text" + }, + "source": [ + "#### **Use entire dataset for model training (For Comparison)**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QUye6SsIRl9T", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "d93a60d1-cfb8-4ddd-843c-b3bd42be4161" + }, + "source": [ + "full = linear_model.LinearRegression()\n", + "full.fit(X, Y)" + ], + "execution_count": 35, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6tMI8n0oR1b5", + "colab_type": "code", + "colab": {} + }, + "source": [ + "full_pred = model.predict(X)" + ], + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "7ZVD8Fg1R6zt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "323a7815-5a7d-49c6-cb13-ad60c7f57e69" + }, + "source": [ + "print('Coefficients:', full.coef_)\n", + "print('Intercept:', full.intercept_)\n", + "print('Mean squared error (MSE): %.2f'\n", + " % mean_squared_error(Y, full_pred))\n", + "print('Coefficient of determination (R^2): %.2f'\n", + " % r2_score(Y, full_pred))" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Coefficients: [-0.74173609 -0.00659927 0.00320051 -0.42316387]\n", + "Intercept: 0.2565006830997194\n", + "Mean squared error (MSE): 1.01\n", + "Coefficient of determination (R^2): 0.77\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AFYYzcc1VqIo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "full_yintercept = '%.2f' % full.intercept_\n", + "full_LogP = '%.2f LogP' % full.coef_[0]\n", + "full_MW = '%.4f MW' % full.coef_[1]\n", + "full_RB = '+ %.4f RB' % full.coef_[2]\n", + "full_AP = '%.2f AP' % full.coef_[3]" + ], + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zwU4QJhhVsKb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7e3042f6-6447-497b-e6a8-6b58a57599e7" + }, + "source": [ + "print('LogS = ' + \n", + " ' ' + \n", + " full_yintercept + \n", + " ' ' + \n", + " full_LogP + \n", + " ' ' + \n", + " full_MW + \n", + " ' ' + \n", + " full_RB + \n", + " ' ' + \n", + " full_AP)" + ], + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "text": [ + "LogS = 0.26 -0.74 LogP -0.0066 MW + 0.0032 RB -0.42 AP\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qp-hjUv4IWe-", + "colab_type": "text" + }, + "source": [ + "## **Scatter plot of experimental vs. predicted LogS**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q6bP41fKEY9O", + "colab_type": "text" + }, + "source": [ + "### **Quick check of the variable dimensions of Train and Test sets**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LA5dH5oiEUnP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "11d5f348-d3da-4bc3-ae52-96c6d607e14f" + }, + "source": [ + "Y_train.shape, Y_pred_train.shape" + ], + "execution_count": 41, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((915,), (915,))" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HIu7YbbFP-7o", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6460df10-4f85-4b2b-bf60-d555afb7dc41" + }, + "source": [ + "Y_test.shape, Y_pred_test.shape" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((229,), (229,))" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OHqv3TlYa5qF", + "colab_type": "text" + }, + "source": [ + "### **Vertical plot**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "shQPfrHIOmRD", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 660 + }, + "outputId": "1ddf9dac-2be1-482f-8451-3e4a978fffd0" + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure(figsize=(5,11))\n", + "\n", + "# 2 row, 1 column, plot 1\n", + "plt.subplot(2, 1, 1)\n", + "plt.scatter(x=Y_train, y=Y_pred_train, c=\"#7CAE00\", alpha=0.3)\n", + "\n", + "# Add trendline\n", + "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n", + "z = np.polyfit(Y_train, Y_pred_train, 1)\n", + "p = np.poly1d(z)\n", + "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n", + "\n", + "plt.ylabel('Predicted LogS')\n", + "\n", + "\n", + "# 2 row, 1 column, plot 2\n", + "plt.subplot(2, 1, 2)\n", + "plt.scatter(x=Y_test, y=Y_pred_test, c=\"#619CFF\", alpha=0.3)\n", + "\n", + "z = np.polyfit(Y_test, Y_pred_test, 1)\n", + "p = np.poly1d(z)\n", + "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n", + "\n", + "plt.ylabel('Predicted LogS')\n", + "plt.xlabel('Experimental LogS')\n", + "\n", + "plt.savefig('plot_vertical_logS.png')\n", + "plt.savefig('plot_vertical_logS.pdf')\n", + "plt.show()" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PswCQ7Yra_CW", + "colab_type": "text" + }, + "source": [ + "### **Horizontal plot**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xG7NWEscT8QO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 334 + }, + "outputId": "c22932ab-ba03-46d6-a83f-ae0ebe0d3dca" + }, + "source": [ + "plt.figure(figsize=(11,5))\n", + "\n", + "# 1 row, 2 column, plot 1\n", + "plt.subplot(1, 2, 1)\n", + "plt.scatter(x=Y_train, y=Y_pred_train, c=\"#7CAE00\", alpha=0.3)\n", + "\n", + "z = np.polyfit(Y_train, Y_pred_train, 1)\n", + "p = np.poly1d(z)\n", + "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n", + "\n", + "plt.ylabel('Predicted LogS')\n", + "plt.xlabel('Experimental LogS')\n", + "\n", + "# 1 row, 2 column, plot 2\n", + "plt.subplot(1, 2, 2)\n", + "plt.scatter(x=Y_test, y=Y_pred_test, c=\"#619CFF\", alpha=0.3)\n", + "\n", + "z = np.polyfit(Y_test, Y_pred_test, 1)\n", + "p = np.poly1d(z)\n", + "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n", + "\n", + "plt.xlabel('Experimental LogS')\n", + "\n", + "plt.savefig('plot_horizontal_logS.png')\n", + "plt.savefig('plot_horizontal_logS.pdf')\n", + "plt.show()" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ARiv3f1iC565", + "colab_type": "text" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jwM1QHeLbxJl", + "colab_type": "text" + }, + "source": [ + "## **Reference**\n", + "\n", + "1. John S. Delaney. [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n", + "\n", + "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n", + "\n", + "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n", + "\n", + "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure." + ] + } + ] +} \ No newline at end of file From 5c8ed10f01a14bf4f4085035142c3734931460f9 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 20 Aug 2020 13:14:28 +0700 Subject: [PATCH 36/73] Add files via upload --- streamlit/boston-house-ml-app.py | 86 ++++++++++++++++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 streamlit/boston-house-ml-app.py diff --git a/streamlit/boston-house-ml-app.py b/streamlit/boston-house-ml-app.py new file mode 100644 index 0000000..87d9bcd --- /dev/null +++ b/streamlit/boston-house-ml-app.py @@ -0,0 +1,86 @@ +import streamlit as st +import pandas as pd +import shap +import matplotlib.pyplot as plt +from sklearn import datasets +from sklearn.ensemble import RandomForestRegressor + +st.write(""" +# Boston House Price Prediction App + +This app predicts the **Boston House Price**! +""") +st.write('---') + +# Loads the Boston House Price Dataset +boston = datasets.load_boston() +X = pd.DataFrame(boston.data, columns=boston.feature_names) +Y = pd.DataFrame(boston.target, columns=["MEDV"]) + +# Sidebar +# Header of Specify Input Parameters +st.sidebar.header('Specify Input Parameters') + +def user_input_features(): + CRIM = st.sidebar.slider('CRIM', X.CRIM.min(), X.CRIM.max(), X.CRIM.mean()) + ZN = st.sidebar.slider('ZN', X.ZN.min(), X.ZN.max(), X.ZN.mean()) + INDUS = st.sidebar.slider('INDUS', X.INDUS.min(), X.INDUS.max(), X.INDUS.mean()) + CHAS = st.sidebar.slider('CHAS', X.CHAS.min(), X.CHAS.max(), X.CHAS.mean()) + NOX = st.sidebar.slider('NOX', X.NOX.min(), X.NOX.max(), X.NOX.mean()) + RM = st.sidebar.slider('RM', X.RM.min(), X.RM.max(), X.RM.mean()) + AGE = st.sidebar.slider('AGE', X.AGE.min(), X.AGE.max(), X.AGE.mean()) + DIS = st.sidebar.slider('DIS', X.DIS.min(), X.DIS.max(), X.DIS.mean()) + RAD = st.sidebar.slider('RAD', X.RAD.min(), X.RAD.max(), X.RAD.mean()) + TAX = st.sidebar.slider('TAX', X.TAX.min(), X.TAX.max(), X.TAX.mean()) + PTRATIO = st.sidebar.slider('PTRATIO', X.PTRATIO.min(), X.PTRATIO.max(), X.PTRATIO.mean()) + B = st.sidebar.slider('B', X.B.min(), X.B.max(), X.B.mean()) + LSTAT = st.sidebar.slider('LSTAT', X.LSTAT.min(), X.LSTAT.max(), X.LSTAT.mean()) + data = {'CRIM': CRIM, + 'ZN': ZN, + 'INDUS': INDUS, + 'CHAS': CHAS, + 'NOX': NOX, + 'RM': RM, + 'AGE': AGE, + 'DIS': DIS, + 'RAD': RAD, + 'TAX': TAX, + 'PTRATIO': PTRATIO, + 'B': B, + 'LSTAT': LSTAT} + features = pd.DataFrame(data, index=[0]) + return features + +df = user_input_features() + +# Main Panel + +# Print specified input parameters +st.header('Specified Input parameters') +st.write(df) +st.write('---') + +# Build Regression Model +model = RandomForestRegressor() +model.fit(X, Y) +# Apply Model to Make Prediction +prediction = model.predict(df) + +st.header('Prediction of MEDV') +st.write(prediction) +st.write('---') + +# Explaining the model's predictions using SHAP values +# https://github.com/slundberg/shap +explainer = shap.TreeExplainer(model) +shap_values = explainer.shap_values(X) + +st.header('Feature Importance') +plt.title('Feature importance based on SHAP values') +shap.summary_plot(shap_values, X) +st.pyplot(bbox_inches='tight') +st.write('---') + +plt.title('Feature importance based on SHAP values (Bar)') +shap.summary_plot(shap_values, X, plot_type="bar") +st.pyplot(bbox_inches='tight') From 988c7337baa3d3874aaefae798f8785ea0430770 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Thu, 20 Aug 2020 13:14:49 +0700 Subject: [PATCH 37/73] Rename streamlit/boston-house-ml-app.py to streamlit/part6/boston-house-ml-app.py --- streamlit/{ => part6}/boston-house-ml-app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part6}/boston-house-ml-app.py (100%) diff --git a/streamlit/boston-house-ml-app.py b/streamlit/part6/boston-house-ml-app.py similarity index 100% rename from streamlit/boston-house-ml-app.py rename to streamlit/part6/boston-house-ml-app.py From 2d8dfdb2678b201e3187fa412cb24e65acd9a0dd Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 6 Sep 2020 23:00:35 +0700 Subject: [PATCH 38/73] Add files via upload --- python/Hummingbird_ML.ipynb | 360 ++++++++++++++++++++++++++++++++++++ 1 file changed, 360 insertions(+) create mode 100644 python/Hummingbird_ML.ipynb diff --git a/python/Hummingbird_ML.ipynb b/python/Hummingbird_ML.ipynb new file mode 100644 index 0000000..2e7d19f --- /dev/null +++ b/python/Hummingbird_ML.ipynb @@ -0,0 +1,360 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Hummingbird-ML.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "IpOdlr3WAPHJ", + "colab_type": "text" + }, + "source": [ + "# **Hummingbird-ML**\n", + "\n", + "[How to Harness GPU to Speed Up Machine Learning with Hummingbird-ML](https://www.youtube.com/watch?v=qN8jcUmo8TI)\n", + "\n", + "Adapted from: https://github.com/microsoft/hummingbird" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ir3DZd5-_jiu", + "colab_type": "text" + }, + "source": [ + "# Install Hummingbird-ML" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ra3JEgWN_bfp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408 + }, + "outputId": "4fae39de-26f0-4939-846d-039fb876725a" + }, + "source": [ + "! pip install hummingbird-ml[extra]" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting hummingbird-ml[extra]\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/ed/3b/cf1b8c1e7531377adead8de29e29b00b5aed380544ad0def4c0188b50d80/hummingbird_ml-0.0.5-py2.py3-none-any.whl (60kB)\n", + "\r\u001b[K |█████▌ | 10kB 16.6MB/s eta 0:00:01\r\u001b[K |███████████ | 20kB 1.8MB/s eta 0:00:01\r\u001b[K |████████████████▍ | 30kB 2.2MB/s eta 0:00:01\r\u001b[K |█████████████████████▉ | 40kB 2.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████▎ | 51kB 2.0MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61kB 1.8MB/s \n", + "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (1.18.5)\n", + "Requirement already satisfied: torch>=1.4.* in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (1.6.0+cu101)\n", + "Collecting onnxconverter-common>=1.6.0\n", + "\u001b[?25l Downloading 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onnx->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (1.15.0)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (49.6.0)\n", + "Installing collected packages: onnx, onnxconverter-common, hummingbird-ml\n", + "Successfully installed hummingbird-ml-0.0.5 onnx-1.7.0 onnxconverter-common-1.7.0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YnA-PmeA_q70", + "colab_type": "text" + }, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lkIThThi_puf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from hummingbird.ml import convert" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFw_4cGa_-tF", + "colab_type": "text" + }, + "source": [ + "# Create some random data for binary classification" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hGGngPPp__mx", + "colab_type": "code", + "colab": {} + }, + "source": [ + "num_classes = 2\n", + "X = np.random.rand(100000, 28)\n", + "y = np.random.randint(num_classes, size=100000)" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WusxNKH4AHII", + "colab_type": "text" + }, + "source": [ + "# Create and train a model (scikit-learn RandomForestClassifier)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GMRJRuBwAGeV", + "colab_type": "code", + "colab": {} + }, + "source": [ + "skl_model = RandomForestClassifier(n_estimators=10, max_depth=10)" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "M_kGo80yAYTn", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "aa863652-02f8-4578-8fb7-e3b028685cd7" + }, + "source": [ + "%%timeit\n", + "skl_model.fit(X, y)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1 loop, best of 3: 4.78 s per loop\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Hp4a8I0tAbBl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "4e083fd5-981f-4238-9158-3f4500585560" + }, + "source": [ + "%%timeit\n", + "skl_model.predict(X)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "10 loops, best of 3: 85.6 ms per loop\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mNiBvy9BA7wR", + "colab_type": "text" + }, + "source": [ + "# Use Hummingbird to convert the model to PyTorch" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vcAOpuxxAzPc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model = convert(skl_model, 'pytorch')" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dpt6_4l8BF7e", + "colab_type": "text" + }, + "source": [ + "# Run predictions on CPU" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_BiU63hNBDu-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1bd8b158-a62b-4fe0-be09-ca382c817247" + }, + "source": [ + "%%timeit\n", + "model.predict(X)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1 loop, best of 3: 174 ms per loop\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F10tJEMKBPZG", + "colab_type": "text" + }, + "source": [ + "# Run predictions on GPU" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l2PUbqoHBJBX", + "colab_type": "code", + "colab": {} + }, + "source": [ + "model.to('cuda')" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-AB23_VTBRMP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "b9efea7d-913c-4326-c14a-6b6ca0e9c063" + }, + "source": [ + "%%timeit\n", + "model.predict(X)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "The slowest run took 5.22 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "100 loops, best of 3: 14.8 ms per loop\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dbkQU69JDt7T", + "colab_type": "text" + }, + "source": [ + "# Calculation Time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hr1R_9nwDwpc", + "colab_type": "text" + }, + "source": [ + "Methods | Timing | Performance\n", + "--|--|--\n", + "scikit-learn | 85.6 ms | -\n", + "PyTorch (CPU) | 174 ms | 2 X slower than scikit-learn\n", + "PyTorch (GPU) | 14.8 ms | Almost 6 X faster than scikit-learn; Almost 12 X faster than PyTorch (CPU)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9lmR3LHoEzhl", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 4cff6de4938580932383557042a8f72334e2cbe3 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 6 Sep 2020 23:01:55 +0700 Subject: [PATCH 39/73] Add files via upload From 09d4a36e564f5d41701f675c8968def2f948a66e Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 16 Sep 2020 00:27:33 +0700 Subject: [PATCH 40/73] Add files via upload --- streamlit/solubility-web-app.ipynb | 714 +++++++++++++++++++++++++++++ 1 file changed, 714 insertions(+) create mode 100644 streamlit/solubility-web-app.ipynb diff --git a/streamlit/solubility-web-app.ipynb b/streamlit/solubility-web-app.ipynb new file mode 100644 index 0000000..d050266 --- /dev/null +++ b/streamlit/solubility-web-app.ipynb @@ -0,0 +1,714 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Streamlit-Solubility.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "QQHZHevuXdEy", + "colab_type": "text" + }, + "source": [ + "# **Model Building for Solubility Dataset**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "*Data Professor YouTube channel, http://youtube.com/dataprofessor*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g1qtHa0zXfWM", + "colab_type": "text" + }, + "source": [ + "# Read in data" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9MdfbvFKXtXq", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nerGP0fCXfgP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "2bb155a6-2710-4461-accb-df64045ba70d" + }, + "source": [ + "delaney_with_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n", + "dataset = pd.read_csv(delaney_with_descriptors_url)\n", + "dataset" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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MolLogPMolWtNumRotatableBondsAromaticProportionlogS
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" + ], + "text/plain": [ + " MolLogP MolWt NumRotatableBonds AromaticProportion\n", + "0 2.59540 167.850 0.0 0.000000\n", + "1 2.37650 133.405 0.0 0.000000\n", + "2 2.59380 167.850 1.0 0.000000\n", + "3 2.02890 133.405 1.0 0.000000\n", + "4 2.91890 187.375 1.0 0.000000\n", + "... ... ... ... ...\n", + "1139 1.98820 287.343 8.0 0.000000\n", + "1140 3.42130 286.114 2.0 0.333333\n", + "1141 3.60960 308.333 4.0 0.695652\n", + "1142 2.56214 354.815 3.0 0.521739\n", + "1143 2.02164 179.219 1.0 0.461538\n", + "\n", + "[1144 rows x 4 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JDwxgKHqYmD4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "a725d7b7-baad-4a99-9686-4dfe1d852c22" + }, + "source": [ + "Y = dataset.iloc[:,-1]\n", + "Y" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 -2.180\n", + "1 -2.000\n", + "2 -1.740\n", + "3 -1.480\n", + "4 -3.040\n", + " ... \n", + "1139 1.144\n", + "1140 -4.925\n", + "1141 -3.893\n", + "1142 -3.790\n", + "1143 -2.581\n", + "Name: logS, Length: 1144, dtype: float64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LNohCdqQY5VZ", + "colab_type": "text" + }, + "source": [ + "# Linear Regression Model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EanoyG2eX9cV", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from sklearn import linear_model\n", + "from sklearn.metrics import mean_squared_error, r2_score" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "mLQJ2KLLY_9a", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6349fa74-f087-4d81-916e-294789c6455c" + }, + "source": [ + "model = linear_model.LinearRegression()\n", + "model.fit(X, Y)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F5f8KGWjZRSc", + "colab_type": "text" + }, + "source": [ + "## Model Prediction" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MI3c8LB2ZCYW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "19b50c6a-7d1c-4bfd-8789-d5884b42d594" + }, + "source": [ + "Y_pred = model.predict(X)\n", + "Y_pred" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-2.77628837, -2.38661054, -2.77190108, ..., -4.73721496,\n", + " -4.19663007, -2.61784284])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fXv7bcolZqa-", + "colab_type": "text" + }, + "source": [ + "## Model Performance" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6f13gYleZVKy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "99894d58-83b4-4b64-f54c-848e8430cd5e" + }, + "source": [ + "print('Coefficients:', model.coef_)\n", + "print('Intercept:', model.intercept_)\n", + "print('Mean squared error (MSE): %.2f'\n", + " % mean_squared_error(Y, Y_pred))\n", + "print('Coefficient of determination (R^2): %.2f'\n", + " % r2_score(Y, Y_pred))" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Coefficients: [-0.74173609 -0.00659927 0.00320051 -0.42316387]\n", + "Intercept: 0.2565006830997194\n", + "Mean squared error (MSE): 1.01\n", + "Coefficient of determination (R^2): 0.77\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yhuc402dZsk3", + "colab_type": "text" + }, + "source": [ + "## Model Equation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QnoUESmXZcMo", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c2e3b76f-4d9a-425c-99e4-5793dc6c1620" + }, + "source": [ + "print('LogS = %.2f %.2f LogP %.4f MW + %.4f RB %.2f AP' % (model.intercept_, model.coef_[0], model.coef_[1], model.coef_[2], model.coef_[3] ) )" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "LogS = 0.26 -0.74 LogP -0.0066 MW + 0.0032 RB -0.42 AP\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uWvxj1iSaL3n", + "colab_type": "text" + }, + "source": [ + "# Data Visualization (Experimental vs Predicted LogS for Training Data)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iPcFF0MjZlh8", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "QRNyIlGAaQQI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "outputId": "949bd284-5952-496f-a57e-47a1333fd50b" + }, + "source": [ + "plt.figure(figsize=(5,5))\n", + "plt.scatter(x=Y, y=Y_pred, c=\"#7CAE00\", alpha=0.3)\n", + "\n", + "# Add trendline\n", + "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n", + "z = np.polyfit(Y, Y_pred, 1)\n", + "p = np.poly1d(z)\n", + "\n", + "plt.plot(Y,p(Y),\"#F8766D\")\n", + "plt.ylabel('Predicted LogS')\n", + "plt.xlabel('Experimental LogS')" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 0, 'Experimental LogS')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YzKTmvZrbFVI", + "colab_type": "text" + }, + "source": [ + "# Save Model as Pickle Object" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DzjpPyVyb8XO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pickle" + ], + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "b2K9ajBaaYUk", + "colab_type": "code", + "colab": {} + }, + "source": [ + "pickle.dump(model, open('solubility_model.pkl', 'wb'))" + ], + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ef4fyvrEb-NC", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 495a750f862ea990179e1e77c430192a3632c108 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 16 Sep 2020 00:28:13 +0700 Subject: [PATCH 41/73] Update and rename streamlit/solubility-web-app.ipynb to streamlit/part7/solubility-web-app.ipynb --- streamlit/{ => part7}/solubility-web-app.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename streamlit/{ => part7}/solubility-web-app.ipynb (99%) diff --git a/streamlit/solubility-web-app.ipynb b/streamlit/part7/solubility-web-app.ipynb similarity index 99% rename from streamlit/solubility-web-app.ipynb rename to streamlit/part7/solubility-web-app.ipynb index d050266..8a480c5 100644 --- a/streamlit/solubility-web-app.ipynb +++ b/streamlit/part7/solubility-web-app.ipynb @@ -3,7 +3,7 @@ "nbformat_minor": 0, "metadata": { "colab": { - "name": "Streamlit-Solubility.ipynb", + "name": "solubility-web-app.ipynb", "provenance": [], "toc_visible": true }, @@ -711,4 +711,4 @@ "outputs": [] } ] -} \ No newline at end of file +} From 48b3f462eb05074107d1ec39e1e86a5812276510 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 16 Sep 2020 00:29:01 +0700 Subject: [PATCH 42/73] Add files via upload --- streamlit/part7/solubility-app.py | 110 +++++++++++++++++++++++++++ streamlit/part7/solubility-logo.jpg | Bin 0 -> 218579 bytes streamlit/part7/solubility_model.pkl | Bin 0 -> 569 bytes 3 files changed, 110 insertions(+) create mode 100644 streamlit/part7/solubility-app.py create mode 100644 streamlit/part7/solubility-logo.jpg create mode 100644 streamlit/part7/solubility_model.pkl diff --git a/streamlit/part7/solubility-app.py b/streamlit/part7/solubility-app.py new file mode 100644 index 0000000..596c595 --- /dev/null +++ b/streamlit/part7/solubility-app.py @@ -0,0 +1,110 @@ +###################### +# Import libraries +###################### +import numpy as np +import pandas as pd +import streamlit as st +import pickle +from PIL import Image +from rdkit import Chem +from rdkit.Chem import Descriptors + +###################### +# Custom function +###################### +## Calculate molecular descriptors +def AromaticProportion(m): + aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())] + aa_count = [] + for i in aromatic_atoms: + if i==True: + aa_count.append(1) + AromaticAtom = sum(aa_count) + HeavyAtom = Descriptors.HeavyAtomCount(m) + AR = AromaticAtom/HeavyAtom + return AR + +def generate(smiles, verbose=False): + + moldata= [] + for elem in smiles: + mol=Chem.MolFromSmiles(elem) + moldata.append(mol) + + baseData= np.arange(1,1) + i=0 + for mol in moldata: + + desc_MolLogP = Descriptors.MolLogP(mol) + desc_MolWt = Descriptors.MolWt(mol) + desc_NumRotatableBonds = Descriptors.NumRotatableBonds(mol) + desc_AromaticProportion = AromaticProportion(mol) + + row = np.array([desc_MolLogP, + desc_MolWt, + desc_NumRotatableBonds, + desc_AromaticProportion]) + + if(i==0): + baseData=row + else: + baseData=np.vstack([baseData, row]) + i=i+1 + + columnNames=["MolLogP","MolWt","NumRotatableBonds","AromaticProportion"] + descriptors = pd.DataFrame(data=baseData,columns=columnNames) + + return descriptors + +###################### +# Page Title +###################### + +image = Image.open('solubility-logo.jpg') + +st.image(image, use_column_width=True) + +st.write(""" +# Molecular Solubility Prediction Web App + +This app predicts the **Solubility (LogS)** values of molecules! + +Data obtained from the John S. Delaney. [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005. +*** +""") + + +###################### +# Input molecules (Side Panel) +###################### + +st.sidebar.header('User Input Features') + +## Read SMILES input +SMILES_input = "NCCCC\nCCC\nCN" + +SMILES = st.sidebar.text_area("SMILES input", SMILES_input) +SMILES = "C\n" + SMILES #Adds C as a dummy, first item +SMILES = SMILES.split('\n') + +st.header('Input SMILES') +SMILES[1:] # Skips the dummy first item + +## Calculate molecular descriptors +st.header('Computed molecular descriptors') +X = generate(SMILES) +X[1:] # Skips the dummy first item + +###################### +# Pre-built model +###################### + +# Reads in saved model +load_model = pickle.load(open('solubility_model.pkl', 'rb')) + +# Apply model to make predictions +prediction = load_model.predict(X) +#prediction_proba = load_model.predict_proba(X) + +st.header('Predicted LogS values') +prediction[1:] # Skips the dummy first item diff --git a/streamlit/part7/solubility-logo.jpg 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a/streamlit/part7/solubility_model.pkl b/streamlit/part7/solubility_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1a13992e80aed053ae274c53a5ecbc6008c20001 GIT binary patch literal 569 zcmZo*PA<;QNlh%u)62=s1Jd!i`6;P6dhtn##i?9AV4`R!u0jUQ#zMy0 zLMDv}UIqq+w9JzD%)FA+qU6+ql0xQ=2u`42UVc$-VoqjNY9UK!1RGEwIlrJXKBADd z1I){d&&p3KE@bnIU>mSapk2zWD7aH8CnauoEeh}x!VhQf(m&xycxY&S_}E03R6ld3sSiX`6HM> zZnY>B@MiF4Y%dfHDiqT2W{zM4irN$k`}z5K{RaXt;muG|D3a9atT4+yJJ@&6^Zmbf zZ(negy?X!J{S)_mG0(DB-W2^*R`mA%LeY{!u_UmEfvzmhOesw*E)>V%kK*LSoWvro zLWv9^XO8LrW7d4&p5#y{*;XhOR45G&hoZ#1?D#?%Zx*nL#hH2Or9d6=g|ZnO8DMY9 zWdQvsA5^FS^`l~j5YUwxPV*a!rB^$o^nZVFwjjje+vUBHFQ0fj?3!}rrt$_AheD;2 zLgl0gE}&20@f2UEk^y#zg+|Er#;gnWg{p0ZYC(nS5duJE@zCTDUzS<~it<8@2p*uY Vfu50(o{?TbesPImp=N249sm#<#G3#B literal 0 HcmV?d00001 From 1cc839cdb83e0958f63616a5db0f37dae9ea9fac Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 30 Sep 2020 19:23:37 +0700 Subject: [PATCH 43/73] Add files via upload --- streamlit/dna-app.py | 95 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 streamlit/dna-app.py diff --git a/streamlit/dna-app.py b/streamlit/dna-app.py new file mode 100644 index 0000000..1836cea --- /dev/null +++ b/streamlit/dna-app.py @@ -0,0 +1,95 @@ +###################### +# Import libraries +###################### + +import pandas as pd +import streamlit as st +import altair as alt +from PIL import Image + +###################### +# Page Title +###################### + +image = Image.open('dna-logo.jpg') + +st.image(image, use_column_width=True) + +st.write(""" +# DNA Nucleotide Count Web App + +This app counts the nucleotide composition of query DNA! + +*** +""") + + +###################### +# Input Text Box +###################### + +#st.sidebar.header('Enter DNA sequence') +st.header('Enter DNA sequence') + +sequence_input = ">DNA Query 2\nGAACACGTGGAGGCAAACAGGAAGGTGAAGAAGAACTTATCCTATCAGGACGGAAGGTCCTGTGCTCGGG\nATCTTCCAGACGTCGCGACTCTAAATTGCCCCCTCTGAGGTCAAGGAACACAAGATGGTTTTGGAAATGC\nTGAACCCGATACATTATAACATCACCAGCATCGTGCCTGAAGCCATGCCTGCTGCCACCATGCCAGTCCT" + +#sequence = st.sidebar.text_area("Sequence input", sequence_input, height=250) +sequence = st.text_area("Sequence input", sequence_input, height=250) +sequence = sequence.splitlines() +sequence = sequence[1:] # Skips the sequence name (first line) +sequence = ''.join(sequence) # Concatenates list to string + +st.write(""" +*** +""") + +## Prints the input DNA sequence +st.header('INPUT (DNA Query)') +sequence + +## DNA nucleotide count +st.header('OUTPUT (DNA Nucleotide Count)') + +### 1. Print dictionary +st.subheader('1. Print dictionary') +def DNA_nucleotide_count(seq): + d = dict([ + ('A',seq.count('A')), + ('T',seq.count('T')), + ('G',seq.count('G')), + ('C',seq.count('C')) + ]) + return d + +X = DNA_nucleotide_count(sequence) + +#X_label = list(X) +#X_values = list(X.values()) + +X + +### 2. Print text +st.subheader('2. Print text') +st.write('There are ' + str(X['A']) + ' adenine (A)') +st.write('There are ' + str(X['T']) + ' thymine (T)') +st.write('There are ' + str(X['G']) + ' guanine (G)') +st.write('There are ' + str(X['C']) + ' cytosine (C)') + +### 3. Display DataFrame +st.subheader('3. Display DataFrame') +df = pd.DataFrame.from_dict(X, orient='index') +df = df.rename({0: 'count'}, axis='columns') +df.reset_index(inplace=True) +df = df.rename(columns = {'index':'nucleotide'}) +st.write(df) + +### 4. Display Bar Chart using Altair +st.subheader('4. Display Bar chart') +p = alt.Chart(df).mark_bar().encode( + x='nucleotide', + y='count' +) +p = p.properties( + width=alt.Step(80) # controls width of bar. +) +st.write(p) From 9b27d61854060d8e339b821521447e912ced8206 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 30 Sep 2020 19:24:18 +0700 Subject: [PATCH 44/73] Rename streamlit/dna-app.py to streamlit/part8/dna-app.py --- streamlit/{ => part8}/dna-app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part8}/dna-app.py (100%) diff --git a/streamlit/dna-app.py b/streamlit/part8/dna-app.py similarity index 100% rename from streamlit/dna-app.py rename to streamlit/part8/dna-app.py From 8e3d7c867c599aff27b0cc72eb41248b705fff10 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 30 Sep 2020 19:25:05 +0700 Subject: [PATCH 45/73] Add files via upload --- streamlit/part8/dna-logo.jpg | Bin 0 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zQ}B-0JOO|^>~D}QR{&(cK=zm9Xom~O8#))g!W&&%?g>RPP4bi_ccSB*CM3i#>NMsN(ZJ$qDZU);@sG gfcoEEG}WWhemLrH@bdo#|Ca*)rNHkg0QxchKih*j0RR91 From 5db19a014a5086d3dd6ce39b3041b8b28cf90834 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 6 Oct 2020 12:40:19 +0700 Subject: [PATCH 48/73] Add files via upload --- football_app.py | 69 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 football_app.py diff --git a/football_app.py b/football_app.py new file mode 100644 index 0000000..6451409 --- /dev/null +++ b/football_app.py @@ -0,0 +1,69 @@ +import streamlit as st +import pandas as pd +import base64 +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np + +st.title('NFL Football Stats (Rushing) Explorer') + +st.markdown(""" +This app performs simple webscraping of NFL Football player stats data (focusing on Rushing)! +* **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn +* **Data source:** [pro-football-reference.com](https://www.pro-football-reference.com/). +""") + +st.sidebar.header('User Input Features') +selected_year = st.sidebar.selectbox('Year', list(reversed(range(1990,2020)))) + +# Web scraping of NFL player stats +# https://www.pro-football-reference.com/years/2019/rushing.htm +@st.cache +def load_data(year): + url = "https://www.pro-football-reference.com/years/" + str(year) + "/rushing.htm" + html = pd.read_html(url, header = 1) + df = html[0] + raw = df.drop(df[df.Age == 'Age'].index) # Deletes repeating headers in content + raw = raw.fillna(0) + playerstats = raw.drop(['Rk'], axis=1) + return playerstats +playerstats = load_data(selected_year) + +# Sidebar - Team selection +sorted_unique_team = sorted(playerstats.Tm.unique()) +selected_team = st.sidebar.multiselect('Team', sorted_unique_team, sorted_unique_team) + +# Sidebar - Position selection +unique_pos = ['RB','QB','WR','FB','TE'] +selected_pos = st.sidebar.multiselect('Position', unique_pos, unique_pos) + +# Filtering data +df_selected_team = playerstats[(playerstats.Tm.isin(selected_team)) & (playerstats.Pos.isin(selected_pos))] + +st.header('Display Player Stats of Selected Team(s)') +st.write('Data Dimension: ' + str(df_selected_team.shape[0]) + ' rows and ' + str(df_selected_team.shape[1]) + ' columns.') +st.dataframe(df_selected_team) + +# Download NBA player stats data +# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806 +def filedownload(df): + csv = df.to_csv(index=False) + b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions + href = f'Download CSV File' + return href + +st.markdown(filedownload(df_selected_team), unsafe_allow_html=True) + +# Heatmap +if st.button('Intercorrelation Heatmap'): + st.header('Intercorrelation Matrix Heatmap') + df_selected_team.to_csv('output.csv',index=False) + df = pd.read_csv('output.csv') + + corr = df.corr() + mask = np.zeros_like(corr) + mask[np.triu_indices_from(mask)] = True + with sns.axes_style("white"): + f, ax = plt.subplots(figsize=(7, 5)) + ax = sns.heatmap(corr, mask=mask, vmax=1, square=True) + st.pyplot() From a901c27560599f7c946f7363e2bbc4d8aa62407f Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 6 Oct 2020 12:44:47 +0700 Subject: [PATCH 49/73] Rename football_app.py to streamlit/part9/football_app.py --- football_app.py => streamlit/part9/football_app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename football_app.py => streamlit/part9/football_app.py (100%) diff --git a/football_app.py b/streamlit/part9/football_app.py similarity index 100% rename from football_app.py rename to streamlit/part9/football_app.py From 74cdd6d25c245bccb456f69806df8a412cf388bd Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 11 Oct 2020 14:18:21 +0700 Subject: [PATCH 50/73] Add files via upload --- streamlit/sp500-app.py | 82 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 streamlit/sp500-app.py diff --git a/streamlit/sp500-app.py b/streamlit/sp500-app.py new file mode 100644 index 0000000..9e979ea --- /dev/null +++ b/streamlit/sp500-app.py @@ -0,0 +1,82 @@ +import streamlit as st +import pandas as pd +import base64 +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +import yfinance as yf + +st.title('S&P 500 App') + +st.markdown(""" +This app retrieves the list of the **S&P 500** (from Wikipedia) and its corresponding **stock closing price** (year-to-date)! +* **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn +* **Data source:** [Wikipedia](https://www.wikipedia.org/). +""") + +st.sidebar.header('User Input Features') + +# Web scraping of S&P 500 data +# +@st.cache +def load_data(): + url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies' + html = pd.read_html(url, header = 0) + df = html[0] + return df + +df = load_data() +sector = df.groupby('GICS Sector') + +# Sidebar - Sector selection +sorted_sector_unique = sorted( df['GICS Sector'].unique() ) +selected_sector = st.sidebar.multiselect('Sector', sorted_sector_unique, sorted_sector_unique) + +# Filtering data +df_selected_sector = df[ (df['GICS Sector'].isin(selected_sector)) ] + +st.header('Display Companies in Selected Sector') +st.write('Data Dimension: ' + str(df_selected_sector.shape[0]) + ' rows and ' + str(df_selected_sector.shape[1]) + ' columns.') +st.dataframe(df_selected_sector) + +# Download S&P500 data +# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806 +def filedownload(df): + csv = df.to_csv(index=False) + b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions + href = f'Download CSV File' + return href + +st.markdown(filedownload(df_selected_sector), unsafe_allow_html=True) + +# https://pypi.org/project/yfinance/ + +data = yf.download( + tickers = list(df_selected_sector[:10].Symbol), + period = "ytd", + interval = "1d", + group_by = 'ticker', + auto_adjust = True, + prepost = True, + threads = True, + proxy = None + ) + +# Plot Closing Price of Query Symbol +def price_plot(symbol): + df = pd.DataFrame(data[symbol].Close) + df['Date'] = df.index + plt.fill_between(df.Date, df.Close, color='skyblue', alpha=0.3) + plt.plot(df.Date, df.Close, color='skyblue', alpha=0.8) + plt.xticks(rotation=90) + plt.title(symbol, fontweight='bold') + plt.xlabel('Date', fontweight='bold') + plt.ylabel('Closing Price', fontweight='bold') + return st.pyplot() + +num_company = st.sidebar.slider('Number of Companies', 1, 5) + +if st.button('Show Plots'): + st.header('Stock Closing Price') + for i in list(df_selected_sector.Symbol)[:num_company]: + price_plot(i) From 2992998aff6b940041b1b00d95bdd680cfacc7cf Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 11 Oct 2020 14:19:32 +0700 Subject: [PATCH 51/73] Rename streamlit/sp500-app.py to streamlit/part10/sp500-app.py --- streamlit/{ => part10}/sp500-app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{ => part10}/sp500-app.py (100%) diff --git a/streamlit/sp500-app.py b/streamlit/part10/sp500-app.py similarity index 100% rename from streamlit/sp500-app.py rename to streamlit/part10/sp500-app.py From 1cb32832b3326fdfa0e2c4c61528ffd56e80776d Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 11 Oct 2020 14:19:59 +0700 Subject: [PATCH 52/73] Update sp500-app.py --- streamlit/part10/sp500-app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/streamlit/part10/sp500-app.py b/streamlit/part10/sp500-app.py index 9e979ea..749fa34 100644 --- a/streamlit/part10/sp500-app.py +++ b/streamlit/part10/sp500-app.py @@ -11,7 +11,7 @@ st.markdown(""" This app retrieves the list of the **S&P 500** (from Wikipedia) and its corresponding **stock closing price** (year-to-date)! * **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn -* **Data source:** [Wikipedia](https://www.wikipedia.org/). +* **Data source:** [Wikipedia](https://en.wikipedia.org/wiki/List_of_S%26P_500_companies). """) st.sidebar.header('User Input Features') From 778479f34289b96e45ca3cbe6ab9fcedd1649e97 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 18 Oct 2020 21:47:52 +0700 Subject: [PATCH 53/73] Add files via upload --- streamlit/crypto-price-app.py | 168 ++++++++++++++++++++++++++++++++++ 1 file changed, 168 insertions(+) create mode 100644 streamlit/crypto-price-app.py diff --git a/streamlit/crypto-price-app.py b/streamlit/crypto-price-app.py new file mode 100644 index 0000000..4b15eb2 --- /dev/null +++ b/streamlit/crypto-price-app.py @@ -0,0 +1,168 @@ +import streamlit as st +from PIL import Image +import pandas as pd +import base64 +import matplotlib.pyplot as plt +from bs4 import BeautifulSoup +import requests +import json +import time +#---------------------------------# +# New feature (make sure to upgrade your streamlit library) +# pip install --upgrade streamlit + +#---------------------------------# +# Page layout +## Page expands to full width +st.beta_set_page_config(layout="wide") +#---------------------------------# +# Title + +image = Image.open('logo.jpg') + +st.image(image, width = 500) + +st.title('Crypto Price App') +st.markdown(""" +This app retrieves cryptocurrency prices for the top 100 cryptocurrency from the **CoinMarketCap**! + +""") +#---------------------------------# +# About +expander_bar = st.beta_expander("About") +expander_bar.markdown(""" +* **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn, BeautifulSoup, requests, json, time +* **Data source:** [CoinMarketCap](http://coinmarketcap.com). +* **Credit:** Web scraper adapted from the Medium article *[Web Scraping Crypto Prices With Python](https://towardsdatascience.com/web-scraping-crypto-prices-with-python-41072ea5b5bf)* written by [Bryan Feng](https://medium.com/@bryanf). +""") + + +#---------------------------------# +# Page layout (continued) +## Divide page to 3 columns (col1 = sidebar, col2 and col3 = page contents) +col1 = st.sidebar +col2, col3 = st.beta_columns((2,1)) + +#---------------------------------# +# Sidebar + Main panel +col1.header('Input Options') + +## Sidebar - Currency price unit +currency_price_unit = col1.selectbox('Select currency for price', ('USD', 'BTC', 'ETH')) + +# Web scraping of CoinMarketCap data +@st.cache +def load_data(): + cmc = requests.get('https://coinmarketcap.com') + soup = BeautifulSoup(cmc.content, 'html.parser') + + data = soup.find('script', id='__NEXT_DATA__', type='application/json') + coins = {} + coin_data = json.loads(data.contents[0]) + listings = coin_data['props']['initialState']['cryptocurrency']['listingLatest']['data'] + for i in listings: + coins[str(i['id'])] = i['slug'] + + coin_name = [] + coin_symbol = [] + market_cap = [] + percent_change_1h = [] + percent_change_24h = [] + percent_change_7d = [] + price = [] + volume_24h = [] + + for i in listings: + coin_name.append(i['slug']) + coin_symbol.append(i['symbol']) + price.append(i['quote'][currency_price_unit]['price']) + percent_change_1h.append(i['quote'][currency_price_unit]['percent_change_1h']) + percent_change_24h.append(i['quote'][currency_price_unit]['percent_change_24h']) + percent_change_7d.append(i['quote'][currency_price_unit]['percent_change_7d']) + market_cap.append(i['quote'][currency_price_unit]['market_cap']) + volume_24h.append(i['quote'][currency_price_unit]['volume_24h']) + + df = pd.DataFrame(columns=['coin_name', 'coin_symbol', 'market_cap', 'percent_change_1h', 'percent_change_24h', 'percent_change_7d', 'price', 'volume_24h']) + df['coin_name'] = coin_name + df['coin_symbol'] = coin_symbol + df['price'] = price + df['percent_change_1h'] = percent_change_1h + df['percent_change_24h'] = percent_change_24h + df['percent_change_7d'] = percent_change_7d + df['market_cap'] = market_cap + df['volume_24h'] = volume_24h + return df + +df = load_data() + +## Sidebar - Cryptocurrency selections +sorted_coin = sorted( df['coin_symbol'] ) +selected_coin = col1.multiselect('Cryptocurrency', sorted_coin, sorted_coin) + +df_selected_coin = df[ (df['coin_symbol'].isin(selected_coin)) ] # Filtering data + +## Sidebar - Number of coins to display +num_coin = col1.slider('Display Top N Coins', 1, 100, 100) +df_coins = df_selected_coin[:num_coin] + +## Sidebar - Percent change timeframe +percent_timeframe = col1.selectbox('Percent change time frame', + ['7d','24h', '1h']) +percent_dict = {"7d":'percent_change_7d',"24h":'percent_change_24h',"1h":'percent_change_1h'} +selected_percent_timeframe = percent_dict[percent_timeframe] + +## Sidebar - Sorting values +sort_values = col1.selectbox('Sort values?', ['Yes', 'No']) + +col2.subheader('Price Data of Selected Cryptocurrency') +col2.write('Data Dimension: ' + str(df_selected_coin.shape[0]) + ' rows and ' + str(df_selected_coin.shape[1]) + ' columns.') + +col2.dataframe(df_coins) + +# Download CSV data +# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806 +def filedownload(df): + csv = df.to_csv(index=False) + b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions + href = f'Download CSV File' + return href + +col2.markdown(filedownload(df_selected_coin), unsafe_allow_html=True) + +#---------------------------------# +# Preparing data for Bar plot of % Price change +col2.subheader('Table of % Price Change') +df_change = pd.concat([df_coins.coin_symbol, df_coins.percent_change_1h, df_coins.percent_change_24h, df_coins.percent_change_7d], axis=1) +df_change = df_change.set_index('coin_symbol') +df_change['positive_percent_change_1h'] = df_change['percent_change_1h'] > 0 +df_change['positive_percent_change_24h'] = df_change['percent_change_24h'] > 0 +df_change['positive_percent_change_7d'] = df_change['percent_change_7d'] > 0 +col2.dataframe(df_change) + +# Conditional creation of Bar plot (time frame) +col3.subheader('Bar plot of % Price Change') + +if percent_timeframe == '7d': + if sort_values == 'Yes': + df_change = df_change.sort_values(by=['percent_change_7d']) + col3.write('*7 days period*') + plt.figure(figsize=(5,25)) + plt.subplots_adjust(top = 1, bottom = 0) + df_change['percent_change_7d'].plot(kind='barh', color=df_change.positive_percent_change_7d.map({True: 'g', False: 'r'})) + col3.pyplot(plt) +elif percent_timeframe == '24h': + if sort_values == 'Yes': + df_change = df_change.sort_values(by=['percent_change_24h']) + col3.write('*24 hour period*') + plt.figure(figsize=(5,25)) + plt.subplots_adjust(top = 1, bottom = 0) + df_change['percent_change_24h'].plot(kind='barh', color=df_change.positive_percent_change_24h.map({True: 'g', False: 'r'})) + col3.pyplot(plt) +else: + if sort_values == 'Yes': + df_change = df_change.sort_values(by=['percent_change_1h']) + col3.write('*1 hour period*') + plt.figure(figsize=(5,25)) + plt.subplots_adjust(top = 1, bottom = 0) + df_change['percent_change_1h'].plot(kind='barh', color=df_change.positive_percent_change_1h.map({True: 'g', False: 'r'})) + 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HcmV?d00001 From 45dc635502e7b2d0ae45d89dc5969e0644338b9c Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sun, 18 Oct 2020 21:49:43 +0700 Subject: [PATCH 56/73] Update crypto-price-app.py --- streamlit/part11/crypto-price-app.py | 1 + 1 file changed, 1 insertion(+) diff --git a/streamlit/part11/crypto-price-app.py b/streamlit/part11/crypto-price-app.py index 4b15eb2..1a10102 100644 --- a/streamlit/part11/crypto-price-app.py +++ b/streamlit/part11/crypto-price-app.py @@ -1,3 +1,4 @@ +# This app is for educational purpose only. Insights gained is not financial advice. Use at your own risk! import streamlit as st from PIL import Image import pandas as pd From a7f50dc54085eda43dcb2f1ebd20f3dd7f17de4d Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 20 Oct 2020 22:04:53 +0700 Subject: [PATCH 57/73] Rename streamlit/part11/crypto-price-app.py to streamlit/part12/crypto-price-app.py --- streamlit/{part11 => part12}/crypto-price-app.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename streamlit/{part11 => part12}/crypto-price-app.py (100%) diff --git a/streamlit/part11/crypto-price-app.py b/streamlit/part12/crypto-price-app.py similarity index 100% rename from streamlit/part11/crypto-price-app.py rename to streamlit/part12/crypto-price-app.py From 464adb679f9c63cacc11730ea2773b73961f2608 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 20 Oct 2020 22:05:18 +0700 Subject: [PATCH 58/73] Delete logo.jpg --- 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"grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "ZHIpNepeVPHX" + }, + "source": [ + "# **Exploratory Data Analysis in Python using Sweetviz**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n", + "\n", + "\n", + "For more information: [Sweetviz](https://pypi.org/project/sweetviz/) " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uWITrekqjFOd", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "652e844a-1578-4c0f-b019-5c4913cba7ed" + }, + "source": [ + "! pip install sweetviz" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting sweetviz\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a2/14/045f8057d87e3851c1a4ddd319f17ab7386e173a2f34de3868f4a359aea4/sweetviz-1.1.2-py3-none-any.whl (15.1MB)\n", + "\u001b[K |████████████████████████████████| 15.1MB 296kB/s \n", + "\u001b[?25hRequirement already satisfied: importlib-resources>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from sweetviz) (3.3.0)\n", + "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.6/dist-packages (from sweetviz) (1.4.1)\n", + "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.6/dist-packages (from sweetviz) (2.11.2)\n", + "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in /usr/local/lib/python3.6/dist-packages (from sweetviz) (1.1.4)\n", + "Requirement already satisfied: matplotlib>=3.1.3 in /usr/local/lib/python3.6/dist-packages (from sweetviz) (3.2.2)\n", + "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from sweetviz) (1.18.5)\n", + "Collecting tqdm>=4.43.0\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/dc/7e/bc84ebdb47cfc1f643d026570cb24dd40c2b7a29e167ea101f1702717658/tqdm-4.52.0-py2.py3-none-any.whl (71kB)\n", + "\u001b[K |████████████████████████████████| 71kB 8.9MB/s \n", + "\u001b[?25hRequirement already satisfied: zipp>=0.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-resources>=1.2.0->sweetviz) (3.4.0)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.11.1->sweetviz) (1.1.1)\n", + "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2018.9)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2.8.1)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.3->sweetviz) (2.4.7)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.3->sweetviz) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.1.3->sweetviz) (1.3.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (1.15.0)\n", + "Installing collected packages: tqdm, sweetviz\n", + " Found existing installation: tqdm 4.41.1\n", + " Uninstalling tqdm-4.41.1:\n", + " Successfully uninstalled tqdm-4.41.1\n", + "Successfully installed sweetviz-1.1.2 tqdm-4.52.0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sbxERWesV7IG" + }, + "source": [ + "# **1. Read in the Penguins Dataset**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ultuR-OaR5IK" + }, + "source": [ + "import pandas as pd\n", + "penguins = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/penguins_cleaned.csv')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PBh2Sz6kToxo" + }, + "source": [ + "# Separating X and y\n", + "X = penguins.drop('species', axis=1)\n", + "y = penguins['species']" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4RBzHoDuUWlo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "b4a5fdeb-0473-4c74-e666-abae67a431e0" + }, + "source": [ + "X" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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333 rows × 6 columns

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" + ], + "text/plain": [ + " island bill_length_mm ... body_mass_g sex\n", + "0 Torgersen 39.1 ... 3750 male\n", + "1 Torgersen 39.5 ... 3800 female\n", + "2 Torgersen 40.3 ... 3250 female\n", + "3 Torgersen 36.7 ... 3450 female\n", + "4 Torgersen 39.3 ... 3650 male\n", + ".. ... ... ... ... ...\n", + "328 Dream 55.8 ... 4000 male\n", + "329 Dream 43.5 ... 3400 female\n", + "330 Dream 49.6 ... 3775 male\n", + "331 Dream 50.8 ... 4100 male\n", + "332 Dream 50.2 ... 3775 female\n", + "\n", + "[333 rows x 6 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oh6hnTplaNXa" + }, + "source": [ + "# **2. Analyze the Penguins Dataset**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3x2YLCYYga3x", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "5582c9fc-fc5e-4810-9fa8-17ae1d35ae8d" + }, + "source": [ + "penguins" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
0AdelieTorgersen39.118.71813750male
1AdelieTorgersen39.517.41863800female
2AdelieTorgersen40.318.01953250female
3AdelieTorgersen36.719.31933450female
4AdelieTorgersen39.320.61903650male
........................
328ChinstrapDream55.819.82074000male
329ChinstrapDream43.518.12023400female
330ChinstrapDream49.618.21933775male
331ChinstrapDream50.819.02104100male
332ChinstrapDream50.218.71983775female
\n", + "

333 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " species island ... body_mass_g sex\n", + "0 Adelie Torgersen ... 3750 male\n", + "1 Adelie Torgersen ... 3800 female\n", + "2 Adelie Torgersen ... 3250 female\n", + "3 Adelie Torgersen ... 3450 female\n", + "4 Adelie Torgersen ... 3650 male\n", + ".. ... ... ... ... ...\n", + "328 Chinstrap Dream ... 4000 male\n", + "329 Chinstrap Dream ... 3400 female\n", + "330 Chinstrap Dream ... 3775 male\n", + "331 Chinstrap Dream ... 4100 male\n", + "332 Chinstrap Dream ... 3775 female\n", + "\n", + "[333 rows x 7 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OfJd3MpqoTvN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 83, + "referenced_widgets": [ + "53ece2ebee884961926c15dc27e7bd6b", + "aeffb4bc773a46fc8041f7931f4bdc59", + "14d37221431d44d4af346071b559845e", + "504827bd29cb4e95b6be9f67b2ed9bdb", + "acd91e99a90c4b26a0b68e331e817808", + "98d14c56362f4ba48a94559ac46a518b", + "56407ebd616840619dee5bd1a144aba0", + "cb4ef50c1185437a9930060ba90879ec", + "2892325b68204023979562eba591015e", + "815fa477ca474284a4eb5da5b0b1aef8", + "79260655eb0c4897ba12089e713a4bad" + ] + }, + "outputId": "cebcabfd-578e-47aa-9579-fe01c38d8823" + }, + "source": [ + "import sweetviz as sv\n", + "analyze_report = sv.analyze(penguins)\n", + "analyze_report.show_html('analyze.html', open_browser=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "53ece2ebee884961926c15dc27e7bd6b", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=8.0), HTML(value='')), la…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Report analyze.html was generated!\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Gk7oQWA8XM3G", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "d853ee62-7e64-41c0-9642-b2d41419f6bd" + }, + "source": [ + "import IPython\n", + "IPython.display.HTML('analyze.html')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
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DataFrame
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NO COMPARISON TARGET
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333
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ROWS
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DUPLICATES
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RAM
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FEATURES
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3
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CATEGORICAL
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4
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NUMERICAL
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0
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TEXT
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1
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VALUES:
\n", + "
\n", + " 333\n", + "
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MISSING:
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DISTINCT:
\n", + "
\n", + " 3\n", + "
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2
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\n", + " island\n", + "
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VALUES:
\n", + "
\n", + " 333\n", + "
\n", + "
\n", + " (100%)\n", + "
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MISSING:
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DISTINCT:
\n", + "
\n", + " 3\n", + "
\n", + "
\n", + " (1%)\n", + "
\n", + "
\n", + "
\n", + " \n", + "
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3
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\n", + " bill_length_mm\n", + "\n", + "
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VALUES:
\n", + "
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\n", + "
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MISSING:
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DISTINCT:
\n", + "
\n", + " 163\n", + "
\n", + "
\n", + " (49%)\n", + "
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MAX
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59.6
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95%
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52.0
\n", + "
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Q3
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48.6
\n", + "
\n", + "
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MEDIAN
\n", + "
44.5
\n", + "
\n", + "
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AVG
\n", + "
44.0
\n", + "
\n", + "
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Q1
\n", + "
39.5
\n", + "
\n", + "
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5%
\n", + "
35.7
\n", + "
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MIN
\n", + "
32.1
\n", + "
\n", + "
\n", + "
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RANGE
\n", + "
27.5
\n", + "
\n", + "
\n", + "
IQR
\n", + "
9.10
\n", + "
\n", + "
\n", + "
STD
\n", + "
5.47
\n", + "
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\n", + "
VAR
\n", + "
29.9
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.883
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
0.045
\n", + "
\n", + "
\n", + "
SUM
\n", + "
14,650
\n", + "
\n", + "
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4
\n", + "
\n", + " bill_depth_mm\n", + "\n", + "
\n", + "
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VALUES:
\n", + "
\n", + " 333\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
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\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 79\n", + "
\n", + "
\n", + " (24%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
\n", + "
21.50
\n", + "
\n", + "
\n", + "
95%
\n", + "
20.00
\n", + "
\n", + "
\n", + "
Q3
\n", + "
18.70
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
17.30
\n", + "
\n", + "
\n", + "
AVG
\n", + "
17.16
\n", + "
\n", + "
\n", + "
Q1
\n", + "
15.60
\n", + "
\n", + "
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5%
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13.90
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MIN
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13.10
\n", + "
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RANGE
\n", + "
8.40
\n", + "
\n", + "
\n", + "
IQR
\n", + "
3.10
\n", + "
\n", + "
\n", + "
STD
\n", + "
1.97
\n", + "
\n", + "
\n", + "
VAR
\n", + "
3.88
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.892
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
-0.150
\n", + "
\n", + "
\n", + "
SUM
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5,716
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5
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VALUES:
\n", + "
\n", + " 333\n", + "
\n", + "
\n", + " (100%)\n", + "
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MISSING:
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\n", + "
DISTINCT:
\n", + "
\n", + " 54\n", + "
\n", + "
\n", + " (16%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
\n", + "
231.0
\n", + "
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\n", + "
95%
\n", + "
225.0
\n", + "
\n", + "
\n", + "
Q3
\n", + "
213.0
\n", + "
\n", + "
\n", + "
AVG
\n", + "
201.0
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
197.0
\n", + "
\n", + "
\n", + "
Q1
\n", + "
190.0
\n", + "
\n", + "
\n", + "
5%
\n", + "
181.0
\n", + "
\n", + "
\n", + "
MIN
\n", + "
172.0
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
RANGE
\n", + "
59.0
\n", + "
\n", + "
\n", + "
IQR
\n", + "
23.0
\n", + "
\n", + "
\n", + "
STD
\n", + "
14.0
\n", + "
\n", + "
\n", + "
VAR
\n", + "
196
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.961
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
0.360
\n", + "
\n", + "
\n", + "
SUM
\n", + "
66,922
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
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\n", + " \n", + " \n", + "
6
\n", + "
\n", + " body_mass_g\n", + "\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 333\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 93\n", + "
\n", + "
\n", + " (28%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
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6,300
\n", + "
\n", + "
\n", + "
95%
\n", + "
5,670
\n", + "
\n", + "
\n", + "
Q3
\n", + "
4,775
\n", + "
\n", + "
\n", + "
AVG
\n", + "
4,207
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
4,050
\n", + "
\n", + "
\n", + "
Q1
\n", + "
3,550
\n", + "
\n", + "
\n", + "
5%
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3,150
\n", + "
\n", + "
\n", + "
MIN
\n", + "
2,700
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
RANGE
\n", + "
3,600
\n", + "
\n", + "
\n", + "
IQR
\n", + "
1,225
\n", + "
\n", + "
\n", + "
STD
\n", + "
805
\n", + "
\n", + "
\n", + "
VAR
\n", + "
648k
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.733
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
0.472
\n", + "
\n", + "
\n", + "
SUM
\n", + "
1.4M
\n", + "
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7
\n", + "
\n", + " sex\n", + "
\n", + "
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VALUES:
\n", + "
\n", + " 333\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
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MISSING:
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DISTINCT:
\n", + "
\n", + " 2\n", + "
\n", + "
\n", + " (1%)\n", + "
\n", + "
\n", + "
\n", + " \n", + "
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\n", + " \n", + " Associations\n", + "
\n", + " [Only including dataset \"DataFrame\"]\n", + " Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical,\n", + " (approximating how much the elements on the left PROVIDE INFORMATION on elements in the row).\n", + " Circles are numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.\n", + "
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\n", + "
\n", + " \n", + " Associations\n", + "
\n", + " [Only including dataset \"None\"]\n", + " Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical,\n", + " (approximating how much the elements on the left PROVIDE INFORMATION on elements in the row).\n", + " Circles are numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.\n", + "
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MISSING:
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TOP CATEGORIES
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\n", + " \n", + "
Adelie
\n", + "\n", + " \n", + "
\n", + "
146
\n", + "
44%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
Gentoo
\n", + "\n", + " \n", + "
\n", + "
119
\n", + "
36%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
Chinstrap
\n", + "\n", + " \n", + "
\n", + "
68
\n", + "
20%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
ALL
\n", + "\n", + " \n", + "
\n", + "
333
\n", + "
100%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + " CATEGORICAL ASSOCIATIONS
\n", + " (UNCERTAINTY COEFFICIENT, 0 to 1)\n", + "
\n", + "
species
\n", + " PROVIDES INFORMATION ON...
\n", + "
\n", + "
\n", + "
island
\n", + "
0.52
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.00
\n", + "
\n", + "
\n", + "
THESE FEATURES
GIVE INFORMATION
\n", + " ON species:
\n", + "
\n", + "
\n", + "
island
\n", + "
0.49
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.00
\n", + "
\n", + "
\n", + " \n", + "
\n", + " NUMERICAL ASSOCIATIONS
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
species
\n", + " CORRELATION RATIO WITH...
\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.88
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.84
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
0.82
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.82
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " island\n", + "
\n", + "\n", + " \n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
TOP CATEGORIES
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
Biscoe
\n", + "\n", + " \n", + "
\n", + "
163
\n", + "
49%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
Dream
\n", + "\n", + " \n", + "
\n", + "
123
\n", + "
37%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
Torgersen
\n", + "\n", + " \n", + "
\n", + "
47
\n", + "
14%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
ALL
\n", + "\n", + " \n", + "
\n", + "
333
\n", + "
100%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + " CATEGORICAL ASSOCIATIONS
\n", + " (UNCERTAINTY COEFFICIENT, 0 to 1)\n", + "
\n", + "
island
\n", + " PROVIDES INFORMATION ON...
\n", + "
\n", + "
\n", + "
species
\n", + "
0.49
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.00
\n", + "
\n", + "
\n", + "
THESE FEATURES
GIVE INFORMATION
\n", + " ON island:
\n", + "
\n", + "
\n", + "
species
\n", + "
0.52
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.00
\n", + "
\n", + "
\n", + " \n", + "
\n", + " NUMERICAL ASSOCIATIONS
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
island
\n", + " CORRELATION RATIO WITH...
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
0.63
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.62
\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.60
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.38
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " bill_length_mm\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.65
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.59
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
-0.23
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
species
\n", + "
0.84
\n", + "
\n", + "
\n", + "
island
\n", + "
0.38
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.34
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
41.1
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
45.2
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
39.6
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
50.0
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
46.5
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
45.5
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
50.5
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
38.1
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
50.8
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
40.9
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
43.2
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
39.7
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
47.5
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
46.2
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
51.3
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
32.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
33.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
33.5
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
34.0
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
34.4
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
34.5
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
34.6
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
35.0
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
35.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
35.2
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
35.3
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
35.5
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
35.6
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
35.7
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
35.9
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
59.6
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
58.0
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
55.9
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
55.8
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
55.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
54.3
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
54.2
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
53.5
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
53.4
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
52.8
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
52.7
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
52.5
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
52.2
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
52.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
52.0
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " bill_depth_mm\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
-0.58
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
-0.47
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
-0.23
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
species
\n", + "
0.82
\n", + "
\n", + "
\n", + "
island
\n", + "
0.63
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.37
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
17.0
\n", + "
\n", + "
12
\n", + "
3.6%
\n", + "
\n", + "
\n", + "
\n", + "
15.0
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.6
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.5
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
17.9
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
19.0
\n", + "
\n", + "
9
\n", + "
2.7%
\n", + "
\n", + "
\n", + "
\n", + "
17.8
\n", + "
\n", + "
9
\n", + "
2.7%
\n", + "
\n", + "
\n", + "
\n", + "
18.1
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
17.3
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
14.5
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
18.9
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
18.8
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
17.1
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
17.5
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
19.5
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
13.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
13.2
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
13.3
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
13.4
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
13.5
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
13.6
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
13.7
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
13.8
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
13.9
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
14.0
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
14.1
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
14.2
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
14.3
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
14.4
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
14.5
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
21.5
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
21.2
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
21.1
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
20.8
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
20.7
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
20.6
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
20.5
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
20.3
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
20.1
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
20.0
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
19.9
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
19.8
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
19.7
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
19.6
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
19.5
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " flipper_length_mm\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.87
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.65
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
-0.58
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
species
\n", + "
0.88
\n", + "
\n", + "
\n", + "
island
\n", + "
0.60
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.26
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
190
\n", + "
\n", + "
21
\n", + "
6.3%
\n", + "
\n", + "
\n", + "
\n", + "
195
\n", + "
\n", + "
17
\n", + "
5.1%
\n", + "
\n", + "
\n", + "
\n", + "
187
\n", + "
\n", + "
16
\n", + "
4.8%
\n", + "
\n", + "
\n", + "
\n", + "
193
\n", + "
\n", + "
14
\n", + "
4.2%
\n", + "
\n", + "
\n", + "
\n", + "
210
\n", + "
\n", + "
14
\n", + "
4.2%
\n", + "
\n", + "
\n", + "
\n", + "
191
\n", + "
\n", + "
13
\n", + "
3.9%
\n", + "
\n", + "
\n", + "
\n", + "
215
\n", + "
\n", + "
12
\n", + "
3.6%
\n", + "
\n", + "
\n", + "
\n", + "
196
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
197
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
185
\n", + "
\n", + "
9
\n", + "
2.7%
\n", + "
\n", + "
\n", + "
\n", + "
198
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
208
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
220
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
184
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
230
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
172
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
174
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
176
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
178
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
180
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
181
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
182
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
183
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
184
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
185
\n", + "
\n", + "
9
\n", + "
2.7%
\n", + "
\n", + "
\n", + "
\n", + "
186
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
187
\n", + "
\n", + "
16
\n", + "
4.8%
\n", + "
\n", + "
\n", + "
\n", + "
188
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
189
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
190
\n", + "
\n", + "
21
\n", + "
6.3%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
231
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
230
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
229
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
228
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
226
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
225
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
224
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
223
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
222
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
221
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
220
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
219
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
218
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
217
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
216
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " body_mass_g\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.87
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.59
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
-0.47
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
species
\n", + "
0.82
\n", + "
\n", + "
\n", + "
island
\n", + "
0.62
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.42
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
3800
\n", + "
\n", + "
12
\n", + "
3.6%
\n", + "
\n", + "
\n", + "
\n", + "
3950
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3700
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3900
\n", + "
\n", + "
10
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3550
\n", + "
\n", + "
9
\n", + "
2.7%
\n", + "
\n", + "
\n", + "
\n", + "
4300
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
3450
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
3400
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
4400
\n", + "
\n", + "
8
\n", + "
2.4%
\n", + "
\n", + "
\n", + "
\n", + "
3500
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
3600
\n", + "
\n", + "
7
\n", + "
2.1%
\n", + "
\n", + "
\n", + "
\n", + "
5000
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
3650
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
4150
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
5550
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
2700
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
2850
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
2900
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
2925
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
3000
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
3050
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
3075
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
3100
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
3150
\n", + "
\n", + "
4
\n", + "
1.2%
\n", + "
\n", + "
\n", + "
\n", + "
3175
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
3200
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
3250
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
3275
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
3300
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
3325
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
6300
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
6050
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
6000
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
5950
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
5850
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
5800
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
5750
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
5700
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5650
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
5600
\n", + "
\n", + "
2
\n", + "
0.6%
\n", + "
\n", + "
\n", + "
\n", + "
5550
\n", + "
\n", + "
6
\n", + "
1.8%
\n", + "
\n", + "
\n", + "
\n", + "
5500
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5450
\n", + "
\n", + "
1
\n", + "
0.3%
\n", + "
\n", + "
\n", + "
\n", + "
5400
\n", + "
\n", + "
5
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5350
\n", + "
\n", + "
3
\n", + "
0.9%
\n", + "
\n", + "
\n", + "
\n", + "
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\n", + " \n", + "
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MISSING:
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TOP CATEGORIES
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male
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168
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50%
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\n", + "
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female
\n", + "\n", + " \n", + "
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165
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50%
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\n", + " \n", + "
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ALL
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333
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100%
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\n", + " CATEGORICAL ASSOCIATIONS
\n", + " (UNCERTAINTY COEFFICIENT, 0 to 1)\n", + "
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sex
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\n", + "
island
\n", + "
0.00
\n", + "
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\n", + "
species
\n", + "
0.00
\n", + "
\n", + "
\n", + "
THESE FEATURES
GIVE INFORMATION
\n", + " ON sex:
\n", + "
\n", + "
\n", + "
island
\n", + "
0.00
\n", + "
\n", + "
\n", + "
species
\n", + "
0.00
\n", + "
\n", + "
\n", + " \n", + "
\n", + " NUMERICAL ASSOCIATIONS
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
sex
\n", + " CORRELATION RATIO WITH...
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.42
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
0.37
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.34
\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.26
\n", + "
\n", + "
\n", + "
\n", + "
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\n", + "
\n", + "
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-5Gr_KnlWOzF" + }, + "source": [ + "# **3. Side-by-Side Comparison of Train vs Test Set**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FTzHz40iWEQY" + }, + "source": [ + "## **3.1. Data Split using 80/20 Split Ratio**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "b3_o0i1CUITP" + }, + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "URm3Rg3Eg5bW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "9a2e803c-5094-4ff7-e04a-cf4b2c04c61d" + }, + "source": [ + "X_train" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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islandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
5Torgersen38.917.81813625female
271Dream46.118.21783250female
266Dream50.019.51963900male
298Dream51.018.82034100male
54Biscoe35.716.91853150female
.....................
116Torgersen40.217.01763450female
202Biscoe45.515.02205000male
310Dream50.818.52014450male
124Torgersen38.517.91903325female
265Dream46.517.91923500female
\n", + "

266 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " island bill_length_mm ... body_mass_g sex\n", + "5 Torgersen 38.9 ... 3625 female\n", + "271 Dream 46.1 ... 3250 female\n", + "266 Dream 50.0 ... 3900 male\n", + "298 Dream 51.0 ... 4100 male\n", + "54 Biscoe 35.7 ... 3150 female\n", + ".. ... ... ... ... ...\n", + "116 Torgersen 40.2 ... 3450 female\n", + "202 Biscoe 45.5 ... 5000 male\n", + "310 Dream 50.8 ... 4450 male\n", + "124 Torgersen 38.5 ... 3325 female\n", + "265 Dream 46.5 ... 3500 female\n", + "\n", + "[266 rows x 6 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vrt7TCSAg72A", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "cde02264-d11f-4123-c55f-84618df21acd" + }, + "source": [ + "X_test" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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islandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
186Biscoe49.616.02255700male
231Biscoe51.314.22185300male
268Dream45.418.71883525female
147Biscoe50.016.32305700male
136Dream32.115.51883050female
.....................
264Biscoe49.916.12135400male
77Torgersen35.119.41934200male
309Dream50.917.91963675female
285Dream42.417.31813600female
125Torgersen43.119.21973500male
\n", + "

67 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " island bill_length_mm ... body_mass_g sex\n", + "186 Biscoe 49.6 ... 5700 male\n", + "231 Biscoe 51.3 ... 5300 male\n", + "268 Dream 45.4 ... 3525 female\n", + "147 Biscoe 50.0 ... 5700 male\n", + "136 Dream 32.1 ... 3050 female\n", + ".. ... ... ... ... ...\n", + "264 Biscoe 49.9 ... 5400 male\n", + "77 Torgersen 35.1 ... 4200 male\n", + "309 Dream 50.9 ... 3675 female\n", + "285 Dream 42.4 ... 3600 female\n", + "125 Torgersen 43.1 ... 3500 male\n", + "\n", + "[67 rows x 6 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9I_uSdGzgsgX" + }, + "source": [ + "## **3.2. Comparison Report**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5BTBb0_MnoFa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 83, + "referenced_widgets": [ + "554425acb0ac4b088b7f6684590e76c7", + "e70e77e027164409989aa6814afeac34", + "40d1d2a5da924422a183efbf7a62e047", + "3eff0b6f492d4d29a661d71f4d474a02", + "c9ff7f18afa0429ebc706b48e87a625c", + "a9910f1efe77494ea48467c6bce375fa", + "c09a5b5d026b4cf58dd48c15ea3f0071", + "0536cc0d2e924db68307aa2039016c79", + "1b277c05d5e74b30a19a13ca15097c05", + "7bba9fcb0c8748cdab0d095600919e84", + "ad0d0701109d48b5a8208e972f72a233" + ] + }, + "outputId": "9e11d272-3d1e-44f8-f2f6-863ead1032bf" + }, + "source": [ + "import sweetviz as sv\n", + "compare_report = sv.compare([X_train, 'Train'], [X_test, 'Test'])\n", + "compare_report.show_html('compare.html', open_browser=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "554425acb0ac4b088b7f6684590e76c7", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(HTML(value=''), FloatProgress(value=0.0, layout=Layout(flex='2'), max=7.0), HTML(value='')), la…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Report compare.html was generated!\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hQQPGtR2oZrZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "e6a6bd72-dcbb-42df-eb81-36a00bd4032b" + }, + "source": [ + "import IPython\n", + "IPython.display.HTML('compare.html')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
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\n", + " 1.1.2
\n", + " Click here for docs & updates
\n", + " Created & maintained by Francois Bertrand
\n", + " Graphic design by Jean-Francois Hains\n", + "
\n", + "
\n", + "
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\n", + " \n", + "
Train
\n", + "
Test
\n", + "
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266
\n", + "
ROWS
\n", + "
67
\n", + "
\n", + "
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0
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DUPLICATES
\n", + "
0
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57.8 kb
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RAM
\n", + "
14.5 kb
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FEATURES
\n", + "
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\n", + "
\n", + "
\n", + "
2
\n", + "
CATEGORICAL
\n", + "
2
\n", + "
\n", + "
\n", + "
4
\n", + "
NUMERICAL
\n", + "
4
\n", + "
\n", + "
\n", + "
0
\n", + "
TEXT
\n", + "
0
\n", + "
\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
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\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
1
\n", + "
\n", + " island\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 266\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + " 67\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 3\n", + "
\n", + "
\n", + " (1%)\n", + "
\n", + "
\n", + " 3\n", + "
\n", + "
\n", + " (4%)\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
2
\n", + "
\n", + " bill_length_mm\n", + "\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 266\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + " 67\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 142\n", + "
\n", + "
\n", + " (53%)\n", + "
\n", + "
\n", + " 61\n", + "
\n", + "
\n", + " (91%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
\n", + "
58.0
\n", + "
59.6
\n", + "
\n", + "
\n", + "
95%
\n", + "
51.8
\n", + "
52.6
\n", + "
\n", + "
\n", + "
Q3
\n", + "
48.0
\n", + "
50.0
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
44.1
\n", + "
45.7
\n", + "
\n", + "
\n", + "
AVG
\n", + "
43.8
\n", + "
44.9
\n", + "
\n", + "
\n", + "
Q1
\n", + "
39.2
\n", + "
40.2
\n", + "
\n", + "
\n", + "
5%
\n", + "
35.7
\n", + "
35.6
\n", + "
\n", + "
\n", + "
MIN
\n", + "
33.1
\n", + "
32.1
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
RANGE
\n", + "
24.9
\n", + "
27.5
\n", + "
\n", + "
\n", + "
IQR
\n", + "
8.80
\n", + "
9.75
\n", + "
\n", + "
\n", + "
STD
\n", + "
5.33
\n", + "
5.92
\n", + "
\n", + "
\n", + "
VAR
\n", + "
28.5
\n", + "
35.0
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.910
\n", + "
-0.785
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
0.077
\n", + "
-0.139
\n", + "
\n", + "
\n", + "
SUM
\n", + "
11,638
\n", + "
3,012
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
3
\n", + "
\n", + " bill_depth_mm\n", + "\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 266\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + " 67\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 77\n", + "
\n", + "
\n", + " (29%)\n", + "
\n", + "
\n", + " 44\n", + "
\n", + "
\n", + " (66%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
\n", + "
21.50
\n", + "
21.10
\n", + "
\n", + "
\n", + "
95%
\n", + "
20.00
\n", + "
20.00
\n", + "
\n", + "
\n", + "
Q3
\n", + "
18.60
\n", + "
18.90
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
17.35
\n", + "
17.20
\n", + "
\n", + "
\n", + "
AVG
\n", + "
17.19
\n", + "
17.06
\n", + "
\n", + "
\n", + "
Q1
\n", + "
15.62
\n", + "
15.45
\n", + "
\n", + "
\n", + "
5%
\n", + "
14.03
\n", + "
13.70
\n", + "
\n", + "
\n", + "
MIN
\n", + "
13.10
\n", + "
13.40
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
RANGE
\n", + "
8.40
\n", + "
7.70
\n", + "
\n", + "
\n", + "
IQR
\n", + "
2.98
\n", + "
3.45
\n", + "
\n", + "
\n", + "
STD
\n", + "
1.93
\n", + "
2.14
\n", + "
\n", + "
\n", + "
VAR
\n", + "
3.71
\n", + "
4.58
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.843
\n", + "
-1.06
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
-0.167
\n", + "
-0.074
\n", + "
\n", + "
\n", + "
SUM
\n", + "
4,573
\n", + "
1,143
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
4
\n", + "
\n", + " flipper_length_mm\n", + "\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 266\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + " 67\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 54\n", + "
\n", + "
\n", + " (20%)\n", + "
\n", + "
\n", + " 31\n", + "
\n", + "
\n", + " (46%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
\n", + "
231.0
\n", + "
230.0
\n", + "
\n", + "
\n", + "
95%
\n", + "
223.8
\n", + "
228.0
\n", + "
\n", + "
\n", + "
Q3
\n", + "
212.8
\n", + "
214.0
\n", + "
\n", + "
\n", + "
AVG
\n", + "
200.5
\n", + "
203.0
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
197.0
\n", + "
199.0
\n", + "
\n", + "
\n", + "
Q1
\n", + "
190.0
\n", + "
191.0
\n", + "
\n", + "
\n", + "
5%
\n", + "
181.0
\n", + "
186.0
\n", + "
\n", + "
\n", + "
MIN
\n", + "
172.0
\n", + "
178.0
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
RANGE
\n", + "
59.0
\n", + "
52.0
\n", + "
\n", + "
\n", + "
IQR
\n", + "
22.8
\n", + "
23.0
\n", + "
\n", + "
\n", + "
STD
\n", + "
14.0
\n", + "
14.1
\n", + "
\n", + "
\n", + "
VAR
\n", + "
196
\n", + "
197
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.978
\n", + "
-0.938
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
0.358
\n", + "
0.388
\n", + "
\n", + "
\n", + "
SUM
\n", + "
53,323
\n", + "
13,599
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
5
\n", + "
\n", + " body_mass_g\n", + "\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 266\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + " 67\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 88\n", + "
\n", + "
\n", + " (33%)\n", + "
\n", + "
\n", + " 43\n", + "
\n", + "
\n", + " (64%)\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MAX
\n", + "
6,300
\n", + "
6,050
\n", + "
\n", + "
\n", + "
95%
\n", + "
5,650
\n", + "
5,685
\n", + "
\n", + "
\n", + "
Q3
\n", + "
4,750
\n", + "
5,050
\n", + "
\n", + "
\n", + "
AVG
\n", + "
4,180
\n", + "
4,316
\n", + "
\n", + "
\n", + "
MEDIAN
\n", + "
4,000
\n", + "
4,200
\n", + "
\n", + "
\n", + "
Q1
\n", + "
3,550
\n", + "
3,662
\n", + "
\n", + "
\n", + "
5%
\n", + "
3,150
\n", + "
3,065
\n", + "
\n", + "
\n", + "
MIN
\n", + "
2,850
\n", + "
2,700
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
RANGE
\n", + "
3,450
\n", + "
3,350
\n", + "
\n", + "
\n", + "
IQR
\n", + "
1,200
\n", + "
1,388
\n", + "
\n", + "
\n", + "
STD
\n", + "
791
\n", + "
855
\n", + "
\n", + "
\n", + "
VAR
\n", + "
626k
\n", + "
731k
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
KURT.
\n", + "
-0.623
\n", + "
-1.03
\n", + "
\n", + "
\n", + "
SKEW
\n", + "
0.526
\n", + "
0.264
\n", + "
\n", + "
\n", + "
SUM
\n", + "
1.1M
\n", + "
289k
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + "
6
\n", + "
\n", + " sex\n", + "
\n", + "
\n", + "
VALUES:
\n", + "
\n", + " 266\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + " 67\n", + "
\n", + "
\n", + " (100%)\n", + "
\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
DISTINCT:
\n", + "
\n", + " 2\n", + "
\n", + "
\n", + " (1%)\n", + "
\n", + "
\n", + " 2\n", + "
\n", + "
\n", + " (3%)\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + "
\n", + "
\n", + " \n", + " Associations\n", + "
\n", + " [Only including dataset \"Train\"]\n", + " Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical,\n", + " (approximating how much the elements on the left PROVIDE INFORMATION on elements in the row).\n", + " Circles are numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " \n", + " Associations\n", + "
\n", + " [Only including dataset \"Test\"]\n", + " Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical,\n", + " (approximating how much the elements on the left PROVIDE INFORMATION on elements in the row).\n", + " Circles are numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " island\n", + "
\n", + "\n", + " \n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
TOP CATEGORIES
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
Biscoe
\n", + "\n", + " \n", + "
\n", + "
130
\n", + "
49%
\n", + "
\n", + "
\n", + "
33
\n", + "
49%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
Dream
\n", + "\n", + " \n", + "
\n", + "
99
\n", + "
37%
\n", + "
\n", + "
\n", + "
24
\n", + "
36%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
Torgersen
\n", + "\n", + " \n", + "
\n", + "
37
\n", + "
14%
\n", + "
\n", + "
\n", + "
10
\n", + "
15%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
ALL
\n", + "\n", + " \n", + "
\n", + "
266
\n", + "
100%
\n", + "
\n", + "
\n", + "
67
\n", + "
100%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + " CATEGORICAL ASSOCIATIONS
\n", + " (UNCERTAINTY COEFFICIENT, 0 to 1)\n", + "
\n", + "
island
\n", + " PROVIDES INFORMATION ON...
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.00
\n", + "
\n", + "
\n", + "
THESE FEATURES
GIVE INFORMATION
\n", + " ON island:
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.00
\n", + "
\n", + "
\n", + " \n", + "
\n", + " NUMERICAL ASSOCIATIONS
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
island
\n", + " CORRELATION RATIO WITH...
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
0.63
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.59
\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.57
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.35
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " bill_length_mm\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.66
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.60
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
-0.28
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
island
\n", + "
0.35
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.29
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
41.1
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
45.2
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
45.5
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
46.5
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
50.0
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
43.2
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
39.7
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
39.6
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
40.6
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
50.5
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
46.2
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
47.5
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
46.4
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
37.8
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
37.3
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
33.1
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
33.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
34.0
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
34.4
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
34.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
34.6
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
35.0
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
35.3
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
35.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
35.6
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
35.7
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
35.9
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
36.0
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
36.2
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
36.3
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
58.0
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
55.9
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
55.8
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
55.1
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
54.3
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
54.2
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
53.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
52.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
52.2
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
52.1
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
52.0
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
51.9
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
51.7
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
51.4
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
51.3
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " bill_depth_mm\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
-0.59
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
-0.48
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
-0.28
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
island
\n", + "
0.63
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.37
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
17.0
\n", + "
\n", + "
11
\n", + "
4.1%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
18.6
\n", + "
\n", + "
9
\n", + "
3.4%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
17.8
\n", + "
\n", + "
9
\n", + "
3.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.5
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.5
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
15.0
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
17.9
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.1
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
19.0
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.8
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
17.1
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
17.3
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.9
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
18.2
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
17.2
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
13.1
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
13.2
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
13.3
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
13.5
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
13.7
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
4
\n", + "
6.0%
\n", + "
\n", + "
\n", + "
\n", + "
13.8
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
13.9
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.0
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.1
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.2
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
14.3
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.4
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
14.5
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.6
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
14.7
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
21.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
21.2
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
21.1
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
20.8
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
20.7
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
20.6
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
20.5
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
20.3
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
20.1
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
20.0
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
19.9
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
19.8
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
19.7
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
19.6
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
19.5
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " flipper_length_mm\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.88
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.66
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
-0.59
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
island
\n", + "
0.57
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.25
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
190
\n", + "
\n", + "
17
\n", + "
6.4%
\n", + "
\n", + "
\n", + "
4
\n", + "
6.0%
\n", + "
\n", + "
\n", + "
\n", + "
187
\n", + "
\n", + "
14
\n", + "
5.3%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
195
\n", + "
\n", + "
12
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
5
\n", + "
7.5%
\n", + "
\n", + "
\n", + "
\n", + "
210
\n", + "
\n", + "
11
\n", + "
4.1%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
193
\n", + "
\n", + "
11
\n", + "
4.1%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
215
\n", + "
\n", + "
10
\n", + "
3.8%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
191
\n", + "
\n", + "
10
\n", + "
3.8%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
185
\n", + "
\n", + "
9
\n", + "
3.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
198
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
189
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
197
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
212
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
220
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
196
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
181
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
172
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
174
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
176
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
178
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
180
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
181
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
182
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
183
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
184
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
185
\n", + "
\n", + "
9
\n", + "
3.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
186
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
187
\n", + "
\n", + "
14
\n", + "
5.3%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
188
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
189
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
190
\n", + "
\n", + "
17
\n", + "
6.4%
\n", + "
\n", + "
\n", + "
4
\n", + "
6.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
231
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
230
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
229
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
228
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
226
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
225
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
224
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
223
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
222
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
221
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
220
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
219
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
218
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
217
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
216
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " body_mass_g\n", + "
\n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + "
NUMERICAL ASSOCIATIONS
\n", + "
\n", + " (PEARSON, -1 to 1)\n", + "
\n", + "\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.88
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.60
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
-0.48
\n", + "
\n", + "
\n", + "
CATEGORICAL ASSOCIATIONS
\n", + "
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
\n", + "
\n", + "
island
\n", + "
0.59
\n", + "
\n", + "
\n", + "
sex
\n", + "
0.42
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
MOST FREQUENT VALUES
\n", + "
\n", + "
\n", + "
3800
\n", + "
\n", + "
10
\n", + "
3.8%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3900
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3950
\n", + "
\n", + "
8
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3700
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
3550
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3400
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
3450
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3500
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5550
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5000
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3600
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
4300
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3650
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
4700
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
3350
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
SMALLEST VALUES
\n", + "
\n", + "
\n", + "
2850
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
2900
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
2925
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3000
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3050
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
3
\n", + "
4.5%
\n", + "
\n", + "
\n", + "
\n", + "
3075
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3150
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3175
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3200
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3250
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
3275
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3300
\n", + "
\n", + "
4
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
3325
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3350
\n", + "
\n", + "
5
\n", + "
1.9%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
3400
\n", + "
\n", + "
7
\n", + "
2.6%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
LARGEST VALUES
\n", + "
\n", + "
\n", + "
6300
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
6000
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5950
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5850
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5800
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5750
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5700
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
5650
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5600
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5550
\n", + "
\n", + "
6
\n", + "
2.3%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5500
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
4
\n", + "
6.0%
\n", + "
\n", + "
\n", + "
\n", + "
5450
\n", + "
\n", + "
1
\n", + "
0.4%
\n", + "
\n", + "
\n", + "
0
\n", + "
0.0%
\n", + "
\n", + "
\n", + "
\n", + "
5400
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
2
\n", + "
3.0%
\n", + "
\n", + "
\n", + "
\n", + "
5350
\n", + "
\n", + "
2
\n", + "
0.8%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
5300
\n", + "
\n", + "
3
\n", + "
1.1%
\n", + "
\n", + "
\n", + "
1
\n", + "
1.5%
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + " sex\n", + "
\n", + "\n", + " \n", + "\n", + "
\n", + "
\n", + "
MISSING:
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + " ---\n", + "
\n", + "
\n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
TOP CATEGORIES
\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
female
\n", + "\n", + " \n", + "
\n", + "
134
\n", + "
50%
\n", + "
\n", + "
\n", + "
31
\n", + "
46%
\n", + "
\n", + " \n", + "
\n", + "
\n", + " \n", + "
male
\n", + "\n", + " \n", + "
\n", + "
132
\n", + "
50%
\n", + "
\n", + "
\n", + "
36
\n", + "
54%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + "
ALL
\n", + "\n", + " \n", + "
\n", + "
266
\n", + "
100%
\n", + "
\n", + "
\n", + "
67
\n", + "
100%
\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + " \n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + " CATEGORICAL ASSOCIATIONS
\n", + " (UNCERTAINTY COEFFICIENT, 0 to 1)\n", + "
\n", + "
sex
\n", + " PROVIDES INFORMATION ON...
\n", + "
\n", + "
\n", + "
island
\n", + "
0.00
\n", + "
\n", + "
\n", + "
THESE FEATURES
GIVE INFORMATION
\n", + " ON sex:
\n", + "
\n", + "
\n", + "
island
\n", + "
0.00
\n", + "
\n", + "
\n", + " \n", + "
\n", + " NUMERICAL ASSOCIATIONS
\n", + " (CORRELATION RATIO, 0 to 1)\n", + "
\n", + "
sex
\n", + " CORRELATION RATIO WITH...
\n", + "
\n", + "
\n", + "
body_mass_g
\n", + "
0.42
\n", + "
\n", + "
\n", + "
bill_depth_mm
\n", + "
0.37
\n", + "
\n", + "
\n", + "
bill_length_mm
\n", + "
0.29
\n", + "
\n", + "
\n", + "
flipper_length_mm
\n", + "
0.25
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + } + ] +} \ No newline at end of file From d03d55097e0d13b63c49c52bf31f8af470e4cd49 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 25 Nov 2020 21:26:31 +0700 Subject: [PATCH 61/73] Update pandas_read_html_for_webscraping.ipynb --- python/pandas_read_html_for_webscraping.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/python/pandas_read_html_for_webscraping.ipynb b/python/pandas_read_html_for_webscraping.ipynb index 24e6494..65c1933 100644 --- a/python/pandas_read_html_for_webscraping.ipynb +++ b/python/pandas_read_html_for_webscraping.ipynb @@ -65,10 +65,10 @@ "source": [ "# The Building Blocks\n", "year = '2019'\n", - "str = 'https://www.basketball-reference.com/leagues/NBA_{}_per_game.html'\n", + "url_link = 'https://www.basketball-reference.com/leagues/NBA_{}_per_game.html'\n", "\n", "# Combining the URL + year strings together\n", - "url = str.format(year)\n", + "url = url_link.format(year)\n", "url" ], "execution_count": 0, @@ -110,10 +110,10 @@ }, "source": [ "years = [2015,2016,2017,2018,2019]\n", - "str = 'https://www.basketball-reference.com/leagues/NBA_{}_per_game.html'\n", + "url_link = 'https://www.basketball-reference.com/leagues/NBA_{}_per_game.html'\n", "\n", "for year in years:\n", - " url = str.format(year)\n", + " url = url_link.format(year)\n", " print(url)" ], "execution_count": 0, @@ -2021,4 +2021,4 @@ ] } ] -} \ No newline at end of file +} From 90b5a99899b7258f003bad27a51f4eca1bf19676 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Fri, 18 Dec 2020 21:06:01 +0700 Subject: [PATCH 62/73] Update app.R --- shiny/005-bmi/app.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/shiny/005-bmi/app.R b/shiny/005-bmi/app.R index 88420c7..b48f4e4 100644 --- a/shiny/005-bmi/app.R +++ b/shiny/005-bmi/app.R @@ -19,7 +19,7 @@ ui <- fluidPage(theme = shinytheme("united"), tabPanel("Home", # Input values sidebarPanel( - HTML("

Input parameters

"), + HTML("

Input parameters

"), sliderInput("height", label = "Height", value = 175, From 4b3d32b4e2e18e1320550e398ea5e918d16d5b04 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Wed, 6 Jan 2021 11:06:13 +0700 Subject: [PATCH 63/73] Add files via upload --- python/klib.ipynb | 2809 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2809 insertions(+) create mode 100644 python/klib.ipynb diff --git a/python/klib.ipynb b/python/klib.ipynb new file mode 100644 index 0000000..cf4c4c7 --- /dev/null +++ b/python/klib.ipynb @@ -0,0 +1,2809 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "klib.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "HjrfJDqnXWWH" + }, + "source": [ + "# **How to Speed Up Data Cleaning with the klib Python library**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor\n", + "\n", + "---\n", + "**Further reading:**\n", + "* [Andreas Kanz @ Medium](https://medium.com/@akanz)\n", + "* [klib Examples on GitHub](https://github.com/akanz1/klib/tree/main/examples)\n", + "* [klib on Towards Data Science](https://towardsdatascience.com/speed-up-your-data-cleaning-and-preprocessing-with-klib-97191d320f80)\n", + "* [klib Documentation](https://klib.readthedocs.io/en/latest/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cWgopKUOzDTR" + }, + "source": [ + "# **Install klib**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2QV7BW7xXVXV", + "outputId": "a6f3b0b3-32ef-4e75-a1f7-f876477c46a9" + }, + "source": [ + "! pip install klib" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting klib\n", + " Downloading https://files.pythonhosted.org/packages/89/bc/5c289a2f573ca02f5c5429592d26e3e67ade4998f7e432fc5d5a8be2d174/klib-0.0.89-py3-none-any.whl\n", + "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from klib) (1.1.5)\n", + "Requirement already satisfied: seaborn>=0.1.0 in /usr/local/lib/python3.6/dist-packages (from klib) (0.11.0)\n", + "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from klib) (1.19.4)\n", + "Requirement already satisfied: matplotlib>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from klib) (3.2.2)\n", + "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.6/dist-packages (from klib) (0.22.2.post1)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0.0->klib) (2.8.1)\n", + "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0.0->klib) (2018.9)\n", + "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.6/dist-packages (from seaborn>=0.1.0->klib) (1.4.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->klib) (1.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->klib) (0.10.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->klib) (2.4.7)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.22->klib) (1.0.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.0->klib) (1.15.0)\n", + "Installing collected packages: klib\n", + "Successfully installed klib-0.0.89\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IdFTspYMazKx" + }, + "source": [ + "# **Missing Values Plot**\n", + "\n", + "Here we can examine the data quality. In other words, how clean is the dataset?\n", + "\n", + "***missingval_plot()*** - provides a high-level overview of the missing values in a dataset. It pinpoints which columns and rows to examine in more detail.\n", + "\n", + "**Top** portion of the plot shows the aggregate for each column. Summary statistics is displayed on the right most side.\n", + "\n", + "**Bottom** portion of the plot shows the missing values (black colors) in the DataFrame." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 + }, + "id": "9SMR3_bGYH5d", + "outputId": "851e1af4-d6a3-414d-f6ae-571e943eda91" + }, + "source": [ + "# NFL Dataset\n", + "import klib\n", + "import pandas as pd\n", + "\n", + "df = pd.read_csv('https://github.com/akanz1/klib/raw/main/examples/NFL_DATASET.csv')\n", + "klib.missingval_plot(df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSpec(6, 6)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GTwPToNepHy3" + }, + "source": [ + "# **Data Cleaning**\n", + "\n", + "The ***data_cleaning()*** function essentially drops empty and single valued columns as well as empty and duplicate rows." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u1waScSfpKcT", + "outputId": "85d30425-ab23-44e0-ed4d-9810479b3e2d" + }, + "source": [ + "df_cleaned = klib.data_cleaning(df)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Shape of cleaned data: (183460, 63) - Remaining NAs: 1757318\n", + "\n", + "Changes:\n", + "Dropped rows: 0\n", + " of which 0 duplicates. (Rows: [])\n", + "Dropped columns: 4\n", + " of which 1 single valued. (Columns: ['PlayAttempted'])\n", + "Dropped missing values: 520667\n", + "Reduced memory by at least: 59.37 MB (-63.3%)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "enUcOWWidMbP" + }, + "source": [ + "# **Correlation Plot**\n", + "\n", + "Here, we can examine the intercorrelation amongst the features." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "szW70rtefazL" + }, + "source": [ + "### Display all correlations data." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 611 + }, + "id": "P3E108xue2ZR", + "outputId": "74ef4e07-3547-4789-ccf7-1d10f083f1ea" + }, + "source": [ + "klib.corr_plot(df_cleaned, annot=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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wFVgrl39e0r3AfaShLMPLjrki/50M3BkRsyPiZWC+pCHA7nm7j5Sob0hK6ncAxkZEe0S8VlYPEXFWlQS+MEmj8i8DE9ra2ha3GjMzMzPrZfpyT/yrwNCKshWBJ8vez89/F7DovZhf9noBsIyktYFjgG0iYoakMaSe9spjFlYcvzDXLeDnEXF2eYMkHV30gso8B6xZ9v79uWwREdEGlLJ3TzFpZmZm1kf02Z74iJgDvCBpVwBJKwJ7ALcsZpWDSENfZklaDfhkjcdfAxwuaUBuzxqSVgVuBvaVtIKkgcCnC9R1BXCgpPfkLxfrAXfV2B4zMzMza1B9uSce4FDgTEm/zu9/EhGPL05FETFR0n3AQ8AzwK01Hn+tpI2A2yUBzAEOiYh7JV0MTAReAu4uHdPRmPiImCLpb6ShPm8BR3pmGjMzM7PmoQiPsmgS/qDNzMysp3nF1jrps8NpzMzMzMz6qr4+nMbKtLfPLRTX0tK6hFtiZmZmZt3hJN7MzMzMGpZ22KLbQ4Zj/H0NNyzIw2kaSF5N9n31boeZmZmZ1ZeT+MYyEqiaxOdVZM3MzMysCXg4TS8l6XjgS6RpJ58hrSq7NfAXSfOAjwAPAhcDHwdOAf5an9aamZmZ1Um/5uyTdhLfC0naCjgQGEH6jO4lJfETgGMiYkKOA3g1IrasU1PNzMzMrA6a86tL77cDMDYi2iPiNdIKrR25uKMdkkZJmiBpQltbW4830szMzKzu1K/7WwNyT3zj63DeyIhoA0rZexSdYtLMzMzMerfG/OrR990M7CtpBUkDgU/n8tnAwPo1y8zMzKyX6afubw3IPfG9UETcK+liYCLpwda7864xwFllD7aamZmZWRNSRLfnx7clTNJoYE5E/LIb1RQeTuMVW83MzKygundja7cPd3+xp+vuqPt11Mo98U3EybmZmZlZ3+AkvgFExOieqKeWnvjZc4s/BDuw1V8OzMzMrE4adHaZ7mrOqzYzMzMza2DuiV/Kemh8u5mZmZlB067Y2pxXbWZmZmbWwJzELwWSjpf0iKRbgA1y2QhJd0iaJGmspKGSVpV0T96/uaSQ9IH8/nFJLZLGSPqdpNskPSFp/zpempmZmVl99evX/a0BNWarG4ikrYADgRHAp4Bt8q4LgGMjYjNgMvDjiHgJWF7SIGAHYAKwg6S1gJcioj0fuzqwPbAXcPJSuxgzMzMz6xWcxC95OwBjI6I9Il4DrgBagSERcVOOOR/YMb++Dfhofv+z/HcHYHxZnZdHxMKImAqs1tGJJY2SNEHShLa2th69KDMzM7NeQer+1oD8YGvvczMpaV8L+AdwLBDAVWUx88ted/gvLyLagFL2XnixJzMzMzPr3dwTv+TdDOwraQVJA4FPA3OBGZJ2yDFfBEq98uOBQ4BHI2IhMJ00DOeWpdtsMzMzswbQpGPi3RO/hEXEvZIuBiYCLwF3511fAs6S1AI8ARyW46dJEin5h5S8vz8iZizdlpuZmZlZb6WIqHcbbOkoPJzGK7aamZlZQXUfUK5Pf6zbyWz88/q6X0et3BPfRFpaiifbTszNzMzMei8n8WZmZmbWuBp0THt3OYlvIrUMp2mf82rhelsGrET73FnF41sHF441MzMzs3druq8uklaSdH/e/ivpufx6jqTf99A5xknauuz9MEkP1FjHSEln9ER7zMzMzPosz07THCLiVdLqqUgaDcyJiF/WtVE9QNIyEfFWvdthZmZmZkteY371WAIk7Szpyvx6tKTzJY2X9JSkz0g6RdJkSVdLWjbHbSXpJkn3SLpG0uoFzjNS0mW5nkclnVK27zBJj0i6i7Rqa6l8FUl/l3R33j5a1s4/S7oV+HNP3xMzMzOz3k5St7dG5CS+Yx8EdgX2Bi4EboyITYF5wJ45kT8d2D8itgLOBU4qWPcI4ABgU+AASWvmLwA/ISXv2wPDy+JPA34TEdsAnwXOKds3HNgtIg5avMs0MzMzs0bTdMNpavDviHhT0mSgP3B1Lp8MDAM2ADYB/pO/wfUHXsgx1eYrLS+7PiJmAUiaCqwFrAyMi4iXc/nFwPo5fjdgeNk3xUGSBuTXV0TEvGoXIGkUMArg7LPP5pBDDi525WZmZmaNokHHtHeXk/iOzQeIiIWS3ox3VsVaSLpvAqZExEeqHPsqMLTs/YrAK5V1Zwvo+nPoB3w4Il4vL8xJfYdTzkREG9BWelt0dhozMzMz692a86tLz3gYWEXSRwAkLStp47xvHHCI3uk6/xJwYxf13QnslGfPWRb4XNm+a4Fvlt5IGtED7TczMzNrfE06O01jtroXiIg3gP2BX0iaCNwPbJd3twGzgYl53wCg0xlwIuIFYDRwO3Ar8GDZ7qOArSVNysNvjujBSzEzMzOzBqN3RolYH1d4OI0XezIzM7OC6j61S78D9u52Mrvw4ivqfh218pj4JtLS0lo8dsBKtdXtxNzMzMxsqXESb2ZmZmaNq0HHtHeXk/gmUtNwmtde6DqwFD9odWbNnVM4fnDrAJ6YOb1w/DpDViwca2ZmZtYMnMSbmZmZWePq13DD2XtEn/z9QdICSfdLekDSJZJacnnx7uJ36to013W/pOmSnsyvr5O0t6TjeqjN4yQ9nGegeUjSGZKGlO2/rez1qZKm5L+rSLpT0n2SduiJtpiZmZlZ79ZXe+LnRcQIAEl/IU3J+OvFqSgiJgOlusYAV0bEpWUhV3SvqYs4OCImSFoO+DnwD2Cn3I7tyuJGAStGxAJJBwKTI+J/erAdZmZmZo1BfbJPukvNcNXjgXXLCyQNkHS9pHslTZa0Ty7/qaSjy+JOkvStjiqWNFLSGfn1GEl/kHSHpCck7SzpXEkP5uS/dMzukm7P575E0oDKevMc9N8DPiBp83zcnPz3CtK88/dIOhY4Bdgn/zqwwuLeJDMzMzNrHH06iZe0DPBJYHLFrteB/SJiS2AX4Fd5ddVzgUPzsf2AA4ELazjlUOAjwLdJPfS/ATYGNpU0QtLKwA+B3fK5JwDfqVZRRCwAJgIbVpTvTf6lISJ+AfwIuDi/n1dx/aMkTZA0oa2trYbLMDMzM2sQTbpia18dTrOCpPvz6/HAnyr2C/iZpB2BhcAawGoRMU3Sq5K2AFYD7ouI4qsewT8jIiRNBl7MQ3GQNAUYBrwfGA7cmr4zsBxphdaOdOtJjYhoI60eCzUs9mRmZmZmvVtfTeLfHhPfgYOBVYCtIuJNSdOA5fO+c4CRwHtJPfO1mJ//Lix7XXq/DLAA+E9EHNRVRZL6A5sCD9bYBjMzM7Pm0aA96d3VnFcNg4GXcgK/C7BW2b6xwB7ANsA1PXzeO4CPSloXQFKrpPUrgyQtS3qw9ZmImNTDbTAzMzOzBtdXe+K78hfgn3nYywTgodKOiHhD0o3AzDwuvcdExMuSRgIXSXpPLv4h8EipXZLmA+8BrgP26cnzm5mZmfU5TTo7jSKi3m3oVfIDrfcCn4uIR+vdnh7kD9rMzMx6Wt1XWup32EHdznEWnndR3a+jVs351aUDkoYDjwHX97EE3szMzKxv6qfubw2oWYfTVBURU4F16t2OJaXo7DQtLa3MbW8vXG9rS0vN8bPmFl88d3DrAJ6ZNaNw/JqDhxaONTMzM2tETuLNzMzMrHF5dhrrbfIqsPvn1+MkbV3vNpmZmZlZ/TmJ7yXy6rJmZmZmVgv16/5W5DTSHpIelvSYpOOq7P+NpPvz9oikmWX7FpTtu6InLtuJ41Ii6afA9Ij4bX5/EvASsD8wA9hQ0gbA6cDHgWeANzqoaw5wGrAXMA/YJyJeXOIXYWZmZtaE8iKcZ5JytGeBuyVdkZ+nBCAivl0W/01gi7IqulqItGbuiV96zgUOhbensTyQ9I9gS+BbEbE+sB+wATA8x27XQV2twB0RsTlwM/CVakGSRkmaIGlCW1tbT16LmZmZWa+gfv26vRWwLfBYRDwREW8Af6Xz9XwOAi7qgcvrkHvil5KImCbpVUlbAKsB9wGvAndFxJM5bEfgorzI1POSbuigujeAK/Pre0jfCqudsw0oZe9RdHYaMzMzM1vEGqRREiXPAh+qFihpLWBtoDyPW17SBOAt4OSIuLy7DXISv3SdA4wE3kvqmQdYnMz6zXhnla4F+HM0MzOzJtWvB2ankTQKGFVW1JY7QxfHgcCluVO2ZK2IeE7SOsANkiZHxOOL217wcJqlbSywB7ANcE2V/TcDB0jqL2l1YJel2TgzMzOzZhQRbRGxddlWmcA/B6xZ9v79uayaA6kYShMRz+W/TwDjWHS8/GJxD+5SFBFvSLoRmBkRC6R3rRA2FtgVmAo8Ddy+lJtoZmZm1lB6oie+gLuB9SStTUreDwS+UBkkaUNgKGU5nKShQHtEzJe0MvBR4JTuNshJ/FKUH2j9MPA5gIgYR/o2Rn4fwDeqHRsRO5e9HlD2+lLg0iLnb2lpLdzW1paWwrGLEz+4dUDXQWW8CquZmZnVS0S8JekbpJEU/YFzI2JKnn1wQkSUpo08EPhr2bBngI2AsyUtJI2CObl8VpvF5SR+KZE0nPQw6tiIeLTe7TEzMzPrC5ZSTzwR8S/gXxVlP6p4P7rKcbcBm/Z0e5zELyX5G9c69WxD0dlpWlpaaZ87u3C9La0DmfXkPwrHD157H56aNaNw/FqDh/LCa7MKx68+aHDN9ZuZmZk1EifxZmZmZtawllZPfG/TnFe9BEkaI2n/Tvbvm4fWmJmZmZktFvfEL337ksbGd/uBBjMzM7Nm16w98U7ia5CfQJ4eEb/N708CXgLWI62a+gxpNdVS/MnA3qTVua4FLsvvd5L0Q+CzwEDgLKAFeBw4PCJmSNoG+BOwEPgP8MmI2ETS8sAfgK1zvd+JiBuX9LWbmZmZWe/RnF9dFt+5wKHw9nSRB5KW3d0AGJ73bZf3rwTsB2wcEZsBJ+ank68AvhsRI/JKXRcAx+aYycCP87nOA74aESNIq7KWHEmajXJT4CDg/JzYv4ukUZImSJrQ1ra4i46ZmZmZ9V79pG5vjcg98TWIiGmSXpW0BbAacB+wA3BRXlr3eUk35PBZwOvAnyRdSRpCswhJg4EhEXFTLjofuETSEGBgRJQWCvh/wF759fbA6bk9D0l6ClgfmFSlvW1AKXuPorPTmJmZmVnv5iS+ducAI4H3knrmd68WlBcF2Bb4GLA/aRGnXZdSG83MzMyaQrOOiW/Oq+6escAewDakVbtuBg6Q1F/S6sAuAJIGAIPzwgDfBjbPx88mjYMnImYBMyTtkPd9EbgpImYCsyV9KJcfWHb+8cDB+RzrAx8AHl4SF2pmZmZmvZN74msUEW9IuhGYGRELJI0l9bBPBZ4GSkNgBgL/yOPVBXwnl/8V+KOko0g99F8CzpLUAjwBHJbjvpzjFgI3kYbnAPwe+IOkyaQHW0dGxPwld8VmZmZmvVez9sQrIurdhoaSH2i9F/hcRDy6BM8zICLm5NfHAatHxLe6UaU/aDMzM+tpdX8qdKVjv9XtHOfVX5xW9+uolXvia5AXaboSGLskE/hsT0nfJ31GT5HG4ZuZmZlZmWbtiXcSX4OImAqss5TOdTFwcU/WObe9vVBca0sLs+cWn8lmYGsr7XNnF45vaR1I+6yniscPXovJ018qHL/piqsy78VbCsevsNr2zJvxSLHYoesXrtfMzMxsSXESb2ZmZmYNq1l74pvzqutA0hhJ+/dgfUMkfb2n6jMzMzOzxuEkvnENAZzEm5mZWVPr169ft7dG1Jit7mUk/VTS0WXvT5L0LUlnSHpY0nXAqmX7T5Y0VdIkSb/MZWMk/U7SbZKeKO+1l/RdSXfn+J/k4pOBD0q6X9KpS+lSzczMzKwXcBLfM84FDoW3p6A8EHgW2AAYnvdtl/evBOwHbBwRmwEnltWzOrA9sBcpSUfS7sB6wLbACGArSTsCxwGPR8SIiPhutUZJGiVpgqQJbW1tPXvFZmZmZr1As/bE+8HWHhAR0yS9KmkLYDXgPmAH4KKIWAA8L+mGHD4LeB34k6QrSVNWllweEQuBqZJWy2W75+2+/H4AKal/ukC72oBS9h5FZ6cxMzMzs97NSXzPOYc0l/t7ST3zu1cLioi3JG0LfIy0Yus3SCu+ApSvvKqyvz+PiLPL65E0rIfabQmw4/QAACAASURBVGZmZtaw+jdoT3p3NedVLxljgT2AbYBrgJuBAyT1l7Q6sAuklViBwRHxL+DbwOZd1HsNcHg+DklrSFoVmA0MXCJXYmZmZma9mnvie0hEvCHpRmBmRCyQNJbUwz6VNPTl9hw6EPiHpOVJvezf6aLeayVtBNwuCWAOcEhEPC7pVkkPAP/uaFy8mZmZWV/WqGPau0sRUe829An5gdZ7gc9FxKP1bk8V/qDNzMysp6nrkCVr2AnHdzvHmfZ/J9X9OmrlnvgeIGk46QHVsb00gTczMzPrk5q1J95JfA+IiKnAOvVuR1eKzk7T2tLC7LlzC9c7sLWV9rmzC8e3tA6kfc6M4vEDhjJ9TvH6VxwwkMeu/d/C8evu/ivaZz5erC1DPgjAuY9MLBR/+PpdPfJgZmZmVjsn8WZmZmbWsJq1J745r7oASatJ+n959dR7JN0uab/FqGeYpAckfSKvrnq/pDl5Jdf7JV2wJM9vZmZmZn2Pe+KrUJoG5nLg/Ij4Qi5bC9h7ceuMiGtI00UiaRxwTERMWFrnNzMzM+uL3BNv5XYF3oiIs0oFEfFURJwuaXlJ50maLOk+SaX534dJGi/p3rxtV+REkr6Te+ofkHR0V+fPx1Rtg5mZmZk1B/fEV7cxabrIao4EIiI2lbQhcK2k9YGXgI9HxOuS1gMuArbu7CSStgIOAz5EmqLpTkk3dXH+DtsQEa9X1D8KGAVw9tlnc/Ahh3R+1WZmZmYNpll74p3EFyDpTGB74A3gWeB0gIh4SNJTwPrAU8AZkkYAC3JZV7YnTUs5N5/nMmCHzs4fEdvk19XaMKn8uIhoA9pKb4vOTmNmZmZmvZuT+OqmAJ8tvYmIIyWtDEwgJfHVfBt4EdicNEzp9Q7iunt+MzMzM8v6qeHWaeoRzfn7Q9duAJaX9LWyspb8dzxwMEAeRvMB4GFgMPBCRCwEvgj0L3Ce8cC+kloktQL75bLOzt9ZG8zMzMysCbgnvoqICEn7Ar+R9D3gZWAucCzwD+APkiYDbwEjI2K+pN8Df5d0KHB1ju/qPPdKGgPclYvOiYj7ADo5P8Dvq7WhJ67dzMzMrJF4TLwtIiJeAA7sYPdhVeIfBTYrKzo2l08DNqmI3bns9a+BX9dy/vwA67vaYGZmZmbNQRFR7zbY0uEP2szMzHpa3Qekb/bbk7ud40w6+ri6X0et3BPfRNrbuxzhA0BLSyvtc2cVrreldTAXPT6lcPxBH9yYp2bNKBy/1uChTJ8zu3D8igMGMvHVFwvHb77SarTPKdaelgFDAZg5Z06h+CEDBgAwb/pDheJXWHHDQnFmZmbW3JzEm5mZmVnDatYx8c151VVIWiDp/rJtmKTbaqzjaEktZe+n5VVVJ0uaKulEScv3fOvNzMzMrJk4iX/HvIgYUbZNi4jtKoMkdfbrxdEsOhUkwC4RsSmwLbAOcHbPNdnMzMzMmpGT+E5ImpP/7ixpvKQrgKmSWiVdJWmipAckHSDpKOB9wI2SbqysKyLmAEeQ5oVfUcmp+fjJkg7I5zpT0t759VhJ5+bXh0s6Kf9C8KCkP0qaIulaSSsspVtiZmZm1qv069ev21sjasxWLxkrlA2lGVtl/5bAtyJifWAP4PmI2DwiNgGujojfAc+Tet53qXaCiHgNeBJYD/gMMIK0wutuwKmSVict5LRDPmQNYHh+vQNwc369HnBmRGwMzKRsdddykkZJmiBpQltbW/E7YWZmZma9mh9sfce8iBjRyf67IuLJ/Hoy8CtJvwCujIjxNZynNIXR9sBFEbEAeFHSTcA2pCT+aEnDganA0JzcfwQ4ClgJeDIi7s/13AMMq3aiiGgDStl7FJ2dxszMzKxRNGpPenc151Uvnrcz4Ih4hNQzPxk4UdKPilQgaSAp4X6ko5iIeA4YQurtv5mU1H8emBMRpXkWy1dnXYC/jJmZmZk1FSfxi0HS+4D2iLgQOJWU0APMBgZ2cMwA4PfA5RExg5ScHyCpv6RVgB2Bu3L4HaSHZEtJ/DH5r5mZmZmVadYx8e7BXTybksawLwTeBL6Wy9uAqyU9XzYu/kZJIn1hGguckMvHkobITCStpvq9iPhv3jce2D0iHpP0FLAiTuLNzMzMLHMSn0XEgI7KImIcMK6s/BrgmirxpwOnl70f1sn5Avhu3ir3/Qn4U379JtBatm8asEnZ+192fFVmZmZmfVv/Bu1J7y6lXNKagD9oMzMz62nqOmTJ+kjb77qd49w+6qi6X0et3BPfROa2txeKa21poX3OjML1tgwYSvvsF4vHD1yN516bWTh+jUFDam7Pk7OmF45fe/CKvDT7tUKxqw4cBMBDM14pFL/h0JUBaJ/5ZBeRScuQtQE46f5bCsUfP2L7QnFmZmZ9VbP2xDfnVZuZmZmZNbA+kcTn1U9vkfTJsrLPSbp6MeoaJ2nrGuL3kHSXpIfyQlEXS/pAF8ccIenQWtvWQV1jJO3fE3WZmZmZNZr+/fp1e2tEfWI4TUSEpCOASyTdSLqun5HmWu+QpGUi4q3FPa+kTUgPsu4dEQ/msr1Jc8E/3Ul7z1rcc5qZmZmZ9YkkHiAiHpD0T+BY0mwuFwIXSloemAccFhEPSxoJfAYYAPSXtAdwHrA58BCwAoCk/qQZYrYmPRR6bkT8puK0xwI/KyXwuR1XlF5L+gowClgOeAz4YkS0SxpNWrzpl5LGAXcCu5AWefpyRIzP5z8Z2Bl4D3BmRJydp6s8Hfg48AzwRnfvnZmZmVmjatSe9O7qa1f9E+ALwCeB3wI7RMQWwI9IPfMlWwL7R8ROpDne2yNiI+DHwFY5ZgSwRkRsEhGbkhL9ShsD93bSnssiYpuI2Bx4EPhyB3HLRMS2pAWefpzLvgzMiohtgG2Ar0haG9gP2AAYDhwKbNfRySWNkjRB0oS2trZOmmlmZmZmjaTP9MQDRMRcSRcDc4BBwBhJ65F60pctC/1PRJSmL9kR+F0+fpKkSbn8CWAdSacDVwHXdnZuSSsB1wMtQFuev30TSSeSetgHUGVu+eyy/Pce0lAcgN2BzcrGuw8G1svtvSgiFgDPS7qhk/vRRlqACiCKzk5jZmZm1ij69+9rfdLF9MWrXpi3E4AbI2IT4NPA8mUxc7uqJCJmkIbYjAOOAM6pEjaF1KtPRLwaESNISXNp4agxwDdyT/5PKtpQbn7+u4B3vlgJ+GZEjMjb2hHR6RcJMzMzM2sOfTGJLxkMPJdfj+wk7mbSEJzSg6qb5dcrA/0i4u/AD8nJeoVTgOMlbVRW1lL2eiDwgqRlgYNrbP81wNfysUhaX1Jrbu8BkvpLWp00lt7MzMysKXl2mr7nFOB8ST8kDYfpyB+A8yQ9SBq3fk8uXyOXlz7Z70OaHhLSDDMRMVnSt4ALJA0CXiHNSlMa1/5/pIdWX85/B9bQ/nNIQ2vuzQ+zvgzsC4wFdgWm5nPdXkOdZmZmZtYH9LkkPiJGl71dv+z1D/P+MaRhLqX4ecCBHVT3rt73yukhI+IqOviSEBF/IH1J6LCNEbFz2etXyGPiI2Ih8IO8VfpGB+01MzMzayqN2pPeXYqIerfBlg5/0GZmZtbTVO8G7HnRud3Oca466PAuryNPS34a0B84JyJOrtg/EjiVd4ZznxER5+R9XyJ3KAMnRsT53W1zn+uJt44VnZ2mtaWlcGwpfsIrLxSO33rl1WmfO6twfEvrYNpnPVM8fvCaPHvXKYXj37/t95j30h2FYldY9cMAnDzxtkLxx22eZgC96pnHCsXvuea6AMx78ZZi7VltewBemTO7UPzKA2oZ0WVmZtb7LY2e+Lx+z5mkdXqeBe6WdEVETK0IvTgivlFx7IqkodaltYfuycfO6E6bmvP3BzMzMzOz4rYFHouIJyLiDeCvwD4Fj/0EeXrznLj/B9ijuw1yEm9mZmZmDatfv37d3soXyMzbqIrTrAGUDwt4NpdV+qykSZIulbRmjcfWxMNpzMzMzKypVSyQubj+SVqQc76krwLnk2YUXCLcE9/LSWqVdJWkiZIekHSApGmSTpE0WdJdktatdzvNzMzM6mEpzRP/HLBm2fv3884DrMDbC3+WFvA8B9iq6LGLw0l877cH8HxEbJ5Xn706l8/KK8GeAfy22oHlPw21tXX3y6WZmZlZ07obWE/S2pKWI01PfkV5QF6Es2Rv0vpDkBbw3F3SUElDgd1zWbd4OE3vNxn4laRfAFdGxPi09hMX5f0XAb+pdmDFT0NRy4wzZmZmZo1gacxOExFvSfoGKfnuD5wbEVMk/RSYEBFXAEdJ2ht4C5gOjMzHTpd0AumLAMBPI2J6d9vkJL6Xi4hHJG0JfAo4UdL1pV3lYUu/ZWZmZmbNIyL+BfyrouxHZa+/D3y/g2PPBc7tyfY4ie/lJL0PmB4RF0qaCfxP3nUAcHL+e3u92mdmZmZWT826YquT+N5vU+BUSQuBN4GvAZcCQyVNAuYDB9WxfWZmZma2lDmJ7+Ui4hoqHn7IY+JPjYhj69IoMzMzs16if3/Vuwl1oQgPp240kqYBW0fEKzUc5g/azMzMelrdM+hDrry42znOhXsdUPfrqJV74htQRAxbnOOKzk7T2tJSOLYU394+t3B8S0trzfGvzJldOH7lAQOZ99JtheNXWHU72me/XKwtA1cB4KpnHisUv+eaaQr/3z94T6H4r2+UppSdN/2hQvErrLghADe/+EwXkcmOq6Vpajf59c8KxT/wnR8UijMzM6uXZh0T35xXbWZmZmbWwJqqJ17SSkBpisb3AguAUhfsthHxRjfqnhMRA3oqzszMzMy61qw98U2VxEfEq8AIAEmjgTkR8cu6NsrMzMzMrEbN+dWljKSPSbpP0mRJ50p6Ty6fJmnl/HprSePy6wGSzsvxkyR9tqyukyRNlHSHpNVy2dqSbs/xJ5bFStKpkh7I+w7I5Wfm1b6QNFbSufn14bn+YZIelPRHSVMkXStphaV0u8zMzMx6lf79+nV7a0SN2eqeszwwBjggIjYl/TLxtS6O+T9gVkRsGhGbATfk8lbgjojYHLgZ+EouPw34Q67/hbJ6PkP6VWBzYDfSXPCrA+OBHXLMGsDw/HqHXC/AesCZEbExMBN4+4uEmZmZmfV9zZ7E9weejIhH8vvzgR27OGY34MzSm4iYkV++AVyZX98DDMuvPwpclF//uaye7YGLImJBRLwI3ARsQ07iJQ0HpgIv5uT+I0BpypUnI+L+KudahKRRkiZImtDW1tbFZZmZmZk1nmbtiW+qMfE1eot3vuQsXyD+zXhn0v0FLHpvC89fGhHPSRoC7EHqeV8R+Dxp/P7s/HDu/LJDFgBVh9NERBtQyt6jlmkjzczMzKz3asyvHj1nATBM0rr5/RdJPeIA04Ct8uvy4Sr/AY4svZE0tItz3AocmF8fXFY+HjhAUn9Jq5B+Abgr77sDOJqUxI8Hjsl/zczMzKxMs/bEN2are87rwGHAJZImAwuBs/K+nwCnSZpASvZLTgSG5gdSJwK7dHGObwFH5vrXKCsfC0wCJpLG1X8vIv6b940HlomIx4B7Sb3xTuLNzMzMDGji4TQRMbrs7RZV9o8H1q9SPgf4UpXyAWWvLwUuza+fJI1nL/lhLg/gu3mrrOtPwJ/y6zdJD82W9k0DNil77ykyzczMrGk1ak96d+mdYdzWx/mDNjMzs56mejfgqHFXdTvH+d3Oe9b9OmrVtD3xzajog62tLS2FYxc3vr19buH4lpbW2uPnvFo8fsBKzJo7p1Ds4Nb0g0v7nBldRJbqTo9MFG1/S0v60aV97uxi8a0Dc/ysgvGDAXjutZmF4tcYNKTme29mZrY0NWtPfHNetZmZmZlZA3MSvwRIOj6vpjpJ0v2SPtRJ7IY55j5JH+wk7gdLprVmZmZmjcuz01iPkPQRYC9gy7yi627AM50csi9waURsERGPdxLnJN7MzMzMAI+JXxJWB16JiPkAEfEKgKQfAZ8mLcx0G/BV4JOk+eAXSPpYROwi6RDgKGA54E7g68BJwAqS7gemAI8D0yPit7nuk4CXIuK0pXeZZmZmZvXXv39z9kk351UvWdcCa0p6RNLvJe2Uy8+IiG0iYhNSIr9XRPyLNC/9b3ICvxFwAPDRiBhBmp/+4Ig4DpgXESMi4mDgXOBQAEn9SItJXbhUr9LMzMzM6sZJfA/L88hvBYwCXgYuljQS2EXSnXnRp12Bjasc/rF87N251/1jwDpVzjENeFXSFsDuwH0R8a7pWCSNkjRB0oS2trYeuT4zMzOz3qRZx8R7OM0SEBELgHHAuJy0fxXYDNg6Ip6RNBpYvsqhAs6PiO8XOM05wEjgvaSe+WrtaANK2XvUMg2kmZmZmfVejfnVoxeTtIGk9cqKRgAP59evSBoA7N/B4dcD+0taNde1oqS18r43JS1bFjsW2APYBrimxy7AzMzMrIG4J956ygDgdElDgLeAx0hDa2YCDwD/Be6udmBETJX0Q+DaPNb9TeBI4ClSj/okSfdGxMER8YakG4GZueffzMzMzJqEk/geFhH3ANtV2fXDvFXGj654fzFwcZW4Y4FjS+9zkv9h4HPda7GZmZlZ42rUnvTuchLfgCQNB64ExkbEo0WPa21pKXyOWmIXJ76lpXXJxg9Yqab4wa0Daqx/aG3xtba/dWCN8YNril9j0JDiddfYdjMzM1vynMQ3oIiYSpVZa7rS3j63UFxLSyvT58wuXO+KAwZSy0OzrS0ttM+dVTi+pXUw7bNfLh4/cBVmzZ1TOH5w6wDaX3uhWN2DVgegfc67JgOqHp+/TLTPLXY/S8l7LZ9Vqr/Y/Swl+7W0p2hbSu2pNd7MzKw7+jVpT3xzXrWZmZmZWQNr2iRe0vsl/UPSo5Iel3SapOUWo56fStqtSvnOkq7s4tiRks6oUn6EpEM7Oa7Lus3MzMyaQX+p21sjasokXpKAy4DLI2I9YH3SrDIn1VpXRPwoIq7ryfZFxFkRcUFP1mlmZmZmfUdTJvGkFVNfj4jz4O3Fmb4NHC7p65Iuk3R17qU/BUBSf0ljJD0gabKkb+fyMZL2z6/3kPSQpHuBz5ROJqlV0rmS7pJ0n6R9KhskaU9Jt0taWdJoScfk8nUlXSdpoqR7JX2w4rhtcp0frKzTzMzMrK/rr37d3hpRsz7YujFwT3lBRLwm6WnSPRkBbAHMBx6WdDqwKrBGRGwCkOeBf5uk5YE/kr4gPMai00QeD9wQEYfn4+6SdF3ZsfsB3wE+FREztOjPOn8BTo6Isfkc/YA183HbAacD+0TE0925IWZmZmbWOBrzq8eSd31EzIqI14GpwFrAE8A6kk6XtAfwWsUxGwJPRsSjERHAhWX7dgeOk3Q/MA5YHvhA3rcraf73PSNiRnmFkgaSvjiMBYiI1yOiNA3MRqQFoD7dUQIvaZSkCZImtLW1LcZtMDMzM+vdmnVMfLP2xE8F9i8vkDSIlFi/ReqBL1kALJN7yDcHPgEcAXweOLzg+QR8NiIerjjnh4DHSdNFrg9MqOEaXiB9GdgCeL5aQES0kRJ9gKhl6j8zMzMz672atSf+eqClNAOMpP7Ar4AxQNUJzyWtDPSLiL+TVl7dsiLkIWBY2dj0g8r2XQN8Mz9Qi6QtyvY9BXwWuEDSxuUVRsRs4FlJ++bj3iOptKrSTGBP4OeSdi543WZmZmZ9SrP2xDdlEp+Hu+wHfE7So8AjwOvADzo5bA1gXB4ScyHw/Yo6XwdGAVflB1tfKtt9ArAsMEnSlPy+/NiHgIOBS6o8oPpF4ChJk4DbgPeWHfcisBdwZu7VNzMzM7MmoJTPWhMoPJzGK7Z2UrdXbO2yPV6x1cysqdS9G/u3D9zV7WT26E22rft11MpJfPPwB21mZmY9re7Jb7Mm8c36YKuZmZmZ9QGNOqa9u5zEN5FahmjMnlt8SMTA1lbaX3uucHzLoDVqHu7y2Mxiw1cA1h2yEi/NrpwBtGOrDhzExU9MLRR7wDrDAXhkxiuF4tcfujJA4eFGrS3pueUnZ00vFL/24BUBuOPlqhMUvcuHV3kfAO0znywU3zJkbZ6ZNaPrwGzNwUMLD9WBPFyn4NAkeGd4kpmZWbPrkw+2Slog6f68uuolZTO69ETdIyRFniu+VDZM0hcqYj7VU+fspC075wWfzMzMzJpS/379ur01osZsddfmRcSIvLrqG6R53XvKQcAtLDqF5DDgC2XvRwBLPIkHdgacxJuZmZk1mWYYTjMe2EzSp0nzuy8HvAocHBEvStoJOC3HBrAjMAC4GBhEukdfi4jxeZ73zwEfB8ZLWj5PLXkysFGefvIi4EhgBUnbAz8HrgROBzYhTTU5OiL+IWkksC/QCqwH/DK374ukBac+FRHTJY0DJgI75fYcTprC8ghggaRDgG9GxPgev3tmZmZmvZjHxPdBkpYBPglcTeo9/3BEhKT/Ab4H/C9wDHBkRNwqaQBpvvhRwDURcVJeCKo0HGc74MmIeDwn1nsCfweOA46JiL3yeV8Eto6Ib+T3PwNuiIjDJQ0B7pJ0Xa5zE9Kqq8sDjwHHRsQWkn4DHAr8Nse1RMQISTsC50bEJpLOAuZExC97/u6ZmZmZWW/VV4fTrJB7xScATwN/At4PXCNpMvBdoLQ66q3AryUdBQyJiLeAu4HDJI0GNs0rp0IaQvPX/PqvLDqkpjO7A8flNo0jJewfyPtujIjZEfEyMAv4Zy6fTBqmU3IRQETcDAzKXwY6JWmUpAmSJrS1tRVsqpmZmVnj6Cd1e2tEfbUnfl5EjCgvkHQ68OuIuELSzsBogIg4WdJVpDHst0r6RETcnHu89wTGSPo18Bfgs8A+ko4nzYu6kqSBBdoj4LMR8XBFmz5EGjZTsrDs/UIW/Xwq50Dtck7UiGgDStl74cWezMzMzKx366tJfDWDgdI8iF8qFUr6YERMBiZL2gbYUNI84NmI+KOk9wBbAv8FJkXEJ8qOPR/YD5gClCfzsyveXwN8U9I383CeLSLivhrbfwBwYx5nPysiZkmaTRq3b2ZmZtaU+quvDizpXDNd9WjgEkn3AOWTfB+dp6KcBLwJ/Js068tESfeRkufTSENnxlbU+fdcPon0gOlESd8GbgSG52kuDwBOID3QOkn6/+zdeZxcVZn/8c83cSHV3elsQCAsQXYCIZAAAgHZ1RlEGMMgshhRoqI46KCg+BN0hhFFxwUGoXEEFESGPaKyCATCGhJCNvYlskPWTi8BJXl+f9xTpKh0p291d9Jd6e/bV71y69znnnvurcbXqVPnPkfz0vtKvZXaczHw+VT2R+CodJ79OlGnmZmZmVWh9XIkPiJq2yi7Gbi5jfJT26jiivQq9bk2jp0MTE5vDyrbvUfZ+y+2cfzlwOUl70e2tw+4MiJOKzv+aWD0aq03MzMz6yP6anYaRXQ4tdp6WMqEc3pETO9CNf6gzczMrLv1eA/6imdmd7mP89ltR/f4dVRqvRyJX99ExAE93QYzMzOz3qivzol3J74PyZudplCoobGlOXe99TW1tDYvyR1fqB1MS2tr7viaQoE3m5bljt+obiAvNeZvz+b1g5m56I1csbsN3RiAv+Wsf8v6wQC0LnstV3xh4Cadqn/yi8/kij9ii22z9rQ0dRCZ2lNTxwuNi3PFAmxVPyR33cX6W1saK4iv55+v/k3u+D8de1LuWDMzs2riTryZmZmZVa2+Oie+b/7+0EWSjpQUknZI7zeVdF0bcRtIelLSLiVl35R0SSfPO1/SsM633MzMzMzWB+7Ed86xwH3pXyLi1YiY0EbcO8BpwEXKjAC+BJzZ0Qkk+VcSMzMzsw701RVb3YmvkKRaYDxZrvZPp7KRkuam7YmSJku6C7gzIm4FXgNOBH5Glq9+vKSHJc2U9FdJG6djz5H0O0n3A7+TNFTS7ZLmSfo16QlwSTWS/pTy0s9NuejNzMzMrI9wJ75ynwRuTTnaF0ka20bM7sCEiPhIen8acC6wYUT8jmwU/8MRsRvwB+BbJcfuBBwSEccCZwP3RcQosoWmtkgxHwNejYhdI2Jn4Na2GippkqTpkqY3NDR05ZrNzMzMeqX+/fp1+VWNPGWjcseSreAKWQf8WODCspg7IuLdlB4R8Woamb8lFW0GXCNpE+ADwAslx06OiOVpe3/gX1Idf5JUTFkyB/ippB8Bt0TE1LYaGhENQLH3Hnmz05iZmZnZe0n6GFkfsD/w64g4r2z/N4AvkE2nXgCcFBF/S/tWkPXfAF6MiCO62h534isgaQjZyqy7SAqyDzGA/ykLbau3vDK9AC4A/jsiJks6gGyKzZqOfY+IeFrS7sA/Af8p6c6I+EEl12JmZma2PlgX2Wkk9Sfr7x0KvAw8ImlyRDxeEjYTGBcRrZK+DPwYKE55Xh4RY7qzTdX5+0HPmQD8LiK2jIiREbE52Sj65hXWUw+8krY/u4a4e4HPAEj6ODA4bW8KtEbElcD5ZNN3zMzMzGzt2BN4NiKej4i/k83G+GRpQETcHRHFhXAeIpt5sdZ4JL4yxwI/Kiu7Hvh2hfWcA1ybpsfcBWzVTtz3gaslzQMeAF5M5bsA50taCfwD+HKF5zczMzNbL3THiq2SJgGTSooa0rTkohHASyXvXwb2WkOVnwf+UvJ+A0nTyabanBcRN3WxySgiulqHVQd/0GZmZtbdejw/4+QXn+lyH+eILbZd43VImgB8LCK+kN6fAOwVEV9tI/Z44KvARyLi7VQ2IiJekfQhsgHcgyPiua602SPxZmZmZla11tGKra/w3unTm7FqavS7JB0CnEVJBx4gIl5J/z4vaQqwG+BOvOWTNztNoVCTO/bd+KYF+ePrNqSxpTl3fH1NLYubm3LHD6mt482mZbnjN6obyJNLFuaK3WFwtmDurEVv5IrfdejGALQ25Ysv1GXx97+52v8vtGnfjUYA8K83/j5X/P8d9RkAljbnu/+Damt5Oue9Adhu8LDK/3Za8n+2hZo6zp/9YO74b47emgcN/wAAIABJREFUm9ebGnPFDq+rz12vmZn1OY8A20raiqzz/mnSc4tFknYDLiEbsX+zpHww2bOMb0saBuxL9tBrl7gTb2ZmZmZVq183zInvSES8I+mrwG1k2Ql/ExHzJP0AmB4Rk8mSjdSSPfcIq1JJ7ghckp5l7Ec2J/7xNk9UgV6VnUaZ+1ImlmLZ0ZLaXMyog7qmSBrXQcz7JC2QVJ7n8/y0Sur5aQXWTSs9f6rnxLSi6py0OuvpHcQfKWmnDmIOkHTLmmLMzMzMrHtFxJ8jYruI2Doizk1l30sdeCLikIjYOCLGpNcRqfyBiNglLdK5S0T8b3e0p1eNxEdESPoS2TeYu8na919kK5S2S9L7IuKdTpzyUOBp4GhJ345VT/lOAoZExIo0b2ku8GreSiW9L9V9GnBYWuzpg8CJHRx6JNmCUF3+dmZmZmbWF6yjOfG9Tq8aiQeIiLnAH4EzgO8BVwJXppHsByRtD5BGyCenlVDvlDRA0h8kPSHpRmBAiusv6fKSEfGvl5yuuPrqi8DeKX4y2U8hMyQdA4wDrpL0WDrHWEn3SJoh6ba06mpx5P/nKX3Qv5GlnTw9Il5N1/V2RFyaYk+W9IikWZKul1SQtA9wBFnqyMckbS1pG0l/TXGPSto6tbtW0nWSnpR0ldRH/3rNzMzM+qheNRJf4vvAo8DfgfHAuWku0iFkI/OfSnG7A6MjYnFa6rY1InaUNDodDzAGGBEROwNIGpT+3QA4BPgiMIisQ/9ARBwhqbm4qlZacev0iJgu6f1kq61+MiIWpE7+ucBJ6VwfiIhx6bizgBntXN8NJR36/wQ+HxEXpC8Qt0TEdWnfw2Tzpm5M7e1H9mT0bsAosl8H7id7QOK+8pOU5jy95JJLOP744zq88WZmZmbVpK+OxPfKTnxEtEi6BmgGBgKXS9qWLNf5+0tC74iIxWl7f+CX6fjZkman8ueBD0m6APgTcHsqPxy4OyKWS7oe+H+STouIFWto2vbAzsAdafC7P/Bayf5rcl7izqnzPohs1P+28gBJdWRfPm5M1/RWKgeYFhEvp/ePASNpoxOfFikoLlQQlWQNMTMzM7Peq1d24pOV6fUfZJ3toySNBKaUxHTYK42IJZJ2BT4KfAn4V7KR82OB8ZLmp9ChwEHAHWuoTsC8iNi7nf2l7ZkHjCVL6F/ucuDIiJglaSJwQEfXUebtku0V9O7P0czMzGyt6d+v180OXyeq4arrWZVMf+Ia4u4l5euUtDMwOm0PA/pFxPXAd4HdJQ0E9gO2iIiRETES+ApZx75cE1CXtp8CNpRUnD//fkmj2mnPD8nmtw9PsR+Q9IW0rw54LU3PKZ3j8u65IqIJeFnSken4D0oqrOH6zczMzKyPqIZO/I+BH0qayZpHnH9F9sDnE8APWDUffQQwJU07uZLsgdOjgLtKV9ICbgY+kbLIlLocuDgd3x+YAPxI0izgMWCfthoTEX8GLgT+Kmke2Rz9gWn3/wMeJpvP/mTJYX8Avpke4t0aOAH4Wpoa9AAwfA3Xb2ZmZtbn9JO6/KpGWpVV0dZz/qDNzMysu/V4D/jeN17qch9n/4037/HrqJTnUpuZmZlZ1eq/DlZs7Y3cie9D8manKRRqaGltzV1vTaFAY0tz7vj6mtqK61/c3JQ7fkhtHUub87dnUG0tLzUuyRW7ef1gAFqbFuSKL9RtmMW35Gt/oSZ7/KK18aV88fWbA7B80dxc8QOG7pzVX0H7K8lqVCjUVHzv894byO5P3s8Kss/rlWVLc8WOGDgIqPyzMjMz6wnuxJuZmZlZ1eqreeL75u8P64ikzSTdLOkZSc9LujBlmTlAUmNamXV2WpV1o3TMREkXltUzRVJxEak/SxqUXqf0xHWZmZmZWc9yJ34tUbYq0w3ATRGxLbAtMIAs2w7A1IgYExGjgUfIUlx2KCL+KSKWki0U5U68mZmZ9Wn9UJdf1cid+LXnIOCtiLgMIK0E+3XgRLJVWoF3O/t1QK6JvpLmp9z35wFbp9H887u78WZmZmbWe3lO/NozilW56gGIiGVphdhtgP1S7vmhZCu9fqck9BhJ40veb9NG/WcCO0fEmPYaIGkSMAngkksu4fjjj2sv1MzMzKwq9avOgfQucye+50yNiMMBJJ1BNs3mS2nfNRHx1WKgpCmdOUFENAANxbeVZBkxMzMzs97L02nWnseBsaUFkgaSrbr6VFnsZGD/ddQuMzMzs/WGuuF/1cid+LXnTqAg6UQASf2BnwIXAsvLYscDz1VYfxPZXHozMzMz62PciV9LIiKAo4AJkp4BFgErI+LcFLJfeih1FnAC8O8V1r8IuF/SXD/YamZmZn1VP6nLr2qkrK9pa5ukfYCrgaMi4tEeaII/aDMzM+tuPd4DnrnojS73cXYbunGPX0el/GDrOhIRDwBb9nQ7zMzMzKz6uRPfh+TNTlMo1NDUkj+TTV1NDS2trbnjawqF3G0ptqfS+itt/8Lmplyxw2qzxxBaWxpzxRdq6rP4Cu49wLzFb+aKHzVko6z+Za/lq3/gJgC5708l9way+1Ppva/0b+HxxQtyx+80ZENmLHw9V+zYYcMBaG1elK8ttUMBWJzz/gyp9SMsZmZrQ1+dG95Xr9vMzMzMrGp1aydeUnOOmNMkFbrzvHlJGiTplJL3m0q6rpN1PZweTH1R0oK0/ZikkXnug5mZmZl1XV99sLUnRuJPAyrqxKf0jN1hEPBuJz4iXo2ICZ2pKCL2Squlfo9scaYx6TU/z/GSPJXJzMzMzDplrXTiJR0gaYqk6yQ9KekqZb4GbArcLenuFHuYpAclPSrpWkm1qXy+pB9JehQ4WtLHUswsSXemmBpJv5E0TdJMSZ9M5RMl3Zza8Iyks1PTzgO2TiPm56dR87npmA0kXSZpTqrrwJK6bpB0a6rrxznvwbmprQ9J2jiVXS7pYkkPAz+WNCbtny3pRkmDJW0kaUaK31VSSNoivX9OUiHV80tJD0h6XlKnvoiYmZmZVbt+3fCqRmuz3buRjbrvBHwI2Dcifgm8ChwYEQdKGgZ8FzgkInYHpgPfKKljUSq/E7gU+FRE7AocnfafBdwVEXsCBwLnS6pJ+/YEPgWMJvsSMA44E3gujZh/s6y9XyFL774LcCxwhaQN0r4xwDHALsAxkjbv4NprgIdSW+8FTi7ZtxmwT0R8A/gtcEZEjAbmAGdHxJvABml11/3SPdlP0pbAmxFRfMJzE7JFog4n+3KyGkmTJE2XNL2hoaGDJpuZmZlZtVibUzqmRcTLAJIeA0YC95XFfJisk3+/svlIHwAeLNl/TUncvRHxAkBELE7lhwFHSDo9vd8A2CJt35EWRELSDWQd3pvW0N7xwAWp/icl/Q3YLu27MyIaU12Pk6WKfGkNdf0duCVtzwAOLdl3bUSskFQPDIqIe1L5FcC1afsBYF9gf+C/gI+R5WGdWlLPTRGxEni8ONJfLiIagGLvPSrJAmJmZmZWDdTzqep7xNrsxL9dsr2inXOJrLN9bDt1dNTrFNno/FPvKZT2YvXFjbqyEECeayn1j1i1ilZ5fJ6e9L1ko/BbAjcDZ5C1/0/ttKlv/vWamZmZ9VE9MQ2oCSgmTH4I2FfSNvDuHPft2jjmIWB/SVuluCGp/DbgVKVhfEm7lRxzqKQhkgYARwL3l5273FTguFTPdmQj+k+1E9tlaWR/iaT9UtEJQHFUfipwPPBMGm1fDPwTq/+SYWZmZtanOTvNutMA3Crp7ohYAEwErpY0m2wqzQ7lB6S4ScANkmaxaprNfwDvB2ZLmpfeF00DrgdmA9dHxPQ0veZ+SXMlnV92mouAfpLmpPonRsTbrF2fJZvHP5ts3v0PAFKGG5GNyEPWeV8aEUvWcnvMzMzMrApo1ayP9YekicC4iPhqT7elF1n/PmgzMzPraT0+jP30koVd7uNsN3hYj19Hpao1q46ZmZmZWZ+1Xo7EW5uipbW14yigplCgtWlB7ooLdRvyxpxLc8dvvMvJPLt0Ue74bQYNrTi+taUxd3yhpp6nlyzMFbvd4GEAVHIvAV5vytee4XX1ACxfNDtX/IChowFobXojV3yhLktktHzJs/nqH7wNTS35sxrV1dTkvjeQ/taa888SK9QO5r4f75U7fvy3HmbhE7/LFTtsxxMAaH39ng4iU1uGfwSA5QsfzRU/YNjuWf2Na0psVVJ/fUeZbM3MeoUeH8F+dumiLndmtxk0tMevo1IeiTczMzMzqzLrVSde0oq0GutcSX+UNKgTdby7ims7+98naYGk88rKv1OyPUjSKZWeu1KprZ9Z2+cxMzMz6636oS6/qtF61YkHlqfVWHcmS8v4lbVwjkOBp8lWgS391L9Tsj0IWOudeLIFtNyJNzMzM+tj1rdOfKkHgREAkqZIGpe2h0man7ZHSZqWRu9nS9o2Hdtf0qWS5km6PeWaLzoW+AXwIrB3quc8YECq5yrgPGDr9P78FPNNSY+k83w/lY2U9KSkyyU9LekqSYdIul/SM5L2THHnSPqdpAdT+cmpLecB+6XzfH1t3UgzMzOz3krq+qsarZedeEn9gYOByR2Efgn4RUSMAcYBL6fybYH/iYhRwFLgU6neDYBDgD8CV5N16ImIM1n1K8BxwJnAc+n9NyUdlurckywf/FhJ+6dzbQP8lCw//g5kI+vjgdN57+j+aOAgsi8O35O0aTrP1HSen7VxHyZJmi5pekNDQ4f3zczMzMyqw/t6ugHdbICkx8hG4J8A7ugg/kHgLEmbATdExDNphswLEfFYiplBNm0F4HDg7ohYLul64P9JOi0iVnRwnsPSa2Z6X0vWqX8xnWsOQFqw6s6IiLTo1MiSOm6OiOXAckl3k30hWLqmk0ZEA9niWlBBdhozMzOzalGtc9q7an0biV+eRtW3JEt5VJwT/w6rrnWDYnBE/B44AlgO/FnSQWlX6UqtK1j1ZedY4JA0HWcGMJRsdLwjAn6YRszHRMQ2EfG/bZxrZcn7lbz3S1Z5+iTnBjUzMzPro9a3TjwAEdEKfA34d0nvA+YDY9PuCcU4SR8Cno+IXwI3k01ZaZOkgcB+wBYRMTIiRpJ9STg2hfxD0vvTdhNQV3L4bcBJkmpTXSMkbVThZX1S0gaShgIHAI+0cR4zMzOzPqVfN7yqUbW2u0MRMROYTdbJ/gnwZUkzgWElYf8KzE1TcHYGfruGKo8C7oqI0pHzm4FPSPog2bSV2ZKuiohFwP0p1eX5EXE78HvgwTRN5joq73zPBu4GHgL+IyJeTWUrJM3yg61mZmZmfcd6NSc+ImrL3n+i5G3pKPt30/7zyDK8lFpM1qEv1vGTkn1XlNW/GNgwvT0jvYr7PlMW+wuyrDblSs81sWR7fuk+YHZEnFhW5z/IN53HzMzMbL3Ur1rTy3SRIjy1ureTdA7QXPaFolL+oM3MzKy79XgP+pVlS7vcxxkxcFCPX0el3InvO6K1tSVXYKFQQ97Yd+OXvZI/fuAIGluac8fX19TyUuOS3PGb1w/mzaZlueM3qhvIvW+8lCt2/403B2DWojdyxe86dGMAWlsac8UXauoBuP2VF3LFHzZiKwBOv++2XPE/Gf/RrD0V/C3MW/xmrliAUUM2orWlKXd8oaYu973J4uvZ8Nv5Z44t+OHPuPPV+bliD950JAD/fPVvcsX/6diTgMo/27z/rRQGjgBg/n1n54ofOf77ueLMzLpZj3d+X1vW2OXO7CYD63v8Oiq13s6JNzMzMzNbX7kTn0hakVY+nSvpj5IGraPzTkwLNxXf/1rSTuvi3GZmZmbVziu2WnHF1Z3JHm79SkcHdJOJwLud+Ij4QkQ8vo7ObWZmZmZVyJ34tj1ItuorkraWdKukGZKmStohlW8s6caU3nGWpH1S+fGSpqVR/Usk9U/lzZJ+JmmepDslbShpAjAOuCrFD5A0RdK4dMyxkuakXwd+VGxcquvcdN6HJG28ju+PmZmZWa/QD3X5VY3ciS+TOt0HA5NTUQNwakSMBU4HLkrlvwTuiYhdgd2BeZJ2BI4B9k0rx64AjkvxNcD0iBgF3AOcHRHXAdOB49KvAMtL2rEp8COyFJJjgD0kHVlS10Pp3PcCJ7dzLZMkTZc0vaGhoWs3xszMzMx6jfUqT3wXDUiLPo0AngDuSCus7gNcq1UTpj6Y/j0IOBEgIlYAjZJOIFsZ9pEUPwAopvZYCVyTtq8EbuigPXsAUyJiAYCkq4D9gZuAvwO3pLgZwKFtVRARDWRfQqCC7DRmZmZm1aKv5ol3J36V5RExRlIBuI1sTvzlwNI0qp6HgCsi4ts5YruSDukfsSo36Ar8OZqZmZn1KZ5OUyYiWoGvAf8OtAIvSDoaQJldU+idwJdTeX9J9alsgqSNUvkQSVum+H7AhLT9GeC+tN0E1LXRlGnARyQNS1N8jiWbhmNmZmZmSb9ueFWjam33WhURM4HZZB3n44DPS5oFzAM+mcL+DThQ0hyyKS07pawy3wVulzQbuAPYJMW3AHtKmks2FecHqfxy4OLig60lbXgNOBO4G5gFzIiIm9fSJZuZmZlZFfE0jCQiasvef6Lk7cfaiH+DVR360vJrWDX3vXzfN9ooux64vqTogJJ9VwNXr6mt6eHY69o6n5mZmdn6rlqzy3SVVk2ttrVJUnP5F4V1zB+0mZmZdbce70EvbW7uch9nUG1tj19HpTwSv470cAcegJbW1lxxNYVC7tjOxje15M+UU1dTs9bjlzY354odVJt9jK0tjbniCzX1WXzOzECFQg0Ary3LV/8mA1P9zYvy1V87NItvacoXX1NHY0u+ewNQX1PLwuZ8dQMMq63LfW8guz95rxWy66343lT4WeW9P/U1teum/go+WzOz9UEfTU7jOfFmZmZmZtXGnfgukLQiPZBafI3shjrnSxrWRvk5kk7vav1mZmZm65N1tWKrpI9JekrSs5LObGP/ByVdk/Y/XNovlPTtVP6UpI92x3V7Ok3XLK8gh7yZmZmZVaGU7vt/yBbYfJlsYc/JKTNh0eeBJRGxjaRPAz8CjpG0E/BpYBSwKfBXSdulxUI7zSPx3ax0JF3SOElT0vY5kq6QNFXS3yT9i6QfS5oj6VZJ7y+p5lupfJqkbdo4x9bpmBmpvh3WzdWZmZmZ9S79pC6/ctgTeDYino+IvwN/YPUshZ8Erkjb1wEHS1Iq/0NEvB0RLwDPpvq6dt1draCPG1AylebGHPFbk+WIPwK4Erg7InYBlgP/XBLXmMovBH7eRj0NwKkRMRY4HbiorZNJmiRpuqTpDQ0N+a/KzMzMrA8p7TOl16SykBHASyXvX05lbcZExDtAIzA057EV83Sarql0Os1fIuIfaYGo/sCtqXwOMLIk7uqSf39WWoGkWmAf4Fqt+ub4wbZOFhENZB1+gKgkg4yZmZlZNVA3ZNEu6zNVBXfiu987rPqFY4OyfW8DRMRKSf+IVUn6V/LezyLa2SbVvdRz8c3MzMzWmVeAzUveb5bK2op5WdL7gHpgUc5jK+bpNN1vPjA2bX+qk3UcU/Lvg6U7ImIZ8IKkowGU2bWT5zEzMzOrbrGy66+OPQJsK2krSR8ge1B1clnMZOCzaXsCcFcasJ0MfDplr9kK2BaY1tXLdie++30f+IWk6UBnnzoeLGk28G/A19vYfxzweUmzgHms/mCFmZmZmXWTNMf9q8BtwBPA/0XEPEk/kHRECvtfYKikZ4FvAGemY+cB/wc8TjaV+itdzUwDnk7TJW2twhoRU4Ht2ig/p71jS/dFxMi0eUZ7x6cnmz/WqUabmZmZrVdyjaR3WUT8GfhzWdn3SrbfAo5u59hzgXO7sz1aNS3b1nP+oM3MzKy75VspaS1qbWnsch+nUFPf49dRKY/E9yGtrS254gqFGirJZFNTKNDY0pw7vr6mdq3XX2n8a8sac8VuMrAegNbGv+WKL9RvmcW3NOWLr6lL9b/UQWSx/uw5meZX/porvnbEIVn9zYvy1V87lNaWfPcGoFBTz5tNy3LHb1Q3MPffJWR/m8sXzc4dP2Do6IrvZWvTG/ni6zYGYPTPz8sVP/u0bHG/vPezUJP9rS1f8nSu+AGDsx8AW5c+l6/+QVvz0IJXc8UCfHjDTXPHmpmtU/nmtK93PCfezMzMzKzKuBPfAySdJWmepNlpoai9Kjz+AEm3rK32mZmZmVWPld3wqj6eTrOOSdobOBzYPSLeljQM+EAPN8vMzMzMqohH4te9TYCFEVFc+GlhRLwq6XuSHpE0V1KD0nKskraR9FdJsyQ9Kmnr0sok7SFpZnm5mZmZWZ+wbvLE9zruxK97twObS3pa0kWSPpLKL4yIPSJiZ2AA2Wg9wFXA/0TErsA+wGvFiiTtA1wMfDIiVnuaTdIkSdMlTW9oqKqVhM3MzMxsDTydZh2LiGZJY4H9gAOBaySdCTRJ+hZQAIYA8yRNAUZExI3p2LcA0iD9jkADcFhEtJliIiIaUgxAVJIFxMzMzKw6VOdIele5E98D0ipdU4ApkuYAXwRGA+Mi4iVJ5wAbdFDNaylmNyB/njgzMzMzq3qeTrOOSdpe0rYlRWOAp9L2Qkm1wASAiGgCXpZ0ZDr2g5IKKXYp8M/ADyUdsE4ab2ZmZtbb9NE58R6JX/dqgQskDQLeAZ4FJpF1yucCrwOPlMSfAFwi6QfAPyhZzjci3pB0OPAXSSdFxMPr6BrMzMzMrAe5E7+ORcQMsgdUy303vcrjnwEOKit+nmw6DhHxIjCqe1tpZmZmVi2qcyS9qxQRPd0GWzf8QZuZmVl3U083oHXZK13u4xQGjujx66iUR+L7kLzZaQqFGppa8meyqaupobGlOXd8fU0trS1NueMLNXW0tLbmjq8pFGhtaayg/vrc9dcUskcSFjbna/+w2rpOxS9tznc/B9XWrvX2PLlkYa5YgB0GD8vddsjaX+nf2utN+T/b4XX1ueOH19UDVPy3kLf9dTU1ALyybGmu+BEDBwHk/lsu1GTtr+RvZ97iN3PFAowaslHF/52bma0TVTqnvav8YKuZmZmZWZXpkU68pOGS/iDpOUkzJP05LUx0S4X1XC5pQtqeImnc2mlxrna8IOmxtLLqwV2oa76kYd3ZPjMzM7P118pueFWfdd6JV7ZS0Y3AlIjYOiLGAt8GNl7Xbelm34yIMcBpZKuompmZmZmtFT0xEn8g8I+IeLejGxGzgKlAraTrJD0p6arU4UfS9yQ9ImmupIZieXskHSbpQUmPSro25V4vjnJ/P5XPkbRDKt9Q0h2S5kn6taS/FUfDJR0vaVoaZb9EUv8Oru9BYEQ6tr+k81PbZ0v6Yio/QNK9kv4k6SlJF0ta7bOQdFP6pWKepEmp7CRJPy+JOVnSzzpok5mZmdn6qY/mie+JTvzOwIx29u1GNpK9E/AhYN9UfmFE7BEROwMDgMPbqzx1vr8LHBIRuwPTgW+UhCxM5b8CTk9lZwN3RcQo4Dpgi1TXjsAxwL5plH0FcFwH1/cx4Ka0/XmgMSL2APYATpa0Vdq3J3BqutatgX9po66T0i8V44CvSRoK/B/wCUnvTzGfA37TQZvMzMzMbD3S2x5snRYRL0fESuAxYGQqP1DSw5LmkOVMX1Ne9A+TdYzvl/QY8Flgy5L9N6R/Z5TUPx74A0BE3AosSeUHA2OBR1JdB5N9uWjL+ZKeBn4P/CiVHQacmI59GBgKFFdrnRYRz0fECuDq1IZyX5M0C3gI2BzYNiKagbuAw9MvCe+PiDltNSg9ZzBd0vSGhoZ2mm1mZmZWzfrmnPieSDE5D5jQzr63S7ZXAO+TtAFwETAuIl6SdA6wwRrqF3BHRBzbwTlW0PH1C7giIr7dQRxkc+Kvk3Qq2cj42HT8qRFx23sqlQ5g9bzt0UbMIcDeEdEqaQqrrvvXwHeAJ4HL2mtQRDQAxd575E0xaWZmZma9W0+MxN8FfLA4xxtA0mhgv3biix3XhWlue3tfAIoeAvaVtE2qu0bSdh0ccz/wryn+MGBwKr8TmCBpo7RviKQt267iXRcC/SR9FLgN+HJx6ouk7STVpLg9JW2V5sIfA9xXVk89sCR14Hcg+4UBgIh4mGxk/jNko/hmZmZmfZIiuvyqRuu8Ex/ZErFHAYekFJPzgB8Cr7cTvxS4FJhL1il+pIP6FwATgaslzSZ70HSHDpr1feAwSXOBo1NbmiLicbL59benuu4ANgFID8CultIyXd9/At8iGzF/HHg01X0Jq0b/HyHr8D8BvECWsafUrWS/RDwBnEf25aTU/wH3R8QSzMzMzKxP6ZEVWyPiVdLId5lLS2K+WrL9XbLOdHk9E0u2DyjZvovsQdLy+JEl29OB4jGNwEcj4h1JewN7RMTbKe4a4Jo26vpCW+1I768Hrk9vv5Ne70rJdZZFxGoP6Ja2Efh4+f4S4wFnpTEzM7M+rjrntHeVokp/QuhOkrYlG9nuB/wdOCUi1jji38XzHQCc3lYnPsexg4BpwKyIOLqCQ/1Bm5mZWXdbY9rvdWH5kqe73McZMHi7Hr+OSrkT33fkfrC1UKhh+YLpuSsesOE4Jr/4TO74I7bYlnNm3JM7/pyxH6G1pSl3fKGmjjebluWO36huIDf+7alcsUdtuT0AD/xs/1zx+3z9XgAWP73ajzltGrLdMQBcN//JXPETRmYzxWYsbHM22mrGDhsOwKKnfp8rfuj2n2H5orm5YgEGDN2Zpc3NueMH1dZSyQPXhUJNxZ/twuZ8fzvDausAeL2pMVf88Lp6ABbnrH9Iqr+ltTVXfE2h0Kn4vPdno7qBNLbk/6zqa2or/2xb8t1LgEJNfe5YM+tVerzzu3zxk13vxA/Zocevo1K9LcWkmZmZmZl1oEfmxJuZmZmZdY++OSe+UyPxkjaTdLOkZ1KGmV9I+kB3NUrSFElPSXosvSakbDA75ThutYwxkiZKurCDY4+XNFvSPEmz0vkGdbL9l0tqNxVmyfXNknS/pO0rqHtkynSkKnbZAAAgAElEQVRjZmZmZn1UxZ14ZalVbgBuiohtge2AWuDcbm7bcRExJr2ui4gvpJSP3U7Sx4CvAx+PiFHA7sADwMZr43zJcRGxK3AFcH4bbeq/Fs9tZmZmtn6IlV1/VaHOjMQfBLwVEZcBRMQKsg7wSZJOSSP0U9Io/dnw7ujxk5KukvSEpOskFSo5aekou6TDJD0o6VFJ16ZFoMrjPyfpaUnTgH07qP4ssmwxrxSvKSJ+ExFPpboOljRT0hxJv5H0wVT+PUmPSJorqSF9wanUvUBxYapmST+VNAvYW9I3Ut1zJZ1Wcsz7unIvzczMzKy6daYTPwqYUVoQEcuAF8nm2O8JfAoYDRxdMr1le+CiiNgRWAac0sF5riqZTjO0WChpGFnO+EMiYndgOvCN0gMlbUK2gNO+ZPnU1zgNJ13To23tkLQBcDlwTETskq7xy2n3hRGxR0TsDAwAKk4ZCXwCmJO2a4CH0wj9cuBzwF5kq7WeLGm3FJfrXkqaJGm6pOkNDQ2daJqZmZlZb7eyG17VZ21kp7kjIhZFxHKyaTfjU/lLEXF/2r6ypLw9pdNpFpWUf5isU36/pMeAzwJblh27FzAlIhZExN9pY7Gm9kjaJX1xeE7SMWQd5hci4ukUcgVQzC94oKSHJc0h+4ViVN7zkL6kkH3ROD2VrWDVIlHjgRsjoiUimsnu5X5pX657GRENETEuIsZNmjSpgqaZmZmZWW/Wmew0jwPveWhT0kBgC+AdVl9UKMr+LS+vlMi+KBzbyePbMo9sHvzdETEHGJMehB3QbiOyEfqLgHER8ZKkc4ANKjjncWnV2FJvpelJHemue2lmZmZW3ap0TntXdWYk/k6gIOlEePcBzJ+STTlpBQ6VNETSAOBIoDhivIWkvdP2Z4D7Otnmh4B9JRXnkddI2q4s5mHgI5KGSno/0NHKpj8EfiJps5KyYgf+KWBk8XzACcA9rOqwL0xz8tvNRtNJU4EjJRUk1QBHpTLovntpZmZmZlWo4k58ZEu8HkU23/0Z4GngLeA7KWQa2ZSQ2cD1JaPNTwFfkfQEMBj4VWcaHBELgInA1ZJmAw8CO5TFvAack/bdDzzRQZ1/Bn4J/EXS45IeIJvacltEvEU2N/3aNG1mJXBxRCwFLgXmArcBj3TmetbQpkfJvhhNI/tS8uuImJl2d8u9NDMzM6t+fXNOvLI+eTdVJk0km17y1bLykcAt6QFQ6xmecmNmZmbdrTOZ+brV8oWPdrmPM2DY7j1+HZXyiq19SGtrS664QqGG5UuezV3vgMHb8NqyxtzxmwysZ8rrL+aOP2D4Fixf/GT+9gzZgTfmXJo7fuNdTuahBa/miv3whpsC8NjvjskVP+aE7Jnq5QvKH39o24ANs2ROLzQuzhW/Vf0QAJ5csjBX/A6Dh6X2TMvZnj1pXfpcrliAwqCteWXZ0tzxIwYOoqkl398lQF1NzVqLr6upAWBxc1Ou+CG1dQAV19/S2porvqZQ6FT80ubmXPGDamvX+r1vbcl3LwEKNXX0O7GjmY+rrPzttbljzWw910fnxHdrJz4iLiebAlJePh9YbRRe0o3AVmXFZ0TEbd3ZrpLzncXq8+OvjYjuXqiqeL51en1mZmZm1jf06Eh8RBy1js93Lt2/suyazrdOr8/MzMys7+mbI/FrI098RSRtllZ5fSblZv+FpA90Y/2Hp9VWZ6WHVr/YQfwHJf015Ypvd86EpImSNu1km+an1V9nS7pd0vAKjj1A0i2dOa+ZmZmZrR96tBMvSWSLGN0UEdsC2wG1dNNoeUov2QB8Iq2CuhswpYPDdgNIi0ytaZGoiUCnOvHJgRExmmzF2e+U7lCmx79gmZmZmfV6sbLrryrU0x3Fg8gWOLoMIC109HXgJEmnpBH6KWmU/mzIMt1IelLSVZKekHSdpEI79deRTRlalOp/OyKeSvV8Iq22OjONvG8saSOyFVD3SCPxW0saK+keSTMk3SZpE0kTgHGkVVcl/bOkm4onlXRomg+fx73ANum6npL0W7K0lZtLOl/S3DRqX/qrwEBJf0rxF7vDb2ZmZta39HTnbxQwo7QgIpYBL5J1vvcEPgWMJstLPy6FbQ9cFBE7AsuAU9qqPCIWA5OBv0m6WtJxJR3e+4APR8RuwB+Ab0XEm8AXgKkRMSa14wJgQkSMBX4DnBsR15GNoB+X4v4M7CBpw1T351JsHocDc9L2tum6RpF9SRgD7AocApwvaZMUtydwKrATsDXwL21VLGmSpOmSpjc0NORsjpmZmVk16Zt54nt7isk7ImIRgKQbgPHATcBLEVFcCfZK4GvAT9qqICK+IGkXso7w6cChZFNhNgOuSR3jDwAvtHH49mRZde7IZv7QH3itjXOEpN8Bx0u6DNgbOLGDa7tb0gqyRbG+CwwC/hYRD6X944Gr068Tb0i6B9iD7EvLtIh4Pt2Xq1PsdW20q4FsOhFA5E0xaWZmZma9W0934h8HJpQWSBoIbAG8w+oLFEXZv+XlbYqIOcCc1NF+gawTfwHw3xExWdIBZCu8lhMwLyL27uhCgMuAP5KtXnttRLzTQfyBEfFucm9Jg4C8veyKrt/MzMxsvVWlc9q7qqen09wJFCSdCCCpP/BTslzzrcChkoZIGgAcCRRH37eQVOxYf4ZsasxqJNWmDnrRGOBvabseeCVtf7ad9j0FbFg8l6T3SxqV9jWRzbkHICJeBV4lG1W/bM2XnctU4BhJ/dM0nf2B4go9e0raKk0NOoZ2rt/MzMzM1k892omPiACOIpvv/gzwNNlIdjFbyzTgerIpJ9dHRHHZy6eAr0h6AhgM/KqdUwj4VnoA9DHg+2Sj8JCNvF8raQbQ5nKXEfF3sl8KfiRpFvAYsE/afTlwcXqwdUAqu4psqs8TuW9C+24ku+5ZwF1kc/ZfT/seAS4EniD7ZSHvQ7RmZmZm65WIFV1+VSNl/ejeR9JEYFxEfLWsfCRwS0SstgJsT5N0ITAzIv63p9vSht75QZuZmVk1U083oPX1e7rcxykM/0iPX0elenpO/Hojjei3AP/e021pT94HWwuFGpYvmNZxYDJgwz2589X5ueMP3nQkp993W+74n4z/KC2trbnjawoFXm9qzB0/vK6eyS8+kyv2iC22BeDhCw/JFb/XV/8KwOKn17TkwCpDtssyiVbanhkLX+8gMjN22PCK27N80dxcsQADhu7M0ubm3PGDamtz/11C9re5sLkpd/yw2joW54wfUpvNjsv7tzO8rh4g9/UOqq0FyP23XFPIMuc2teS7P3U1NQC82bQsV/xGdQNpbMn/WdXX1Fb+2bbk/++wUFOP9tstd3xMnUm/zx2bK3blZVfnrtfMqk+s7Jtz4nttJz4iLiebslJePp8sY8x7pLzsW5UVnxER+XuLXZBSUJa36WHgg2XFJ6QHbc3MzMzMOqXXduIrFRFH9XQbykXEXj3dBjMzM7P1WbXOae+qXA+2StosrZ76jKTnJP1C0ge6owGSbkwPhz4rqTFtPyZpn46P7rDuKSULRHU5rrtImihpQbrOxyWdXOHx67S9ZmZmZta7dNiJV7bK0Q3ATRGxLbAdUAuc2x0NiIij0qqn766Uml4PdEf9vdg16boPAP5L0salOyWtN7+SmJmZma0tsXJFl1/VKM9I/EHAWxFxGUBaQfTrwEmSTkkj9FPSKP3ZkGWQkfSkpKskPSHpOkmFvI1Kx98labakOyVtkcovlzShJK65ZPsMSXMkzZJ0Xkl1R0uaJulpSful2AGS/pDadiMwoKSeY1M9cyX9KJUdLem/0/a/SSqulvohSfen7fmSvi/p0XT8DnmuNSLeBJ4DtkzXd3GaS/9jSWMkPZTuw42SBpccekIayZ8rac+899bMzMzMql+eTvwoYEZpQUQsA14km1O/J/ApYDRZh7k4zWN74KKI2BFYBpxSQbsuAK6IiNFkudd/uaZgSR8HPgnsFRG7Aj8u2f2+iNgTOA04O5V9GWhNbTsbGJvq2RT4EdkXlzHAHpKOJFt4ab907H7AIkkj0va9JedaGBG7k+WtPz3PhUr6EPAh4NlUtBmwT0R8A/gt2cO5o4E5Je0HKKSR/FOA37RT9yRJ0yVNb2hoyNMcMzMzs6rSV/PEd8diT3dExKKIWE427WZ8Kn8pIoorrF5ZUp7H3sDv0/bvchx7CHBZRLQCRMTikn03pH9nACPT9v6pTUTEbLJFlQD2AKZExIKIeIfsC8T+aZGlWkl1wOapbfuTdeKndnCu9hyTFqC6GvhiSZuvjYgVkuqBQRFxTyq/Ip2z6OrU/nuBgZIGlZ8gIhoiYlxEjJs0aVIHzTEzMzOzapFn3vXjZKuWvkvSQGAL4B1WX0Qoyv4tL++Kd0hfPCT1A/I8XPt2+ncFXcvG8wDwObLVYqcCJ5F92SjNC1/Jua4pX8gqyZs0e23cXzMzM7Pq0kfzxOcZib8TKEg6EUBSf+CnZDncW4FDJQ2RNAA4EiiOvm8hae+0/Rngvgra9QDw6bR9HKtGu+eTpr4ARwDvT9t3AJ8rzruXNKSD+u9NbULSzmRTgQCmAR+RNCxd57FAcSR8KtkUmXuBmcCBwNsRkX81kwqkepcU5/EDJ5S0BeCY1P7xQOPaaoeZmZmZ9T4dduIjIoCjyOa7PwM8DbwFfCeFTAOuJ5uScn1ETE/lTwFfkfQEMJhsnnhep5J1ymeTdV7/LZVfStbJnkU2Ct6S2ngrMBmYnqaodDQf/Vdk02OeAH5AmvMfEa8BZwJ3A7OAGRFxczpmKtlUmnvTw70vUdkXk874LHB+ug9jUluL3pI0E7gY+PxaboeZmZlZr9TTc+LTYPYdKcnLHWWJSIoxYyQ9KGleSlhyTMm+yyW9UJJmfUyu82Z99E43eiIwrnxaiKSRwC0RsdrKqtZjPN3GzMzMupt6ugGNL9zc5T5O/Vaf7PR1SPoxsDgizpN0JjA4Is4oi9mObGz8mZRIZQawY0QslXQ5Wb/5ukrO61zkZmZmZla1ekGe90+SrfsDWSKSKcB7OvER8XTJ9quS3gQ2BJZ29qRdGomv+GRZTvatyorPiIjb1lkj1iFJn2PVVKCi+yPiKz3QnGhtzffMbKFQQ2vjS7krLtRvzvSFr+WOHzdsE2YsfD13/Nhhw5m3+M3c8aOGbMTrTfkfERheV89fXn4+V+zHN/sQABfMeyRX/Kmj9gBg5qI3csXvNjRb86uSz6oz8Q8teDVX/Ic33JSXGpfkigXYvH4wrS35732hpp7W5vz1F2oH8/jiBbnjdxqyYe6/heF19QA0tjR3EJmpr6kFYHFzU674IbV1nar/hcbFHURmtqrPHgXK+3ltXj+Yppa8z9FDXU0NS5vztR1gUG1t7muF7HqXv5l/jb8BG+3DG3MuzRW78S7ZotjLF0zvIDLVvaEXxDarQI+PxC997oYud2YHb/OpLwKlqfwaIiJXfm5JSyNiUNoWsKT4vp34Pck6+6MiYmUaid+bLEHKncCZEfF2e8cXrdOR+Ig4al2er6elBbIu6+l2mJmZma2vuiPPe+qwt9tpl/RXYHgbu84qqycktfulQtImZOnTPxsRxbQ63wZeJ8u62EA2iv+DtmtYpTvyxK8VklaUTPB/LK3i2uEwTekqrmXl71nttY3975P0X+mhhOI5z2ovPkc75ksatob9xeubK+laVbai7URJF3a2bWZmZmaWX0QcEhE7t/G6GXgjdc6LnfQ2pw+kFO1/As6KiIdK6n4tMm+TDf7umadNvbYTDyyPiDElr/kRsc9aPN9/ApsCu6SVUPdjVQrLtaF4fTsDfwe+VLpTkp9XMDMzM+tArFzZ5VcXTSbLKEj69+byAEkfAG4Eflv+AGvJFwCRpWufm+ekvbkTv5rSUXZJ35T0SErT8/02YiXpQklPpZ9ANlpDvQXgZODUiHgLICKaIuKckphvpFHzuZJOKym/SdKMlDKos8uiTgW2kXSApKmSJgOPS9pA0mWS5kiaKenAkmM2lzQl/XJwdifPa2ZmZmZdcx7ZuknPAIek90gaJ+nXKeZfgf2BiW2kkrxK0hxgDjCMbGC5Q715tHdAyvkO8ELpfHpJhwHbkv3cIGCypP0j4t6S448Ctgd2AjYmW3n2N+2caxvgxYho8wk1SWPJVmvdK53vYUn3RMRM4KSIWJwWu3pE0vURsSjvRaYR948Dt6ai3YGdI+IFSf9ONr1qF0k7ALenFEWka9+ZbMGtRyT9qSRHf7HuSaSHNC655BKOP/64vM0yMzMzqwrdMSe+a+ePRcDBbZRPB76Qtq8Ermzn+IM6c97e3Ilfnqa1tOWw9JqZ3teSdepLO/H7A1enhZlelXRX3hOXZJUZCuwDjAdujIiWtP8Gsuk2M4GvSSp+wdg8tSNPJ770S8pU4H/TuaZFxAupfDxwAUBEPCnpb0CxE39H8ctCas944D2d+LKHNHJnpzEzMzOz3q03d+LXRMAPI+KSbqrvWWALSXVpGs1lwGWS5gL9222EdADZzyZ7R0SrpCnABjnPudqXlGwqFHl72uVPPnsxJzMzM7M+oqrmxJe4DThJUi2ApBGSyue83wscI6l/emDgwPJKiiKilWwk/EJJG6Q6+5Ol+oFspPxISQVJNWRTdaYC9WS5QFvTdJcPd98lvnve41J7tgO2AJ5K+w5VtszvALKHIO7v5nObmZmZ9XqxckWXX9WoKkfiI+J2STsCD6bR62bgeN6b0udG4CCyufAvAg92UO1ZwH8AcyU1AcvJEvG/GhHzUyL+aSn21xExU9LjwJckPUHWuX6ojXq74iLgV+lhh3eAiRHxdrrmacD1wGbAleXz4c3MzMxs/bVOV2y1HuUP2szMzLpbj6/YuuDxy7vcx9lwp4k9fh2VqtbpNGZmZmZmfVZVTqfpCkk3AluVFZ8REbethXMNBe5sY9fBlaSh7C55s9MUCjUsf7PDxXHfNWCjfbjz1fm54w/edCRHXvvb3PE3HX0i0xe+ljt+3LBNmPziM7njj9hiWy59ambHgcDJ2+8GQPMrf80VXzviEABalz6XK74waGsArpv/ZK74CSN3AGDe4jYXh1vNqCHZoyMLn/hdrvhhO55AS2trrliAmkKh4vjW5iW54wu1g3NfK2TX+1Jjvvo3rx8MwCvLluaKHzFwEACvNzXmih9eVw9AU0u+/w7ramoAeHLJwlzxOwzOFoh+funiXPEfGjSEpc1tLnDdpkG1tSxsbjMLb5uG1dZV/Lcw+Jun5o5fcv4FXDDvkVyxp47aA4ABX8u3lMfyX2ZJve5/85Vc8ftuNCJXnJmtHd2wWFNV6nOd+NJ88+vgXIuA9tJkmpmZmZl1Sp+ZTiPprLSq6uy0StZea4jdIcXMlLT1GuK+08m2jJS0PJ3jcUkXS8r9WUg6R9LpnTm3mZmZ2fokYkWXX9WoT3TiJe0NHA7sHhGjyXK7v7SGQ44ErouI3SJiTfMgOtWJT55LeeJHk60qe2RZm/vcryRmZmZmlk9f6ShuAiyMiLcBImIhgKTvAZ8ABgAPAF8EPg6cBqyQdHBEHCjpeOBrZHnjHwZOAc5l1aqr84DngMUR8fNU97nAmxHxizU1LCLekfQAsI2kicC/kK1A2z+tBPsb4ENAKzApImanQ3eV9CAwDPhxRFza1ZtkZmZmVnWqNM97V/WJkXjgdmBzSU9LukjSR1L5hRGxR0TsTNaRPzwi/gxcDPwsdeB3BI4B9k0j5yuA4yLiTNKqqxFxHFln+0SANDXm08CVHTVMUgE4GJiTinYHJkTER4DvAzPTrwffAUqfBh1Nlgd/b+B7kjZto+5JkqZLmt7Q0JD7ZpmZmZlZ79YnRuIjolnSWGA/spVbr5F0JtAk6VtAARhCNqL+x7LDDwbGAo+kRZYG8N5FpYrnmC9pkaTdgI3JOt9rykCzdRrFD+DmiPhLGom/IyKK6SXGA59K9d8laaikgWnfzRGxHFgu6W5gT+CmsjY1AMXee+TNTmNmZmZWLap1TntX9YlOPEBkn/AUYEpaAfWLZKPZ4yLiJUnnABu0caiAKyLi2zlO82tgIjCcbGR+TYpz4svl7Wn/f/buPE6uqsz/+OfLnqpuOhtrBAICImuAiBJBAZdxFAQUlIA4ccswMig68nMdZXRUHFQQR8VWIYAICsriCshOEpaGxCTsCEGGNXu6uyJK8vz+uKekaLrTp3pJd6W/79erXrn31nPPPffW7crTp889p+vEBp7MyczMzGyEGBHdaSS9StIuNZsmAQ+m5cWSmoBjetj9euAYSVumssZK2iG993dJG9fEXgG8DXgNMBDjzt8KnJCOewhFv/6V6b0jJW2WxqI/BMgbMNnMzMxsPRJr1vT71YhGSkt8E/BdSaOBF4BHgOnAcmAB8Aw9JMERcZ+kLwDXpr7ufwdOBh6n6KoyT9I9EXFCRPwtdW1ZHgPzt53TgfMkzaN4sPVfat6bB9xI8WDrVyLiqQE4npmZmZk1AEW4F8ZASUn+PcCxEZE/Zei64Q/azMzMBpqGugJP3X12v3Ocbfc/dcjPo14jojvNuiBpd4oW/uuHYQJvZmZmZuuRkdKdZtBFxH0U47n/g6S9gIu6hD4fET3OFjuYckenKZXKrFr6QHa5o8buxoLLpmXH73nsDM5ecGd2/Kl7HsAlf743O37qK/fgyZXLs+MnbD6aOUuezYrdd9xWAFz75GNZ8W+dsCMAZ86bnRV/2t4HAtDemfdZNZfLADyxYllW/HYtYwBYtXhuVvyo8ZO4/qmFWbEAb9p2IpXO9uz4UrmZSkde3QFKTWNoW/x0dvzk8dvw9MoVWbHbbN4CwHPtK3uJLGzZXAwUtbQj73zHNjUD9X+29d47Dy1bnBW/65jxLO/oyIoFGN3UxIrO/PiWclPd5edeGyiuz6pFed8jo7Y4AIDFmZ/V+PRZnX73zVnxp+9fjFr8nit+lhX/i6OPz4ozszwxQseJdxI/iCJiPsVDtGZmZmZmA8ZJvJmZmZk1rJE6TvyQ94lPExjNTa9nJD1Zs75JP8vO+ltubtxAkTRD0mPpHO+RdGCd+6/T+pqZmZnZ8DLkLfFpVtNJAGnCpY6I+OaQVmrdOC0iLpf0VuCHFBNP/YOkDQdomEozMzOz9VajjvPeX0PeEt8dSW+SNEfSfEnnSdo0bV8oaXxanizpprTcJOn8FD9P0rtryvqqpD9Jul3SVmnbjpJmp/j/romVpDMlLUjvvTdt/56kd6blKySdl5Y/mMqfKOl+ST+SdK+kayWNyjzdW4Cda87vG5LuAY6VNDXVY4Gkb3S5RmelY10vaYseruN0SW2S2lpbWzOrY2ZmZmbD3XBM4jcDZgDvjYi9KP5a8G+97POfwIqI2Csi9gZuSNvLwO0RsQ9FsvyRtP07wA9S+bVDXbyL4q8C+wBvBs6UtA3FzKkHp5gJwO5p+eBULsAuwPciYg+KSaT+8YtEL44A5tesL4mI/VK53wAOS3V6jaSjas6rLR3rZuBL3RUcEa0RMTkiJk+fPj2zOmZmZmaNI2J1v1+NaDgm8RsCj0XEQ2n9AuANvezzZuB71ZWIqI5Z9zfgN2n5bmBiWn49cElarh0C8iDgkohYHRHPUiTIryEl8Wks+PuAZ1NyfyAwK+37WERUx+2rPVZPzpQ0l2Lm2A/VbP95+vc1wE0RsSgiXgAu5sXrsKYm7qep3mZmZmY2Qgx5n/g6vcCLv3hslhH/93hxStrVvPR8s2f3iognJY0G3kbRQj4WeA9F//12SeOA52t2WQ301p3mtIi4vJvt+QMl11SxD/uYmZmZNbyROk78cGyJXw1MlLRzWj+RokUcYCGwf1qu7a5yHXBydUXSmF6OMRM4Li2fULP9VuC9kjZM/czfAFRnE7kdOJUiib8V+FT6d7DcCbxR0nhJGwJTefE6bAAck5aPB24bxHqYmZmZ2TCjFxuqh151dBpgDvBNipbzu4B/i4jnJR0M/ARYCdwETI6IQyQ1UXSn2Z/il4D/iohfSeqIiKZU9jHA4RExTdKOwM+AJuAq4NSIaJIk4H+Af6Zo3f7viPh52v9DwFciYltJG1P0ez8xHWci8JuI2DPFfgpoiojTezjPGSn+8i7bF6ZzWpzWpwKfAwT8NiI+nbZ3AK3AW4HnKJ4fWNTL5R0+H7SZmZmtLzTUFVh425f6neNMPOi/hvw86jWskngbVP6gzczMbKANefI7UpP4RusTb/3QWalkxZVLJSqV/K75pVK57rpUOtvzyy83s2rpfdnxo8buzuL7L+o9MBn/6hNZ8dhVWbEtOx4JwNV/eTgr/p3b7wJApWNJVnypaRwAq5Y91EtkYdSYXYvylz+WV/7oHQE46Mf/mxV/24f/nUrHst4Dq+U3jan73qk3ftWSBdnxo8btSWXFE3llt2wHQKX92bz45q1SfG9/BKvGFyPBVjpX5MWXWwBYteyRrPhRY4oeiJXlf84rf/Qrs+tSrU/d90KdP+cdT/4xO75pwptZtfSBrNhRY3cDoPOpG7Piy9seClD398Ki+2ZkxW+x+zSA7O+p8a8+MSvObKQaqX3incQPIknfoxgJp9Z3IuL8oaiPmZmZma0fnMQPoog4ufcoMzMzM+urWD0yW+KH4+g0/SJptaS5Na/P9BK/sGam12slbb2W2MmSzumlvNGSPtpLzERJq1L97pN0rqTsz0LS6enhWTMzMzMbgdbHlvhVETGpzn0OjYjFkr5GMRrMx7oLiog2oK2XskYDHwW+30vcnyNikqSNKGaYPQr4VfVNSRulSZ7MzMzMrAcjtU/8etcS3x1JLZIelPSqtH6JpI90E3oLsLOkzSSdn1ro50g6NO13iKTfpOXTJZ0n6SZJj0qqJv5nAK9Mrexn9la3lKjPSsedJulqSTcA10saK+nK9FeC2yXtXbPrPpJmS3q4h3NB0nRJbZLaWltbcy+XmZmZmQ1z62NL/ChJc2vWvx4RP5f078AMSd8BxkTEj7rZ96QdIdUAACAASURBVHBgPsXEURERe0naDbhW0q7dxO8GHAo0Aw9K+gHwGWDP3L8GSCoBbwK+CGwF7AfsHRFLJX0XmBMRR0k6DLgQqJa7N/A6oAzMkfTbiHiqtuyIaKUYTx4gckenMTMzM2sUI7VP/PqYxHfbnSYirpN0LMWkUPt0eftGSauBecAXgPOB76b9HpD0ONBdEv/biHgeeF7ScxRJeK5Xpl82ArgqIn4vaRpwXUQsTTEHkWamjYgbJI2TtHl676qIWAWsknQjcABwZR3HNzMzM7MGtT4m8d1KD46+GqgAY4D/q3n70OosqSk2t9jna5ZXU9/1/HMPrfW5g2Z3ndjAkzmZmZnZiLPGfeLXe58A7geOB86XtPFaYm8FTgBI3Wi2Bx7MPE47RfeagVBbj0OAxRGxMr13ZOq7Pw44BLhrgI5pZmZmZsOcItavBtzULWZ+zaY/UHSPuRI4ICLaJX0baI+IL0laCEzu0hK/GfADYDLwAvDJiLgxJdKfiojDJZ0OdETEN9M+C4DDI2KhpJ9R9Fn/fUSc1k0dJwK/iYg9u2yflury72l9LHAesBPFXxCmR8S8dOydgF2A8cD/9NDHv9b69UGbmZnZcJDdfWGwPPjbU/qd47zqHd8d8vOo13qXxFuP/EGbmZnZQBvy5HekJvEjpk+8QaWS192+VCqztKM9u9yxTfX3Hqp05pdfKjdT6ViSH980jsqKx/PjW3agsvLpvNjNtwHg6ZUrsuK32bwFgNyRgcqlEgCVjmV59WkaU8R35tWnVC7qU0/9c+8bKO6dwY5f3tGRHT+6qanua5N7b5bKzX2Lr+PnsC/x9dxrg/1ZrejM/6xayk1U2p/NL795q/p/TjLLLzUXYxRUVj6ZF7/5hCJ+xRN58S3b9S2+znvBbKTw6DQ24CTtBVzUZfPzEfHaoaiPmZmZma0fnMQPooiYz4vjupuZmZnZAIs1I3OC+4YbnSaNlT43vZ6R9GTN+iZ1lnV6zf4LJL2zl/jfSRrdS8w0Sdv2EnNTmkH2T5JmVmeSzazzxPQQrZmZmZmNUA3XEh8RS0it211HiOmjsyLim5JeDdwqacuIWNPDsd+eUd40YAHwVC9xJ0REm6TpwJnAS36BkLRhRIzMTl5mZmZmmdaM0D7xDdcS340NJN0NIGkfSSFp+7T+Z0ml1Hp9g6R5kq6vvl8rIu6nGE5yvKSpkuan1vlvVGMkLZQ0PpV3v6QfSbpX0rWSRkk6hmJYyotT6/6ojPrfAuycyu+Q9C1JfwIOlPTJVIcFkk6t2WcjSRenOlwuqdRdwZKmS2qT1Nba2pp3Nc3MzMxs2Fsfkvg1wGaSNgcOBtqAgyXtADwXERXgu8AFEbE3cDFwTtdCJL02lbUx8A3gMIoW/9dIOqqb4+4CfC8i9gCWA++OiMvT8U+IiEkRsSqj/kfw4rj2ZeCOiNgHWAV8AHgt8DrgI5L2TXGvAr4fEa8GVgIf7a7giGiNiMkRMXn69OkZVTEzMzNrLLFmdb9fjWh9SOIBZgGvB94AfC39ezDFjKcABwI/S8sXAQfV7PsJSXOBbwLvpWhJvykiFkXECxRJ/xu6OeZjETE3Ld8NTKyzzhen474e+FTathr4ZVo+CLgiIjojogP4VTongCciYmZa/mmX8zEzMzOz9VzD9YnvwS0UCe4OwFXApykmN/ptxr5n1fapl3Rk5jGfr1leDeR0nal1QkS0ddn218x+8F0nNfBETmZmZjYijdRx4teXlvhbgfcBD6eHUpcCbwduS+/PAo5LyyfwYgt9d+4E3pj6vm8ITAVurqMu7UD9sx+93K3AUalPfxk4mhfrvb2kA9Py8bx4nmZmZmY2AqwXLfERsVCSKFrkoUhqXxER1en8TgHOl3QasIiir3lPZT0t6TPAjRRTCf82Iq6qozozgHMlrQIOzOwX31097pE0g+KXCoAfR8QcSROBB4GTJZ0H3Af8IKfMembx68ssrPWozl6ZHd80rr74lh3qi08zseaqzsSaqzoTa67qDJPZ8eX66lNP/eud/XGw40c3NdVXfp3Xpu57s974Qb4+9dxrg12XlnKdn1WaKTU7vt6fk3rLTzOxZsenmVUHLd4zsZp1q1H7tPeXItwTY4TwB21mZmYDTUNdgXk/O77fOc7ex/9syM+jXutFS7zl6axUsuLKpRKVSmd2uaVSmafuPjs7ftv9T2VxR3t2/PimZm565i/Z8YdsvX32uUJxvu2deefbXC5awg5sfdkAR92aPf1jAHzsppzHM+CcQ94BwKrFc3uJLIwaX0wIXGl/Niu+2hL5pyV58fuM24rfPvFIVizAO7bbmUrHkuz4UtM4Kp0r8uPLLTywbHF2/G5jxrM0816r/vWpnp8ToO57p974q//ycFb8O7ffBYCnV+Zdz202b6n756Te+NxrD8X1v/I/Xpkdf9S3/sylJ79stOBuHfe94vvjd5/Nm1fv7V9/EIA9v/21rPgFn/wcAJWVT2fFV//yN+pjeaOGrTqntU/lVzrzrn+9f00yG25G6jjxTuIHkaQrgB27bP50RFwzFPUxMzMzs/WDk/gBImk1L473DnBpRBzdh3JmAG8EVlCMW39yRMyWNA24NiJ6mwnWzMzMbMQYqX3incQPnFURMWmAyjotIi6X9Fbgh8DewDRgAeAk3szMzGyEW1+GmByWJLVIelDSq9L6JZI+kpY7JJ0l6V5J10vaopsibgF2lnQMxSRUF0uaK2mUpDMk3SdpnqRvdrOvmZmZ2XovVq/u96sROYkfOKNSgl19vTciVgD/DsyQdBwwJiJ+lOLLQFtE7EExDv2XuinzCGB+RFwOtFFMEDUJKFGMG79HROwN/Hd3FZI0XVKbpLbW1tYBPVkzMzMzA0ljJV0n6eH0b7fj30paXZMnXl2zfUdJd0h6RNLPJW2Sc1x3pxk43XaniYjrJB0LfA/Yp+atNcDP0/JPgV/VvHempC9QjGn/oW6OtQL4K/ATSb8BftNdhSKiFahm71HPyBJmZmZmjSBWvzDUVfgMcH1EnJHmGvoM8Olu4nrqev0N4KyIuFTSuRS5X69zALklfpBJ2gB4NVAB1jYzSe0Yp6dFxKSIeEtELHhZYMQLwAHA5cDhwB8GsMpmZmZmlu9I4IK0fAFwVO6OabLSwyhyurr2dxI/+D4B3A8cTzFr7MZp+wbAMWn5eIpZZtemHWgGkNQEtETE71L5+6xtRzMzM7P11Zo1q/v9qu2CnF55EzkUtoqI6kQOzwA9TQ+9WSr7dknVRH0csDw10AL8H5A1XbS70wycUZJqZ+j5A3A+8GHggIhol3QL8AWK/u+dwAGp28xzwHt7KX8GcK6kVcA/A1dJ2oxiprRPDuiZmJmZmY0gXbogv4ykPwJbd/PW57uUE5J6mkF2h4h4UtJOwA2S5lN0ke4TJ/EDJCI27OGtV9fEvCTZ7rqetk3rofxfAr+s2XRA/bU0MzMzW7+si9FlIuLNPb0n6VlJ20TE05K2oWic7a6MJ9O/j0q6CdiXIrcbLWmj1Br/CuDJnDopoqdfFmwwSeqIiKZ1eEh/0GZmZjbQNNQVuPP7/9TvHOeAj17T5/OQdCawpObB1rER8f+6xIwBKhHxvKTxwGzgyIi4T9JlwC9rHmydFxHf7+24bokfIus4gQcgd3SacqlEpdKZXW6pVKa9Mz++uVyuu/xKZ3t+fLmZSseS/PimcVTaF+XFNhfD+V//1MKs+DdtOxEg+3xLpTIAly98ICv+mIm7AfCWi/KGEL3uxKKL38znsn7J5/VbTuDqvzycFQvwzu13qfuzWtqRHz+2qZnHVyzLjt+hZQyPLl+aFbvT6LEAnDlvdlb8aXsfCMCqxfdkxY8avx8AT919dlb8tvufClD3vfnVub09XlP4/KSD6Hjyj1mxAE0T3kznUzdmx5e3PbTue2F5R0d2/Oimpux7Z2xTM0B2+aObiq/n3O+RUtO4Ij7zfEvl5r6VX+f3SF3f+e3PZsUClJp76u5rNjSGwYytZwC/kPQh4HHgPQCSJgMnRcSHKXpm/FDSGornIs+IiPvS/p8GLpX038Ac4Cc5B3USb2ZmZmbWRxGxBHhTN9vbKJ6NJCJmAXv1sP+j9KGbtJP4YUDSamB+zaZL059kbgK2oRgTvgP4YEQ8mPa5Etg6Il63rutrZmZmNlw06oyr/eUkfnjoafB/KGZprQ51dCbwTkmjgf2BDkk7pd/gzMzMzGyE8DjxjeMWYOe0/C7g18ClwHFDViMzMzOzITYQ48Q3Iifxw8MoSXNrXt2NGX8EL3a5mQpckl5Teyq0duKC1ta8Bx/NzMzMbPhzd5rhYW3daS5OEzwtBE6RtBWwC3BbmlDg75L2jIgFXXfsMnFB5I5UYGZmZtYo3CfehqsT0tPNAEg6BRgDPCYJYHOK1vjPd7+7mZmZma1vnMQ3nqnA2yJiNoCkHYE/4iTezMzMRqBY88JQV2FIuE/88NC1T/wZ3QVJmgjsANxe3RYRjwErJL12ndTUzMzMzIacW+KHgYjYsIfth3RZXwhM6CZuv0GpmJmZmdkwN1L7xCsihroOtm74gzYzM7OBpqGuwK1fn9zvHOfgz7YN+XnUyy3xI0il0pkVVyqVWd7RkV3u6KYm5i99Ljt+r7FbUulckR1fKrdk1x2K+q9a9lB2/Kgxu7Jq6QN5sWN3A+DezPPdY+yWADyxYllW/HYtYwB4YNnirPjdxowHYM6SZ7Pi9x23FQB3L34mK37/8Vtnx1bj6/2s6hk1qVwqZcdWbfCR92XFrfnRTwFY+ZffZcVvvv3bAVja0Z4VP7apGYBHly/Nit9p9FgAKp155ZfKRfnLHrksK37MzsdSWfl0VixAafNtqHQsyY9vGpd9baC4PvXeO+2defHN5TJQ33cgwJMrl2fFT9h8NED2vVy9jysded8Lpabie6He+tdzfer9Oaz3O99sMDXqOO/95T7xZmZmZmYNxkn8OiLpd5JG9/DeQknzax5sPSdtnyHpsbTtHkkH1uxztqQnJfkzNDMzsxErVq/u96sRuTvNOhIRb++6TcVA79U+WIdGRHd9KE6LiMslvRX4IbB3StyPBp4A3gjcOEjVNjMzM7NhyK24g0DSlZLulnSvpOlp20JJ4yVNlPSgpAuBBcB2mcXeAuyclg8B7gV+QDFuvJmZmdmItGZN9PvViJzED44PRsT+wGTgY5LGdXl/F+D7EbFHRDyett1Y053mE92UeQQwPy1PBS4BrgDeIWnj7iohabqkNkltra2t/T4pMzMzMxse3J1mcHxM0tFpeTuKpL3W4xFxe5dtPXWnOVPSF4BFwIckbQK8HfhkRLRLugP4J+A3XXeMiFagmr1HPSM/mJmZmTWCNWvWDHUVhoST+AEm6RDgzcCBEVGRdBOwWZewerLp0yLi8pryjwBGA/OLLvWUgFV0k8SbmZmZ2frJSfzAawGWpQR+N+B1A1z+VODDEXEJgKQy8JikUkTkD/RrZmZmth5o1D7t/eU+8QPvD8BGku4HzgC6dpvpSW2f+Au7C5BUAt4G/La6LSI6gdso+sybmZmZ2QjglvgBFhHPA//czVsT07+LgT277DOxa3DaPq3LegUY203cu+qvqZmZmVnjG6kt8YoYmSc+AvmDNjMzs4Gm3kMG1x8+9+p+5zhv+9r9Q34e9XJL/AjSWcnrMl8ulah0LMsut9Q0hlXLHsqOHzVmV1Z0dmTHt5SbqGdknVKpzOKO9uz48U3NPNO+Iit26+YWAOYvfS4rfq+xWwJQWfFEVnyppZg2YOZzT2bFv37LCQBMverSrPhLjjwOgPuWLsqK333sFjywrLtBk7q325jxVDrzr32p3Fz3Z1uveT87Pitu7+N/BsBhM87Nir9h2kkAVJY/lhVfGr0jAAsum5YVv+exM4D6fm4BtvnP07Lin/7KmVRW5t1nAKXNJ2TXpVqfer9Hltbxczu2qZnlHXnfI6ObmgCyyx/b1AzU/3Obe++XykX59X62gxlf6ViSFQtQahpH8yc/mh3f/u3v88cv7pEd/+Yv35sdawawJkbm6DTuE29mZmZm1mCcxA8xSadLerLmoda5kkZLOkTSirR+v6Qv1ewzSVJIettQ1t3MzMxsqHnGVhtKZ0XEpJrX8rT91oiYRDHz6/sk7Ze2T6UYkWbqUFTWzMzMzIaW+8T3g6QvA0sj4uy0/lXgOeCdwBhgY+ALEXFVev/9wKcoHjKdFxEn5hwnIjol3Q3sLGkOcCzwFuBWSZtFxF8H+NTMzMzMGsJInbHVLfH9cx7wfgBJGwDHAZcCR0fEfsChwLdU2AP4AnBYROwDfLymnE/UdKW5setBJI2jmDTqXmAK8FhE/Bm4CXhHT5WTNF1Sm6S21tbWAThdMzMzMxsO3BLfDxGxUNISSfsCWwFzgKXAWZLeAKwBJqT3DgMui4jFad+lNUWdFRHf7OYQB6eW9zXAGRFxr6T/pfhFgfTv+4Ff9lC/VqCavUc9I0uYmZmZNYJG7dPeX07i++/HwDRga4qW+ROALYD9I+LvkhYCm/Wx7Fsj4vDqiqQNgXcDR0r6PMXYrOMkNUdE/thsZmZmZtbQ3J2m/64A3ga8BrgGaAGeSwn8ocAOKe4G4NjUNQZJL5t5NcObKPrSbxcREyNiB4pW+KP7exJmZmZmjcij01ifRMTfgBuBX0TEauBiYLKk+RRdXR5IcfcCXwVulvQn4Ns1xdT2iZ8raWIPh5tK8UtDrV/iUWrMzMzMRhR3p+mn9EDr6yhGjCH1eT+wu9iIuAC4oMu204HTuwlfSPHgam3sB7op82rg6nrrbWZmZrY+GKmj0yiiMf+EMBxI2h34DXBFRPzHUNenF/6gzczMbKBpqCtw+ccn9jvHOeY7C4f8POrllvh+iIj7gJ2Guh65KpXOrLhSqczSjvznZMc2NVPPyDflUolKZ375pXIfys88VyjOt55rA2TXp1wq9Sm+3voMZv3rvfaDHV/vZzvYn1Uj3wvD7bOq93uh7muTWX6p3FzE113+iszyW/pUn8G8dwbr56pan8G+d2xka9Q+7f3lPvFmZmZmZg3GLfFmZmZm1rDcEt8gJHV0WZ+WJkBal3U4UtKVNeuflfRIzfoRkq5OywslzZc0T9K1krZel3U1MzMzs/VPwyXxw8QsihFpqg4EVkraMq1PSTFVh0bE3kAb8Ln+HFiS/3piZmZmlqyJNf1+NaL1KomXNFHSDanV+3pJ26ftMyT9QNLtkh6VdIik8yTdL2lGzf5vlTRb0j2SLpPU1N1xImIRRdK+c9o0gWK89ilpfQows5tdbwF27mZ79fgdks6SdG+q/xZp+02SzpbUBnxc0v6SbpZ0t6RrJG1T35UyMzMzs0bWiEn8qNqJkYAv17z3XeCC1Op9MXBOzXtjKFrMP0ExrvpZwB7AXpImSRoPfAF4c0TsR9Fq/sm11GMmMEXSq4CHgdvT+kbAPsBd3exzODB/LWWWgbaI2AO4GfhSzXubRMTkdE7fBY6JiP2B8ygmkXoZSdMltUlqa21tXcthzczMzBrTSJ2xtRG7ZqyKiEnVFUnTgMlp9UDgXWn5IuB/avb7dUREmkn12YiYn/a/F5gIvALYHZgpCWATYPZa6jGLosV9wxR3J/BFYF/ggYj4a03sjZJWA/MoflHoyRrg52n5p8Cvat6rbn8VsCdwXarnhsDT3RUWEa1ANXuPeobsMjMzM7PhqxGT+L56Pv27pma5ur4RsBq4LiKmZpY3EziFIon+UUS0S9oMOISX9oeHok/84j7UufZXw2oGLuDeiOh2VlgzMzOzkWSkztjaiN1p1mYWcFxaPgG4tY59bwdeX+3nLqksade1xN8PbAscBMxJ2+YCJ9F9f/gcGwDHpOXjgdu6iXkQ2ELSgameG0vao4/HMzMzM7MGtL4l8acAH5A0DzgR+Hjujulh1WnAJWn/2cBua4kP4A5gSUT8PW2eTTGDa9eW+FydwAGSFgCH8dL+/tXj/o0i0f+GpD9R/OIwpWucmZmZ2UgwUvvEq8hFbTiQ1BER3Y6IMwD8QZuZmdlA01BXYMaHt+53jjPtx88M+XnUayT1iR/xOiuVrLhyqUSlY0l2uaWmcdTz0GypVKa9Mz++uVyuu/zcc4V0vpnll0ploL5rCQx6+YNZn3qv/WDHD/ZnO9ziB/NeGG6fVb3fC/Vem9zym8t9u/aDXf5g3juN/nN+7L6bZMVeNudv2eVa42jUlvT+chLfC0lXADt22fzpiLimH2XeAWzaZfOJg9gKb2ZmZmbrESfxvYiIowehzNcOdJlmZmZmI5FHp2lQko6SFJJ6fAh1gI8nSYsljUnr26TjH1QTs0jSOEmnS3oyTUy1QNI710UdzczMzGz91vBJPDCVYijG3PHd+yWNSnM7xcRSUIwMMyf9S5rBdUlEVDuVn5UmpzoWOE9Sn695mg3WzMzMzEa4hk7iJTVRjNP+IeA4SRtKeiy1lo+WtFrSG1LsLZJ2kXSApNmS5kialZLu6vu1M8HeJmmfHg5dna2V9O9ZvDSpf9k48RFxP/ACML6Hc5kh6VxJbZIeknR42j5N0tWSbgCuT+PXnyfpznQOR9Z10czMzMzWIyN1iMmGTuKBI4E/RMRDwBJgEsVkSLtTJPf3AAdL2hTYLiIeBh4ADo6IfYEvAl9LZf2EYpx40iRPm0XEn3o47kxeTOIPAK4AtkvrU+hmnHhJr6WYHXbRWs5nYirvHcC5aQZYgP2AYyLijcDngRsi4gDgUOBMSeXuCpM0Pf1S0Nba2rqWw5qZmZlZI2n07hlTge+k5UvT+q3AGyhGlPk68BHgZuCuFNcCXCBpF4qx0zdO2y8D/lPSacAHgRlrOe5dwL4ped44IjokPZpme50CfKsm9hOS3ge0A++NtQ/M/4uIWAM8LOlRXpxs6rqIWJqW3wq8U9Kn0vpmwPYUM8i+RES0AtXsPeoZssvMzMysETRqS3p/NWwSL2ksxayme0kKYEOKpPwE4N+AbSla2k8DDqFI7gG+AtwYEUdLmgjcBBARFUnXUbTuvwfYv6djp9iHKZL9e9Lm24G3A1tS/DWg6qyI+GbmaXW9C6vrtQPsCnh3RDyImZmZmY1Ijdyd5hjgoojYISImRsR2wGMU5zQFWBMRfwXmAv8K3JL2awGeTMvTupT5Y+Ac4K6IWNbL8WcBpwKz0/ps4OPA7b20tq/NsZI2kPRKYCde+stA1TXAKZIEIGnfPh7LzMzMrOGtWbOm369G1MhJ/FSKvui1fgkcBzxB0TIORQt8MzA/rf8P8HVJc+jyl4iIuBtYCZyfcfyZFIl2NYm/B3gF3fSHr8NfgDuB3wMnpV9CuvoKRRegeZLuTetmZmZmNoKo743G6x9J21J0r9kt9U1fl8eeAfwmIi4fpEP4gzYzM7OBpqGuwP9OHd3vHOffL1k+5OdRr4btEz/QJL0f+CrwyXWdwK8rlUpn70FAqVRmaUd7drljm5qp56HZcqlEpTO//FK5D+VnnisU51vPtQGy61MulfoUX299BrP+w+Xa9zV+sD+rRr4XButaQh/vhTq/F+q+Npnll8rNRXzd5a/ILL+lT/UZzHun3s92sOPrvXd0cF7P0rh1DgDH7rtJVvxlc/6WXQ+zdc1JfBIRFwIX1m6T9AGKfu61ZkbEyX09jqTPU0z8VOuyiJjW1zLNzMzMRqqh7tOeBlv5OcVQ4QuB93R9tlLSoRTzClXtBhwXEVem3hhvBKotAdMiYm5vx3USvxYRcT55/ePrKfOrFC3+ZmZmZtb4PgNcHxFnSPpMWv90bUBE3Egxn1E16X8EuLYm5LR6u1QP6wdbJXV0WZ8m6X/XcR2OlHRlzfpnJT1Ss36EpKvT8kJJ8yXNk3StpK0HuW6HSJrSe6SZmZnZ+mkYzNh6JHBBWr4AOKqX+GOA30dEvybwGdZJ/DAxC3hdzfqBwEpJW6b1rjO0HhoRewNtwOcGuW6H8OLMsWZmZmbWB7Wz3KfX9Dp23yoink7LzwBb9RJ/HHBJl21fTY3AZ0naNOegDZvES5oo6YZ0wtdL2j5tnyHpB5JuT7OoHiLpPEn3pz5H1f3fKmm2pHskXSapqbvjRMQiiqR957RpAsVQltXkeQrFcJNd3QLs3M326vE70gd1b6r/Fmn7pFT3eZKukDQmbf+YpPvS9kvTRFUnUcwIO1fSwdkXz8zMzGw9MRAt8RHRGhGTa16ttceQ9EdJC7p5HVkbl+YK6rFpX9I2wF4U8/5UfZaij/xrgLF06YrTk+GexI9KCepcSXOBL9e8913ggtTqfTHFJE1VYyhazD8BXE3xIMEeFLO7TpI0HvgC8OaI2I+i1fyTa6nHTGCKpFcBD1OMQT9F0kbAPsBd3exzOC+OTd+dMtAWEXsANwNfStsvBD6dzmt+zfbPAPum7SdFxELgXIoZYSdFxK10UftbZWtra9e3zczMzCxDRLw5Ivbs5nUV8GxKzqtJ+nNrKeo9wBUR8feasp+OwvMUz2IekFOn4f5g66qImFRdkTQNmJxWDwTelZYvopjEqerXERGS5gPPRsT8tP+9FE8OvwLYHZiZJj7dhBcnberOLIoW9w1T3J3AF4F9gQe6TMp0o6TVwDyKXxR6sobiSWaAnwK/ktQCjI6Im9P2C4DL0vI84OLUP/9KMqTfIqvZe9QzZJeZmZlZIxiAPu39dTXwL8AZ6d+r1hI7laLl/R8kbRMRT6tISo8CFuQcdLgn8X31fPp3Tc1ydX0jYDVwXURMzSxvJnAKRRL/o4hol7QZRZ/0rjO0HhoRi/tQ597uwHcAbwCOAD4vaa8+HMPMzMzMBtYZwC8kfQh4nKK1HUmTKXpPfDitTwS2o+iBUevi1K1awFyK7tK9Gu7dadZmFsWDAQAnAC/rTrIWtwOvr/Zzl1SWtOta4u8HtgUOAuakbdWL3F1/+BwbUDydDHA8cFtErACW1fRvPxG4WdIGwHZpeKJPAy1AeT4IAAAAIABJREFUE9AONPfx+GZmZmYNb030/9UfEbEkIt4UEbukbjdL0/a2agKf1hdGxISuk4pGxGERsVfqnvO+iOjoeozuNHISfwrwAUnzKJLdrpMy9Sg9rDoNuCTtP5vigYKe4gO4A1hS04dpNrATL2+Jz9UJHCBpAXAYL/b3/xfgzFSvSWn7hsBPU/egOcA5EbEc+DVwtB9sNTMzMxtZVOSntq5J6oiIbkfEGST+oM3MzGygaagrcMaR5X7nOJ+5qnPIz6NejdwSb2ZmZmY2Iq2vD7b2iaQrgB27bP50RFzTXXxmmXcAXQftP3Edt8ID0FnJmxisXCpR6ViSXW6paRz1jHxTKpVp78yPby6X6y4/91whnW9m+aVSGajvWgKDXv5g1mewr/1gx9d77Ydb/GDeC4N1LaFv90K93wv1Xpvc8pvLfbv2g13+YN479X62gx1f771z7L6bZMVeNudvAOjgfbPi49biMbh6y7d1a/XQj04zJJzE14iIowehzNcOdJlmZmZmNrI1VHcaSUdJCkk9PoQ6wMeTpMU1s6Zuk45/UE3MIknjJJ0u6cn0kOkCSe8c5LqNlvTRwTyGmZmZ2XA31KPTDJWGSuIpBsi/Lf076NKoNLdTTCwFxYRPc9K/pBlcl0REte/JWWlyqmOB89LQkINlNOAk3szMzGwEapgkXlITxTjtHwKOk7ShpMdSa/loSaslvSHF3iJpF0kHSJotaY6kWSnprr5fOxPsbZL26eHQ1dlaSf+exUuT+peNEx8R9wMvAON7OJcZks6V1CbpIUmHp+2bSTpf0vxU50PT9j0k3Zla+edJ2oViYoFXpm1n5l9JMzMzs/WHW+KHvyOBP0TEQ8ASijHUHwR2p0ju7wEOlrQpxcRIDwMPAAdHxL7AF4GvpbJ+QjFOPGmSp80i4k89HHcmLybxBwBXUMy2Rdr+snHiJb2WYnbYRWs5n4mpvHcA56YZYE+m+APAXhR/bbggbT8J+E5q5Z8M/B/wGeDPETEpIk7r7gCSpqdfFNpaW1vXUhUzMzMzaySN9GDrVOA7afnStH4r8AaKEWW+DnyEYirbu1JcC0UivAvFOOkbp+2XAf8p6TTgg8CMtRz3LmBfSWVg44jokPRomu11CvCtmthPSHofxUyq7421D8L/izRj18OSHqWYbOog4LsAEfGApMeBXSkmlvq8pFcAv4qIh6XehzONiFagmr1HPU/7m5mZmTWCRm1J76+GSOIljaWY1XQvSUExg2kAJwD/BmxL0dJ+GnAIRXIP8BXgxog4WtJE4CaAiKhIuo6idf89wP49HTvFPkyR7N+TNt8OvB3YkuKvAVVnRcQ3M0+r6y3X4y0YET9LQ1W+A/idpH8FHs08jpmZmZmtZxqlO80xwEURsUNETIyI7YDHKOo/BVgTEX8F5gL/CtyS9msBnkzL07qU+WPgHOCuiFjWy/FnAadStIiT/v04cHsvre1rc6ykDSS9EtiJ4peBWyl+Mal289keeFDSTsCjEXEOcBWwN0Vrf3Mfj21mZma2Xlizpv+vRtQoSfxUir7otX4JHAc8QdEyDkUS3AzMT+v/A3xd0hy6/NUhIu4GVgLnZxx/JkWiXU3i7wFeQTf94evwF+BO4PfASemXkO8DG0iaD/wcmBYRz1P8tWCBpLnAnsCFaUScmWk4Sz/YamZmZjaCqO8NyY1N0rYU3Wt2S33T1+WxZwC/iYjL1+FhR+YHbWZmZoOp94f0Btnn/mlUv3Ocr12zasjPo16N0hI/oCS9H7gD+Py6TuDNzMzMzPqrIR5sHWgRcSFwYe02SR+g6Odea2ZEnNzX40j6PMXET7Uui4hpfS2zPyqVzqy4UqlMpWNJ74HV+KZxdcev6OzIjm8pN/Ho8qXZ8TuNHkulo7fHHGrrMyY7vtQ0BoAnVuTFb9fSt/jKisfz6tOyQxG/8um8+M23AeCxFXnXc8eWsSztaM+KBRjb1EylMz++VG7mufaV2fFbNm/OqiXzsuNHjdubyvLH8uoyekeA7Hu51DSuiK/z2lfa1zbybE188xYAzFnybFb8vuO2Ksqv43wrnSuyYgFK5ZbsukNR/9zvHCi+d1YtujM7ftQWB7Bq8T29BwKjxu8HwKpFbZllTwbI/t7ZafRYoP57od7yH8/8Htmh+j2S+bNYKjdz56K8ugMcsMU2df+c1/udXO/3Tu7oa+VSCajv/0Ng0Mu3/vHoNCNcRJxPXv/4esr8KvDVgSzTzMzMzMxJfDcknQU8HhFnp/VrgCci4sNp/VvACuBvEXFGnWVPBO6nGI1mE4qRdD6a261H0ulARx1DWZqZmZmttxp1dJn+GpF94jP8Y5ZWSRsA44E9at6fAlxbbwJf489p9tW9KWacPar2TUn+5crMzMzMeuQkvnuzgAPT8h7AAqBd0hhJmwKvBvaW9L9QjDYj6RxJs9JsrsfkHCQiXkjH2lnSNElXS7oBuF7SWElXSpon6XZJe9fsuo+k2ZIelvSRgTppMzMzs0azJvr/akRO4rsREU8BL0janqLVfTbFaDYHApMpxqH/W5fdtgEOAg4HslroJZWAN/HiuPb7AcdExBuB/wLmRMTewOd46YO4e1PMYHsg8MU0XGZ35U+X1CaprbW1NadKZmZmZtYA3G2jZ7MoEvgpwLeBCWl5BUV3m66uTP3a75O0VS9lvzJN3BTAVRHxe0nTgOsiojpcwUHAuwEi4gZJ4yRtnt67KiJWAask3QgcAFzZ9SAR0QpUs/eoZ6QIMzMzs0bQqC3p/eUkvmfVfvF7UXSneQL4D16c5XVsl/jna5Z7mzCg2ie+q9wsu+vtOkJvXzMzM7ORyd1pejaLomvM0ohYnVrIR1N0YZm1Do5/K3ACgKRDgMURUR1Q+0hJm0kaBxwC3LUO6mNmZmY27KyO6PerEbklvmfzKUal+VmXbU0RsVga9Nl5TwfOkzQPqAD/UvPePODGVL+vpD78ZmZmZjZCKBr0tw+rmz9oMzMzG2iD3qrZm1PeuFm/c5zv3vzXIT+Perk7jZmZmZlZg3F3mkEiaS/goi6bn4+I1w5FfQA6K5WsuHKpRKWzPbvcUrmZJ1cuz46fsPlolnd0ZMePbmqinpF1SqUylY5l+fFNY7LLL5XKAMxZ8mxW/L7jioGK6rn2AJf8+d6s+KmvLOYgO/mGX2fFf++wIwC4e/EzWfH7j9+aa598LCsW4K0Tdqz73qm0L8qPb96CZ9pXZMdv3dzCEyvy7oXtWsYA9V/7VUsWZMWPGrcnAO1PXJMV37zdPwFkX59S8xYA/PSRvPq8b+c9WbVkXlYswKhxe7NqUVt+/BaTs+97KO79er9Hnmtf2XsgsGVzMbBXbvkTNh8NQKUz714rlVsA6q5PpWNJXvlN41J98n62SuXmuuMrK57IigUotWzHxh/9YHb8379/HvcufS47fo+xW3LAuWdnx9950ql1f4fX+5086OVn/p9VahqTFTfSeHQaG1ARMR/obgQaMzMzM7N+cXeaISDpLEmn1qxfI+nHNevfkvRJSaskzZV0n6RzJW2Q3h8v6e+SThqK+puZmZkNF56x1dal6hj0pMR8PLBHzftTKIaxrI4nvzewO3BUev9Y4HZg6rqqsJmZmZkNH07ih8YsivHmoUjeFwDtksZI2hR4NVCduZWIeCHts3PaNJVi4qkJkl6xzmptZmZmNsysWdP/VyNyEj8E0rjuL0janqLVfTZwB0ViP5liPPq/VeMllYA3AfMlbQdsExF3Ar8A3tvTcSRNl9Qmqa21tXXQzsfMzMzM1i0/2Dp0ZlEk8FOAbwMT0vIKiu42AK+UNJdijPerIuL3kj5FkbwDXAqcB3yruwNERCtQzd6jnpEizMzMzBpBo8642l9O4odOtV/8XhTdaZ6g6CKzEjg/xVT7xNeaCmwt6YS0vq2kXSLi4XVQZzMzMzMbBtydZujMAg4HlkbE6ohYCoym6FIzq7sdJO0KNEXEhIiYGBETga/jB1zNzMxshPLoNLauzacYleb2LttWRMTiHvaZClzRZdsvcRJvZmZmNqIoRmg/ohHIH7SZmZkNNA11Baa9dtN+5zgz7nh+yM+jXm6JNzMzMzNrMH6wdQTJHZ2mXCqxtKM9u9yxTc08unxp74HJTqPHUulYlh1fahpDpTO/PqVyM6uWPZIdP2rMzlRWPJ5XdssOADyyfElW/M6jxwHw5MrlWfETNh8NwOMr8q7PDi1j+lSf+5YuyorffewW2bHV+EqlMzu+VCpn35dQ3Jv1estFecOrXnfidABWLWrLih+1xWQAlnd0ZMWPbmoC4OmVK7Lit9m8BSD7epZKZQBWPdftIzUvM2rLKVQ68u4bgFLTOCqdeXUHKJVbWFzH98j4pua6f87r+U4DsssvlZsBeKY973y3bk6fVeb1KZVTfOb3YKmp+Dmv916o5/rU+3OY+50DxffOuE9/PDt+yTe+w0N/OLX3wGTXt51d92fb3pl3LZvLxbUc9PIzfxZLTcV3+O2LnsqKf90W22bFNbpG7dPeX26JNzMzMzNrME7iB4mk30ka3c32j0s6u2b9h5L+WLN+iqRz0vJqSXMlLZB0WZr0CUkbSVok6Yx1cS5mZmZmw5VHp7EBFRFvj4iX9KGQJIrZWafUbN4HaJG0YVqfwotDTK6KiEkRsSfFDK4npe1vAR4Cjk1lmpmZmdkI4iR+AEi6UtLdku6VND1tWyhpvKSJkh6UdCHFpE7PArtKGiWpBVgFzKWY9AmKJH5mN4e5Fdg5LU8FvgP8hWJceTMzM7MRaXVEv1+NyEn8wPhgROwPTAY+Jmlcl/d3Ab4fEXtExOPAHOA1wOuAOyjGip8iaQLFsJ9P1O4saSPgn4H5kjYD3gz8GriEtYwRL2m6pDZJba2teQ/3mZmZmdnw59FpBsbHJB2dlrejSNprPR4RtZM6zaJocR9F0b3mYeBzwCJeOlvrKElz0/KtwE+AdwI3RsQqSb8E/lPSqRGxumulIqIVqGbvUc/oA2ZmZmaNYM2aoa7B0HAS30+SDqFoGT8wIiqSbgI26xLWdaypmRT92zcDvkeRvO/Oy5P4VRExqcvxpgIHSVqYNo0DDgOu6++5mJmZmVljcHea/msBlqUEfjeKLjK9mZ3itoiI56KYNncRcCTd94cHQNLmwMHA9hExMSImAiezli41ZmZmZuszj05jffUHYCNJ9wNnUPRvX6uIWEaRtN9bs3k2sCXwp7XsejRwQ0Q8X7PtKuAISZvWW3EzMzMza0yKBn0i1+rmD9rMzMwGmoe6HiJuiTczMzMzazB+sHUEyR2dplwqUelYkl1uqWkclRWP58e37MDSjvbs+LFNzbR3dn02uGfN5TKL6yh/fFMzz7WvzIrdsnlzAB5atjgrftcx4wGorHw6K760+TYAtC3Oi588voj/z7tuzIr/ymsOBeqrf25sNb5Syf+sSqVy3fH1enzml7Pidnj9FwE4+YZfZ8V/77AjAKiseKKXyEKpZTsAnp3/o6z4rfb6CFDfzy3AYTPOzYq/YdpJVNoXZcUClJq3yK5LtT6VzhX55Zdb6v65Xd7RkRU7uqkJILv88U3NQP0/tys68+rTUi7qk/u91lwu7vt674X6vvOXZcUClJrGZN9nUNxrcy96b3b8pBN/zgYfOj47fs1Pflb3tcn93ql+5wx2+fXey7t/87+z4u/71Be49OTts2IBjvveX7Jjbei5Jd7MzMzMrME4iV+HJI2WtESS0vqBkkLSK9J6i6SlkjaQNEPSY5LmSrpH0oE15Zwt6UlJ/vzMzMzMRiAngetQRCwHngZenTZNoZi9dUpafx1wZ0RUpy04LY0T/xnghwApcT8aeAJ44zqqupmZmZkNI07i6yDpy5JOrVn/qqSPS7o+tZbPl3RkzfvvlzRP0p8kXZQ2V2drJf17Vpf17saJvwXYOS0fQjE05Q/w+PBmZmZmI5KT+PqcB7wf/tEifhxwKXB0ROwHHAp8S4U9gC8Ah0XEPsDHUxkzeTFp3wm4DJic1qfw0hlbq44A5qflqcAlwBXAOyRt3FNlJU2X1CaprbW1tS/na2ZmZmbDkEenqUNELEx92vcFtqLoCrMUOEvSG4A1wIT03mHAZRGxOO27NBUzC/ispB2BhRHx15T0NwH7A3fUHPJMSV+gmBjqQ5I2Ad4OfDIi2iXdAfwT8Jse6tsKVLP3qGdkCTMzMzMbvpzE1+/HwDRga4qW+ROALYD9I+LvkhYCm/W0c0Q8LGk0Rev67LT5buADFEl97ThTp0XE5dUVSUcAo4H56dnYErCKHpJ4MzMzM1s/uTtN/a4A3ga8BrgGaAGeSwn8ocAOKe4G4FhJ4wAkja0p43aK7jXVJH42cCrd94evNRX4cERMjIiJwI7AWySV+n1WZmZmZtYwnMTXKSL+BtwI/CIiVgMXA5MlzafoL/9AirsX+Cpws6Q/Ad+uKWYmsB3QltZnU/SP764/PAApUX8b8NuaunQCt1G06puZmZnZCKGIGOo6NJT0QOs9wLER8fBQ16cO/qDNzMxsoGmoKzBSuSW+DpJ2Bx4Brm+wBB6KH7KXvST9a0/vrev44VQXx/tecLw/W8f7XnB8VrwNlYjwawS/gLbhEj+c6uJ43wuO92freN8Lju97vF+D/3JLvJmZmZlZg3ESb2ZmZmbWYJzEW71TuQ5m/HCqi+OHNn441cXxAxs/nOri+KGNH051cfzAx9sg8+g0ZmZmZmYNxi3xZmZmZmYNxkm8mZmZmVmDcRJvZmZmZtZgNhrqCti6I0nAAcCEtOlJ4M7o4cGINDvtMRHxi0Goy4bAhRFxQmb86yNipqRNI+L5ga5PI5P0rrW9HxG/GqDjbAq8G5hIzXdHRHx5gMrfMSIe67LtNRFxVw/xr4+Imb1t60d9pvDyc71wIMpudPV+VsOVpM2BiIj2IazDOOB04PUUM2vfBnw5IpYMUPmbAR8FDqop/wcR8deBKH8tx826H1L9joiIywazPgNlsL931jVJG0fE3zNjG+qzGgncEj9CSHor8DDFfxZvT6//Ah5O771MRKwB/t9g1CciVgM7SNokc5dz0r+z6zmOpC0kfU5Sq6Tzqq+1xG8iac/02riXsr+Rs62fx9hU0vHpHL5YfXUJOyK9PgT8BDghvX4MfHAtZe8k6deSFkt6TtJVknZaS3WuAo4EXgA6a149lX+K9P/ZO+9oSaqqi/82A0jOoChRBCRIRjISxICASpIBEfkQEyqKioKoIAgSFVFRBIYgGMhBQIKEgSHHIQoCBhRUkIzE/f1xbk1X16uqrnph0uu9Vq/3qvrc27e70rnn7rOP5q37fgWcJSmbYCLpPUDlsQKOabgPSdtJmjP9v5+ksyWtVtWxpFOBIwjHZ830WqNu8IM9H5pC0p5N9uXea3vuLyPpCkl3p+2VJO1XYd7qWEk6UtIKVe+X2M8iaa90nM6S9JXkQNS12VrSUemzPtrDdk1JE4G7gLsl3Slp9QbjWk3Sl9K5XXn+JNu3SNpK0paS3lJj+hvgX8QEeVvg38BvG4ylaf+nACsQ18ZPgOWBU3v0fZikuSTNlM6Jf0v6eIMxLS/pQEkPAcfW2I2RtHm6zv4CfKxX36nd7JJ2lvT7HnZvlnSCpItz49qtxn5WScs2GQPt7juDfUY0Pbb5NktJ+rakexrYStKmkk4A/t7DdlDHqo/JhCldbar/mjwv4D5giZL9SwL31bT7AfA1YFFgvuxVY781MVl4BngWeA54tsL2FOBm4NvAXtmrwvYGQt7qX4RD3/WqGc8E4FBge+IhuQ2wTYXtRsRN6mrgGuARYMOavm8r2XdXj+PQ9jMuIR7oewNfzV4VtpcCC+e2Fwb+UNP3DcDORKR5RuDjwI019ne3POcOAh4Cfgd8gKSGVWO/Zjof3kJMMu8EFi2xWyf9Dn/LnzfEBPXOir7vSn/XB64CPtTju97Xa7xDPR/aXCs1/d8+HOd+sr+aWKm7Pbev9Jg3PVY5+08B1wE3Ap8F5u7xW/6OmJBunF6/BM6osf9ZOv93Ta9LgJ/W2N8FbJDbXr/BtfsdYCIR/Dggfef9ar7vX4GTgJOBR4H/a3pdARN7jKVN//c22Vd4/47096PpOMxdc20tAeyTftNbgf9Q8qxJtu8BfkFcu2cBjwOz9RjLzGkcZ6TrZBwRDa5rc3E67+9M2zNW/aZEAOQB4JG0vQpwfondYO47g3lGtDm2bwW+QlyL/wO+C7yrpu+1iWfmX4HngV2AeYfrWPVfk/81xQfQf02mAx3Owowl+2cGHqpp90jJ6+Ea+4eA5RqO6btlrwrbBYAdCAd4l+Kr5jPuaPEb3Qosm9teBri1xO5zxMP8hfTgyl6PAL8ajs/Ivd/YcaYwGSNW2uomaAMeJlUPo/TecXUPiIo2At5PRBsfAg4GlqqxXyf9ljcBC1bYvCedK/8snDt7AUtXtLk9/T0E2DG/r8L+DHIToh7fcVDnQ9NrBRgLXAD8Fzg/97oSuGI4zv1kf3Pxd6nro8mxKmmzLBEY+AtwOrBxhV0rxxO4n9ykq8G5P+DYU+JwFd5/AJgltz0r8ECN7fy57flrbI8i7m0zpNf2wBENxtK0/18Ba+e21yKojHX935P+Hg98IP0/4N5ArIzeQwRilk77Hqno8+/ExHJnYM462/Te+wiH/bH0HbYEHh3uc5m4J89dsB3g8NPivsPQnhE9jy3waeL6/xMRLFmpx295MOEDXEFMEubvYd/qWPVfU+7V58SPHpwI3CzpN8TMGiK6vgMRaanCci5wJ3ssaz9h+74mA7J9QOpvNtsv9jD/uu1vSFrM9slN+k+4UNLmti9qYDuT7Qdy4/tTBd3ldCLScwjwzdz+52w/NUyfkWGCpHfZnthg/FdI+gPw67T9MeDyGvuLJX2TcLCd7C+SNF8aW/G7rA98UtIjwMuEg27bK1V9gG1LepyI4rwGzAucKeky23sDSLogfX6G2Yjo9AmSsL1Voc+rgaslnWT7L2rGa35M0i+AzYBDFfz+AXTC3FjmBO6VdFP6rtlnb1Vsw+DPh6bXygTCcVgAODLfP+EYVKHNuQ/wH0lLkY6FpG3T505C22NVaDsGeGd6/YeIZO8l6TO2dyiY3yZpbds3pLZrAbfUjP0hYDFicgBxb3uoZAwZBebqdD78ms65f1VN/wD/AGYhIp4AbyIczDI8SRyfDM+lffmxPJc+W8CXCUcV4rx8nlgBrUKT/iem/mci7iN/TW8tRkx66nCBpPuBl4DPSVqQzvfO4wkix+rNwIKEo+gSO4AzgY8Qv/Xrks6rsYVYTRkPrO+UfyHp6B7jzvCCItcgO5fXJs7TMrxq+xlJ+X0DxlW876R+ZwDmsP1swXwoz4iex5agRV1PBCRuSWOp+y0/RTj8xwIX2H65h33bY9XHFEK/2NMogqTlCE5zPrH1fNv31rS5zfZqvfbl3juaWGI/l27nZ0BypaR1iAnEHLYXk7Qy8Bnbny+xnUhEG26t+uyK8TwHzJ7G8iodx3OuEtsTgTfoPEx3AsbYruOVr09EYcZJWoCIWjxSY9/qMyTdC7yDiOD0dJwTF3jDtHmN7XNqxpIfZ3YjyJ5ktv32gv3iZf1kD7SS/vcEPkE4bMcD59p+NT34HrS9VLJ7T9UYU/9XV/S/BhGpmzPteoZYdr61xHY2gtIz0faDkhYmVhUuLdi1HoukuWw/m01+StqUPrTbXCu5Nm8hKC8moo2P19hm5/4rxLmfuh947if7txOrLesSUf9HgI/bfjRnM9hj9UNgC+CPwAm2b8q994DtZdP/ecdzWWLZH5LjaXv5Qr/ZpGJuguKT9ftuIml/o4L9lfXD9yYlYz8mfcZi6TMuS9ubpc/YOme7V/p3FeBdRB6JifvuXbY/WfP5PdGm/6rrNUPVdZvavok4d56x/bqk2Yn79BMltnMT1LCxwNLAPMD788c4ZyuCUjiWoGHNTeTyXGT7+YLtKkSQaTvgYSLY8B3btd8rtV2N4KmvCNxNTDC2tT1g0pt44VcQzvY2wJeIYMtnK/o+naCEvU7QWOYCjrZ9eIntUsDfk9O8EfEMO8X20yW2bY7t/MTvMpa4h/wO+KTtRSvGPIY4X8cCmxJR/PcSFLjXKto0PlZ9TDn0nfg+SpGchbcRzmZeQWYu4Oe231nRblzJbpc5qZJuJBK5zre9atp3t+0VS2wPB3YH5gDyUftKp7wt0oNrDyLiDBEF+pkr1HAkfZdIdlzW9jKS3krwdtcbxs9o6zgvTkwqLk+O65iqCLWk7YFLkgP6bWA14EDbt1XYH0jw+CfYrkxozdkfAJxYNlZJyxWj0JKWBP6ZrfxImhV4c96JLNjfBexhe3zaXp/4LVfK2ZQ61hlqHOzGY5F0oe0t0qQoi6zmPsKlycJtrpVkvxuxfP/H9BnvIVRM6pJ/WyM5bDPUrWwM4ljtCvyu7LyRNLftZ9L/rRzPwU4q2kDSLj0+4+Sc7Xd72B5Q8Rlb0Zl8X2X7wgq7wfa/MrBB2hxv+866ftoGb3I2CxF0oLHAYlVOZbKdiaDajSWc/gVqbNdNdtsQKzjn2D6uwnYM4YgfQ0wERdBRShVY0n3yWwR9B+APwEGuUO+RdIftVSTtRNwzv0kElwYEViTdQTwjlgAuIhzzFWxvXmI72GO7CBExH0tMvM6xvW9VP+kZtEWy34Cg5O1Y99ltjlUfkxd9J76PUqQH1yeJG1BeJuw54KSqCK+kWapufiW2N9peS9LtOSf+Ttsr17Q5z/aHG/Rd+7CpclTbIN2gVyW4tNn476qKkg/yMxo7zpJ2J7iS89leStLSxIRr0wr7u2yvlJzfAwk1lu/YXqvCflfipr8OcR6MJ6L951XYn2p75177cu/dAqxr+5W0PTNwne01K+wnnTe5fV2ORsGxXoyIMIuIFv7V9pLDMZbBoM21kuwfSGN6Mm3PT5wXlaoaTZzDXASwFLaPKmnT+vdRKBUtTVBs5k6RAAAgAElEQVRSsr6vqbFv63i+mYiUQ0TI/1VjOzcxIcp+m6uJCVEV5SJrNzORxwI1jmHOfg6AusilpB+kcZ+Wdo0FbrG9T13fLfrfkwiAZCs8HwWOsz1AUaUQvNmRzoS0NHiT7jFHAEsRHPCv2X4svbd4ftKloLT8Imf7f9lEXtKstl8qGc8qxErkPbbvU6zivRfYoWqym9rdZPvdVe/n7MYAl9veuJdtrs09RLT8dOAntq+uem5l9yNJewMv2T6m7L5V8Tm1x1bSR4jfZqLtP6R9yxC/zfcKtrMQqwfvICh4J9p+TUFF/IgL0rmDOVZ9TBn0OfF9lCJFl06W9FW6o4sGlswe/CUP+LslPUE4eOOBa2sejH9LERanmf6ehCpI3bh6OvAJR9a8Z6Bs2Txbys/jGYKLe5AH6ja/YttK3MIUwSyFpA8Di9j+adq+kVjiBfiGq3V3HyYe6j9W0CPqHOc9CBrBjQAO2shCVWMiloMhlFp+afv3kg6qMrY9DhiXHvTbE5zdT9OhsxTRJSmYHph1Mn4zZk5h+rxXVC9BerVKeM3ZBM72bZmTLumXRITqorT9QYLzOeSxDGHC2OZagWZc2fy4is7hngo966JzWHX86tDqWEn6FHF9LwLcQahkXE/JdZjsi47nrySVOp7JfnvgcILXLuAYSV+3fWbFkE4kaBbbp+2dCWpWZc0FBR0iUwsRsKikXcomIpJWJGQc50vb/wE+YbtM/m9zYBWHpC+STgZuJxRfqsbSpv/dgLWyIIBC4vB6ymUR308EbxYhEm4zPAuURXdPJFTGrgG2Sn1uDaWrhT8h7hmZ7Y/S51HhwH+HUMy6FThM0iG2f0moEF1atC/gOkk/IZS9JgU/iteigyr0hnKrQQ3wC+IcuBO4RrF6VOTEZ3hV0liCVrhl2tdLVrjnsZX0M+L+OgE4UNK7bR9o+09AWd2OkwlK3XjifFse+LKDy19W+6LVsepjCsJTQXZt/zX1vohow4NEtOVIIjnmV9QrySxGUHB+RtzsqlQBFiAcjCcI6chfkcvKL9hem/4+R06Ojx6yfC2/62FEItK70uv7wA+BbxDJQEX7rxE39IcJh+N64IsVfV9HToKPcGTmT79VpcJIzv4txBLxX4nkqDKbG9PfTIllRuolDi/MjX8eIlGvTp3meOKhcQ6hyPBuyhWP9knH5bXcMcoczkNq+r8M2Cq3/eG634bgdVa9/liwLVObqJTxazOWwuc+WzeOwV4ryfYUwrnbn7j+biNk6EqlWYmI2wy57TF150PLa6XtsZpIROAz6cJ3AmfX2N8FzJ7bnr3HuXwnsFBue8Ee5/KA37nut0/vN1aWStfJxrntjYhVk6rvOl9ue75ex6ll/xPpVtWZpe7cTzZbNzwP7ihsVyr8FN+rs03v30OSNCTulTe3OD973hNytucR99UTaCBbXNHHgPtg2r986m9s2l6SCNoM6dgSE9Ax6f/Zqs7D/DmQH2uD377Vseq/ptyrH4kfhZD0aef4hMXtAhYBVnPix0raH/i97dLCH4mftx6xDL4ycSO+tszW9n/o5ttXwvb66W+jqKEGV8X0ve7mfE7MLYcO+L62j5C0GeG4LUtQUS6r+MiZbf8tt32tI7L/ZI8I/vHEgyCL2G5LOG9luFrSvsCsaVyfJ6QJq7A9kex5hO2nFcmeX6+xn59wBJ8GngL+45KkKNuHAIekyFlPSkAOnwVOSxE0ESpKn6gydoslcOAfisJF+YTifzQcC4TkWikNKD+OtFTeaFxtrpWEP6dXhmw1pu6amIc4VhCJaXXjeTtwNBElNzEp/Yrth0vMWx0r4H+2/ycJRdXl+1VfXEd0VopI/6vCFmKykqfPPEl9McOXJK1v+1oASesRSix1aKMsNbvtK3O2V9Vc5wcDtyuSbkVQfL5ZYTuY/scBN0rKKJAfoUKRTKECdBzwDklfJ0ejqMAsklalc2xmzW+7O/I9T+G+3LVdck9+2Um1zPaTiUrTCC3vDWfTWfHpiSoqFiXqN7bvlfQ1YJkUYX/Adq9iT02O7SuOgonYflFS3bUBncR2HDSaHuatj1UfUwh9J350ongF113RbybULTK8kvZV4a8Eh/5gV2f3Z2oPpbD9par3JL2LiOJB6EZXVafLli4XItQ2/pi2NyYiHWU3oTFpWfKm9FlrEk4rRFS5bKyXEVHJXuiqXGr7C7nNBalGI8c54ZvE0vlE4DNEItXxVR2nB+TZue1/UpAULNh/FEChcvR+4EpJY2wvUtHkW2nys6TtAyUtSmivD1CtSP3/GVi7Fxc0g6R5CMdxCXL3sorzZyzx4D2HOPeuSfuq8IbtrrEokjl7oU2SUc9rpavjisS2GhxCO+fwdOCnBGcaQhnk14SueHEsrY4V8Pd0vM4FLpP0XzpykGVo7HgmXKKB8qp10pqfBU5JDhlErkRtAitwa5pU5yeCVbKXDyuSxbPKqB8nVry6kBzTN4iJU8bn/4ZrVIcG0f8NBM0oS6bf1fbtFf3+lAoaRQUep5t2k98u0havoXNfhnB+t8zZFu/Jb5d0fvZVgKVy27heznR+4npfP/V9LZHzMIB+ZvvkRAV7Z7J9wDmqWAkaU7HaULByaHJs36lI7IfOb3NX+t8emJe1sqRnc/azpu0qYYi2x6qPKYR+YmsftZD0LeJmlX+Y/jZFW8vsVyZunBsSVIEHgattn5CzyR6W6xFR5qzE+HaEYz7AoUkP2/NSn3cSN593EY7Qhz1QpzdrdylRDOqfaXthIjF3wIMpOe0nEgo4IiLsuwH3Ah+y/buC/dZERcyFkn2dfOVpRGLhLwv7PwNsZLvOocw7zl8hllGrHOcRg6QtiKjxhkSE9wYi4bBUHUXSsYSDsont5RSJjZe6kPwo6eO2f6WKBEuXJFamdhPSGCamz8nsTy7YjSFk3Rqt+qQ2Zeoct9qu4/SXtqux7XmtFOzXIFQ0Fqd70lKZSJ3O93yyZ50k5YCkbNUkmkv6EMHLzSeqlvFxi+3eQ6wKXFLmLCXHc21Cl3ySilOV45mikIsQ3zNvX5V8PwY41PbXFIl9VN0/Cu0aK0ulc/0AOk7keOAA2/8tsb3F9hq9Pn8I/TdKpEy2xcTwxufzcENDUB6SdBnhiOYnXBvZfm+J7eYErfDPxD18SULq+OKKvu+wvUqvfWn/rYSW+wNpexng13X3kSbHVtJXCIrmU+Si7BlcIx/ax/SFvhM/SlDlIGWocpRS29XoqERcUxPFyeznIG5AGxBRBFyi7SvpBqKQx2tpeybi4bt2ie2PiVWAvd1JAJuBqP44q+0vVozlPtvL5bZnIJQOliuzTzZzpzH3Uqp4iCj/3bNgjyLBNNMDz5aYVyd46B9xif5yatfYcU6UgP3pOHnZpKJU4rAtEnVifPr8OipKZp9RkWrVhxTFfn6hCom1qgh0S4f5WmIyURdhQ9I7Ccf0MLqpRXMRBcdWKGmTX1nagdCzzo+/bmWp0bWSbB9IYypOWoqyi+900FVKf5sCxQF1ZDi/QUSk88W/5nUJJUrSzwku7sbEas+2xCRht4q+S+Fqic/Gjmeyn2j7XS3sbyi7z9TYjyHuG6XSuiW2jRVPFAnI/2FgEmbVb9O2/yMIatTZ7vHAl/Qw3UWmjshvF2kUkva2fVj6fzvnEvQlHeyc1KGkH9n+cvp/T9tH5947yQUNfaX6CxXjXMz2X8veS+8PkCquOkcURa22sP1Q2l6KoIxWyShfT9wL8lSsI2yvU2JbNjGuVDBremzTMV2XWD2YSDj0Ewju/IDzRtKPks11De/dTxICCVm/N7p3QcY+pgD6TvwoQc5BWpaIWGXLklsSD99SjvsgPucWwjGdQMfhq9I0fwBYJ7vppAjEDS6RzFMUPVrJBSqJpBmJpJ1Spzw5nkvTvcz+UJnTr5ayc5Kuc40mfEWbTeiottxj+4897Bs7zulh9BUiAW8Sn9glS8iDhdrJ+N1IPGhuTs78gkQkvtQ5kzRf8QEkaUlXFM9K0ajniQTdfLGksofYKcByxHmfd5SOKth9mFht2orONQKRmPsb2xNK+q6lYRRXBnLtGl8ryf5ap9yQOihUXD6t8sJGdqGgkcr17fP2AyaB6siTZn/nAC62vUHBrnXfqV1jxzPZn0zI/d3cyzbZH0tIKZ5B9/lQV2jrPCJxvdJ5zNleQSSH9lQ8UXfRtdxQqiffLfvPin69Rqxu1K0Yjqvpyi7IOuYn0r2i+G1sS+yvcE4qt9cEXtJRROGvbPV0W+DdtgdUwZV0s3Org5JE3NuqpG1XISgycxO/5VPEam9ZIanBFBBsc2xnJmSg1yWkf9cBnvbAomhfSDbrpl0T0us6IgH8jYL9XMRqWNZmdaL4WzYR6FqV7mPKoe/EjzJIuoaghmSJqnMSUYcN61s27n9B2/9uaLsrETnOc3b3L3N6qpYre72X3m9UxVTSWQTXMfv8nYGVnavIWLBvXXEztVs5jcdpPANu/gX7Ro6zku5+XV9DgaTtiMjcVcTx2oCISJXK+CmKoXyMKIhyMvEg3c8VcpqSrgM+mEXfFBSiM4oRtZz9HoSC0NN0IuFVTmfbKP86tq8ve2+40OZaSfabEjz+K2hwvqlEh75s32CgTo2HGwgu8FPA3bbfMdS+U/+NHc9kfz8xWX+UcMp7VTYuc1YHOKmFNtcQdSFuotvxH8DNTg7/qkS+TN62clWmDUa6/xbjyK+yda2e1G2XvFfmxDfuu2Rc2fmTBTPG0Pmdus6jNKFbnHD4TdA6/wpcnoyrrq+eVCy1LO6X2jQ+tinwtA5BTV2HWK2daHvXmv7fSsc534pQdaotlqhIrN0V+DKwpO0xdfZ9TD70E1tHH9omqrbFKykK0jOabXucpIvpJM7VJXQVVRAyiIhmDoC6l8BLHfcClrK9TW77AEVBpyrMRVSPfV9uX23Sjzr612elsZ+mev3rouNcp399paKy7dl0O3lDLmyVsB+wZjaJSJH1y4GusWTRc9unKTihm6axf8T11KODgQsUXOtlCUnFOh77V4F3OFSOalHlrNfg9jRJKHK+yyoPL0A8qP9L5FQcTkxw/gx81WmZvgSNr5WEXYnl85no0GnqzrcJxASq177se4whagYsQTfnvoxqd6EiUfUwYuUHapKoU/9bk+P52j63ytYNVahyqEu+LI5jQSKB8yHbT7f4jG+3sO2peKKOEsyAgjrD1H++ENNdxIT7sSadq3liqCv+L9ueIa22zpD7P7uflzmFbfrufrPH+SNpBXdEEWYh1L8yDv6/gVmJVeoB11fxt1FQ9aqSZl9Oq6lXENdsr6RZaHZsjyPuTc8RtJcJwFEuyYnItcnyyNalk4/2EJ0E2rxt3tHPAki3Es+AEQ1u9NEOfSd+9OEU4CZ1qz6ULvcPEm2LqIwhbpozEjJcy7g8c/+fdKsg5FHq+DsKeTygHvzJHFrJztVFO2rQpvAKNHScE7LJUD5JrqgQMRQ0lfE7E1g9twR+f5POHcWmZiIKucwJfNRRvKQKDxGTqJ5Iv9veDHTKq36bU9O430/Ix+1EdSGy0wmVkqWJKO04QqpxA8Kx3aiiXdtrZU3XVGfNoE7VzS65P2LSOVtN0wuIqHcX577Q95rA32wfmLbnSPb3EzUVqsb0M6JaZEZr+6ykzWzvUbBr5Xgqck32TX1PJOoQ1EVGP0VMFv8MLKmQ1z2/yj61yVe7nAic4GqFKBSVNBckV0mzAm2VYNr2X1mIqQF+k9plQY2dCM5+MTE0Uz3JK56Qtmcp2M5NOILZ+ZgPLpQ55QspcrmU+z/ru07RqwlOJU1me93HJe3jbiGHpr8NKSDxc3JJs4ocoKqk2abHdjEiePUg8BghgVs5IVUk+s5F1Ce5gVDEqpsw/p04Pj8Evtlg4tHHFEKfTjMKoZaJqi37bpO5fyhBt7iHXGSxbHl6CONpswS+MvHQ65Kdq6K7SDoMOIhw9C8BViJ0tX9VZp/aTCScsf+l7VkIznhpUp4KyViKxNw7q+xHEinKvxLd+QV32f5Gwe52gmv8OUocu2JkV92JoSImHX8mqBGVFIE0EV2BoGPlVx7Klp0vJR60XyMcsl2AfxfHnv8OtldVh/Ndl3R9p+2VU6TrL7YXy73XigbWw34ccLjte8vez9ntQlTdXINuCcTnCGWmKnpAZcJdzuY2op7CU5I2JByaLxJl6JezvW1Fu/vT+07bpQnmksbT7Xiu4wo6W7K/hHAMrwG2AOZ0IUGyYH83UUjn3wpd/NNckpBYaPNbOtUuP0gc4z0rbPOVNDclisQdWGHbWgmmZf9d51KT/nO2jRND2yBdI4s2CaqoggKXYRCra/m+h6LYMyJJs22ObbJXss8i5isStLbrbX+3YPsL4t79EuHEX5/sSlcxJWX8+nWBJYl78fXpdUsdHaiPyYt+JH50Yjaiyuk4SQuqJnlwEGgTzf4IUQGx8Q1B0mxEhcrFHMl7S6c+LqxocibhSJYqPRTwbHLGJnEdVa8N/j7beys4948SUa68rFl+3Jn6wrDrX2uQEo1tYfvrkrYhlmIBjnN5fsEOxPeakfpCRBmKWttV2ttFnJteTTC/7RMUqhhXE4Wx6pIgM9m2pxVFWh4npETLkBVdsaJEeh6lEe2EtgWH1gbuUCRCvkwF79uRU3KypG1sn1XTXxEXS3qf7bqS9mPcSRz+GHEOnAWcpXrq2UNE9DBL3F007StiTndkWA9Pk4Y6LGz7W+n/PzSwf8UpD8H2wwrOci8snzlokk4gAgJV2JDIo3k93avGA1WO2GAK6rTpv00hpiIulbQD3YmhdZHhRkjXyO8JWkcv20E76U2G0sK2SOFs89s852463cPEZLoMbY4taUJ8t6SniUJTzxAT2XcTdJ+87WdgEo8/S1jdQ7FCebftXQr2mcN+VGq3BEEvOpmQcy2usvQxhdB34kcZUnRjDYJzPI7g1/6KjmM2VLQpovJw+vw2s/pxROQty7J/jIj6VjnxCwFfIpYGTwT+kEUDS3AWUZ02vxx/JpGZX4bs+vkQkYD5jKor4a0E4VBLuopmhVcyx3nrnH2Z45xV82vLI26NzGHrYfMAcGiK7JYuGxfsJ9G5JM1KTNAeqGkyqV0L+8wp/2da4v4HUd6+CscpOLv7ESo1cwDfqbDNitKIgQVq6iaBbQsOfaDmvTJcpZBm7VnwJuEG4JwUJX8VSpNJx0ia0UEn2RT4dO69uufJnMB9km5KY3k3cEv2W+VWxlo7nipwq/PbHqhUtEj6TUq3K1Z92lS7bFNJM19Ap7hdlevQpv8iBbGuEFMRuxNJjBlfegzwgqKuRfGcaIvbJK3p5kpCJwN7OuUvpON7pGuSkIcZxedFm9/mFkkX0Z00e3M2WStM1BofW0lfohOBf5WO2syJBOWrCi8TFMSX0v+LADNXfMY7c5+xHh2J45/X9N/HZEafTjPKkKJlqwK3uZP533MZvUG/+Siw6DiWLxA3tgHRYIUazMoMVNuo09W+xfYa6qE9XmgjIvl0V2IC8zuC1/rn9H5rbfDU7gdExPklwimZB7jQJQoxaVl1LAOjOtl3Lo2KKZIHl06bf3ID2bHhhkLpoa7CbuUDXS0KAknakuBDz2x7SYWU2/dcQa9qYq9QnvgmQR8bT0SAjyGO7QHuwYduAnWK0sxOHCsDDxD88tKiNBp8waH1gaWzVTRgjqpVNLUoeJPsHwE+TPBxS4+3ovjb5oSu+WLEpNeS3gGc7ArJVTUs3KNyWcyc2QB5zEeJ1Y5GEpYahByopNfpUPFEJD2+SMkkR9KLdFYYRHD7H8rZtr7PKip8njyC/W/mqDw9WZDuhe8gVmWaKAkNoL6U7Ws5hsZ1Atp+lnJJs2oh2dnm2CoS4q8jdOErq2zn7H9IOONLA7cTUfbrCErNAC59WlH8R85ugqsT9PuYguhH4kcfXkkP3YybOnuvBg2RRYEzHfrziJvPx6lefj6fbi3uJnglRV+z8S9Fj0h++r6PE5Go14B5gTMlXWZ77zTmLQgnPB8Ze46IulT1+00FL/6ZtAT6IuEEleFtwJFUOBsUomJpmf8XxCThYSKBdHEFDeezLq90OQuRONtTUaUNnJQeJB1IRPdOTd9jJ2DhqnaqKAhU81H7E5Ohq9Ln3qHgLQ/F/mGCnrN/mgA9k8ZTNea9iON5QmH/bgTV40clza4nJoCfIPH4CcWnY2z/QNIqtruoJul8WT/939N5T2Nou4q2sLs5tQdJ+ljNR/yNWFqvm7B9X6FjvTCh+Z/ZzkBw46vaXS1pcWICcnm6hmd0krrN2TUtYLSZ7ctsL9HQfgXb95Q56RX2xzjVknBDOb0UIa4sIjcE7ElHfGAk+j+UkDOcBEm7ubvK9hhCHnY4KC6NlYQSZpA0r5PyiqKAWKnvooaJznkHXtICrle4KpXErcFgk2YbH1vbtcUbS/AIkYD/hJuJPCw1JQJGfQwCtvuvUfQiEvt+QTg3uxMOyBeHsf9rCGcn256TSJ4drv43I5ad/w2cRjhNG9XY70nQb/5ALGXOlPbPAPy5YLtOwzFsXfeqaHN7y+/5vfT9ir/lKcCBFW3OIDiUfyZoGZcCRw/jb39nk3259+4q/J2DSA6tsr+h+FtlbYdiT0ygfkus+Gxbd6zSuTJTyf6Zq8YC/JiYpOSP1VyEfOCxwCMV7Y4lJrE79zp/kv0dxOSp6e9zFJGfMEN6bU9UlqyyPyldv/sQeSd7AXsN07mzO3Bzds0REcErhtDfbVOTfds2RAS0qW2re8dw9E84fBcRk7UV07GrPHcafs5c6e98Za+adp8g1I8OJIQE7gd2rrC9hKgd8X5ixe2kmn63JJ4j/yTUWNYdyvcbzPEa5HnW+NiWtJ3Y0v5kYJ7c9rzAicPxO/Vfw/PqR+JHGWwfIWkz4FkiovcdD+9Sak8deoVCS1ED+D+EysgRrilGY/syRfLa2oRDs6froyjzEY5RVyVM229I2qJg+1FJ99BbbSaL1i9ELFFmVVc3JniJtRq/DbE1UWFwkoSi7eckfZ7gJZZpVr/D9naSPuzgi59OUEiGCy8oCjj9hjhmY8kp/pQgS9J8UaE7/BQ1kXvgHkk7ErzmpYlchgEVUtva235MkUz3feLY1Wmsz2j71cI+bL9Sw1HdnIgwO2f/rKTPEef1ByvazULIdOZXYep039uuomXc3V+lfnvxmh9Jr5mp4MkOAXsQqyY3Eh/+YIqaDha1xPRhsB8M2nxGm8TAwXBeh9S/7R3Tqs1E4hrf0fZ1gxhHHqcTK563Fj5Tabt01c32KYp6E9kqzdauVmhqk+j8fWAD2/crNPsPo6MVPxQMJWm2CYaSVNoqH4Gokj6JbmP7v4oclT6mEvSd+FGI5AjfSDr+Kil3PwSU6dCfVLApOs8QzvYuRPSkksKS8DbCIZkR2FASrpDNc0Fqq/BeUSe3kdqM0xKpQrZweSdOoqSFGfhdM+zd4zsV8Ubegc999vOZE1eCNooqg8GOhP750cSD6rq0rwptCwJ9EfgWQY/6NbF6UqnOULA/vcxe0gpExPsfxKSoF390Bklvtv1EoZ+6gmhv5B34DA7KzL9t31DWyO3rDPxOIRU3j6Tdgf+j5vd0y4JJHlk1kJfTRAgASTMyOOc0Q9u2kyP5q81njLSjN6TvmybFexJJ7MsBOydueKO6DKUDsrdIf5dMlJilaeiQ2r5H0r8ze9XU/lDzROfXbN+f9t+oqF4+uTGY4zSUY7sWsJOkRvkItKAy9TFl0D8YowwpCncAkXSXJYRVRkHawsGZvZiODv0A9ZViVDzhL0SVzFrNekknEhHyLm15hif6PVP620RtBkIVJe8UPkEk+5XhqArnu+om6sLDKI8q2cJMUeXbdBRV2lSZrIXtR6nm/E+CBlkQKDkI30qvJvhQirpNsldUuM1zWM8kVmvqZBPzOBz4vaSv0ilGs3raf0RFm3slfcL2Kfmdkj5OdYEoFImpuzOwQmppDkPbVbS0crATsKTtAyUtSkQqS/MSFEmlZZOR4SgWdrWkfQm1mc2AzxPFpaZWTI7IfVMMKQIu6U0uyPgW9j1a0uwCYA/bV6TzaC+CUlOa5N9yPJ8iJgiLEBSxteloo5fZb0XkE70V+BewOHFdlY1lbrqLSJHbLj7n8gWkBmx78NK8bQojTe7zrG0+wpHA9ZLOIMa6LbGC0cdUgr46zSiDpAcJ7nfPUvVTAuqtNHOv7eVH6LMbq80k+2OAZejWcH/IKSGuYLt43WcXJzaqV93Adp104YigqdOplgWBJF1AvfpNlTrNgOI1xX2KBOHXgMvdPGnyg4SizYppXPcAP3B1lcW3EZPIl+isOKxBqJh81BUVRyVNIOhOt5K05mGSjGeZ/aEeWFhrwL7ce8cS59AmtpdLE7xLba9ZYZ+XUp2FqEj5miP5e0hQyFbuRqhEiZB6/WV9q9r+znZNEagS+8ZqJMn+k7ZPajmmNgWE8upabyJ+6yXovq5KVZwG0X/P66Sk/VwuJIQqqmnXVVBuOraJhPjBDbZXUaiDHVx1PCXdSVDOLncUYNsY+Ljt3YY4jspVWhi4MqWW1YFTm9qkWUn72j64+agHp8yTHc8USR+AupX4tJqZ3Tv/WENl6mMKoO/EjzIoKhxuPZRl0WEYQ9nDY15Cyeb5Mic41/YEQiN4RG4k6SaXqc3MTiQrPl5h+yVC0WPDtOsalxc/GlFIWgRYwp2iQXsRUXiA0z1M0mBNnc78REzST4nKqPun7bIqpRkPdWvgLXToS2MJNYWvFOw/SPDQtycSVjPMRdCb3l0y9iuI876n4oJCiWPPtpE4SZvQiQ7ea/uKHvaV1Vkr7MucsUp52MxeLeRYS/q4qez3HCokvY+Qb92ssL/WMa+izUk6myiadrHtugJbmf0yhJzs4nQ7zgNWHaocn1ybpzK73P9fBH6V0RBK+lzR9t3p/0sI1aTidXVkzfhrJ3QKOt1/COrhrwjaWz4G8cAAACAASURBVBYQmAv4ucurhu5t+7D0/3a2z8i9d7Dtfet+iyaQdLPtNRVyx2vZflnSPa6W8s1khe8EVnXkM5Wex5LOJaL61xGVsCuj4nmaSMNxN64OrJC/PZEIILwObG+7Lr+nMfLnTos2F9reQiEjOyAfwQUp1pL2C9GteNZE4aaPyYA+nWb0YR9ggoIT30ibfQRQfDiZSPC7ilD0qMMpxPLe49RUrWyDMsdB3TSaKqrOgEJSDT5rbYL3vxyRPDgGeMEVWuuSrrC9aY99hxNKNhk+Q/yOsxHUqZ16jashZquK+hbQqiCQOxrhR9peI/fWBZLKqrf+g5CN3IpO5BtCEvQrJfYAzwMTFdrpk5Jxy877NIHbge5COT1h+490kpyb4EJJm9u+qM5IkSD7eWApSXfl3pqT+sTfV9OEJEuEXZCaCrIFZ3UGgkY0d4V5I6SJzc8JKsS5hJzhOOK6LVuW37JkX4Y62tzPiDoQP05L/+NcXwDsjDSuX5JznCuQJWJWycO+HQZEM99MFPUpLTJXcMIWsd22kNdmQPFa/GC2z/bdCk38TxK0lfy5/BwRUS7DDkQOC8SzIk9N+0BNuzb4uyJX5lzgMkn/pVPJtwxPKyh544HTJP2L6oT64wmxge8DK0u6j45TP8HduS4PKPTQr8tseqw0TJak2fQ8OpR4vojOMy6rJ9HKgU9tBpWPoHZUpj6mBDwVSOT0X5PvReh0H0U88HbJXlN6XC3G/xDhvC1J3FAWBxYfYp/j0uv3RNXMrCrpUwSdpq6tCJ7hb9LYDiY0dqvsbyGWY28nHPhdiWXZot0swPzAncQqRSbFtgRwf8H2tsJ2XoKwUtJxEL/TQcDmDey+RTwYz0vfM1vxewfxoKxqdx/w9tz2ksB9NfZ7l+zbs8J2l7JXTd8/BH5C5Haslr2G6Xd8juC1P0c41S/ltp8tsZ87Hfdf5895amT5UrudiNyIvxNOxQPAdjX2j+ReDxISpesP8bveDmwEvImgqj0PfGG4zsmK3+qzxArZhHR9lUmG3jpSY8h9RqN7AzHhflfDPj9HRzHmrtzrESLyX9ZmmzbHq+z/su1h+o3eQ9zPZ66x+RKwIBEA2CVtz9+g7zEEre1r6fd/vcRmGWKicxxwN5HXdF7FvaV4L+7aLtgW78lt5EcfImiHI3FOfiqdP/8l1OBeokbqNX3H+bNjT9BqThiJsfVfg3v16TSjDIPh040UFIofBwNvtf1BScsTfP0Tatpcb3udERrPpYRj16U2Y7s2GUjSyoSz8AHixrg2kBWSKtpmS8OTaBBlx0TSnoQ84FuBx+hEAZ8Ffmn7JznbrjyBwrL+fbaHVCBGnYqtWSXelwklnAEVK3Nt1qZTEOiFtG8ZosJoVXXaDxAP04dT34sDn7FdusJRQS8ZlvNb5ZVD7WFI8pS0uMuTu3u1O9X2zr32Fd5/J7EaIuJhPSDRVjVKH0NF8RhJesD2sg3bNq72m+znJyh5OxOrNacB6xMO8kYF2/2JyOI5dK9I1qp0KfIKuqKYtq+pse95b5B0LzHBfYQeq4uS5iYcx0OIvI0Mz1WNXS049/njVXLsann0IwVJBxErBKWrGiX2CxDR+HWJ33sWIoH2etcU+1IUDtycSLp9m+1ZC+8/SnXujp2jpEj6O92rH3vlt11D1ZN0nSsqHw8Vap+P0JjK1MeUQd+JH2WQdDChRnABLR5eIzSWi4kI+Ldsr6yQnbvd9rtq2vyMSDgtjn/I6jSS7neOJ6pIxrunyglOjvYnCO7p8cC5tl9N7R60vVRJm2uA9yb7x4lCI5+suilK+qLtY3qM+0ai+MmfCvvfCZziEeA0jxSSw5Edg/tdUNVINmMJju/6dOvgz0VE2zbN2f7O9vYaWJsA4sE72R9Gg3WGSpyqGYliT6WJ3mkidY9TVVRJcxERvhur+pV0lu1t2o6tZswPE5HQDIcTXHSgluNeWu3XFcmMCknbZYlqmSc5pxqVOSIF+0dKuulyxEo+o0xV5fqyiV2be4Mqkt57TfQSVerNdDvmAyZjasG5l/Q6HenBWYEsd0rALLZnKraZHFDwG99HTIjWAH5HRIT/XLB7kPiuZxH1NG62/XxFn5mjvw6wKBE8uCG9bnMNn77BeFslzRbaHk3kBp3L8D/j2uYjXE6soP2AiMj/C1jT9rpDHUsfw4M+J370YWz6u09u37BJTLbEArZ/J2kfANuvpYdIHWYlbmzvy+0bLonJyyT9gW61mctr7NsUksqwM8E3/gLB316UiJJV4XFJczoKPe1H0DoOKkSzv0vwq79PtyzivoTTMSxQ6Of/0Sk5NPFaN7J97hD73cT2HzUwN2EpldcAmEBMfhagO7/iOWL5N4/s++ePh4jffR8qMJhVohZoJSuXro9MnjFTwxAhZVen8HIsqfx7wvMl+4rjGe77wNV089yvyW3XXbfr2l4prVgdIOlIoFQdKOGXLuQWKMkoFh14GLS60550opgbZ1HMCts294ZPEb/LhGzVqhckfQHYn6B/5KV2y3KDGnPubY9pYje5YduKPKjHiWTReYEzJRVXPE8kJlfbAO8CVpR0PREcKj5briXulz8EznEPsQe1SJoFfuwWSbMFzEVMnkbiGdc2H+F84pn7ZYKeNzdRTbyPqQT9SHwfUwySriJutpc5VDTWBg61PRxV8wYznsmiNiNpZoKLCfCASyqE5mzvSs7M+gQn/XBCH3ytgt2KREGpLKJyN3C4B5EEVTOWMmWZIdNXJB1g+7uSxpW8bVfophf6mJ1Qt9nB9ocqbFYlIvjbEdSFs5yjJRVsW68SNYUiMe83Ve+7Islc0iG29ynsm6nq/Kk4XgPUbOooFFMKkm60vZakG4jj+iSxqvCOCvtWMoqSriUmGOOJPI3nGoypcRRTLahPknYlci/WISai44l7z3k1Y3kojeHJBuM+DjjG9sRetlMjBrPimdotQyfSvj7wn/yzRdJb6NBu3k0ENW8DridWWB4u9LdFzn5lIoenNGk2XeNtkmYnOxSqYHMDl1RNSNpSmfqY/OhH4kchksO3PN28zlOqW4wY9iJm+ktJuo5IXtq2zFBJ+kyhzV5WkGY41HVaq820haSNgJMJSpOARSXt4mpebRY9+hBwnO3fpxtrF5Kz/onhHm8BM5TsG/I9xKmqrltWME2ToQ8Rjvn7iSX0nxdsliFWn8YSD9XfEsGLXprxg1klaoq8nnxjZA58ohZsQnzvLQhKRRkeThPTY9P25wnKQBErpwi/GBjttyuUk4YKSau5Ij+CTrXfw4nr0ZSsOiRH7G3EuFeFLhnF2Wo+fmfCcd4GOFzSy0QSeJW6EbSLYnY59on6snqZoe1xwLj0XbYn6EefJtSHqvA3gjbSBOsDn0wUomFR9JrMaL3iKenthGO+FhGZX4iYuOfbP05Et89ObWYjqiAfACxJJMbm7S8ELky2Y4BViaTtw4v2thfKTSLWBb6qUIe6gXDoD6OAyfSMy/d3dQOb/SR9mw6V6SeSSqlMfUwZ9CPxowyJq7cR4cRfRMiSXetCAZ7JOJ4ZCS6rqIlKS9rC9oUK2bQBcE3CUsvxNOJeDqH/W4EdneTv0o3+17ZLH/CSLiQSWzcjaBAvEdzgRlxuSZ+23Uu2s+nYTwSeBn6adu1BqDJ8cpj635OIfj9HOGyrAd90odqqQmN8LHGcriQc82NsL1HS5xtEZHM3J718SQ+7ty7yVYzQKtFgo91pDDsSHNX5iN///Kple4W2848Jh9/AFcCXbf9rsGMfTkj6pe3dG9i9ieBjD3Ba1ZFRXINQfsrwLHByCRUr33ZhQh1lA4J7/9emtJOqKGae+kRQIrJJxSvEJHwAhUvS8cT9+AniXL2W4GS/VvP5JxD3zd/TzZsekDCpQXLup0UociPWIo7/BDpR8LKE7rmJKH3maK9KqDJdn9qcWdJmRJJmk823icDRSsT50kW7G65n3GCgFuINfUxe9J34UQZFgt/KBDVgZQX391cuFF2ZTGMZQ0RSl6A7OavsQXSp7fcV94/QuEbshlVBZ6gr2DNbGsdE2w8mx+NdRce25vM+Y/sXQx136mt24NtEYi7AZQQ/vxGPt0H/d6Zz8v2ETOB+wKklNInMMf+k7UfSvlLHXNJHiOXg9YBLCBrL8e7BiVYUJDuGqNp6N7FKtJ3tIue+NdS+eujBBAXor0S+xjnALb2+w7QMSTMRcooZte0q4Bc1k/xtXFHptsL+z8TKzOnEuXSHmxWJWplw+iEi96XnQxn1qabPcwgVqnsJis81RSpHSZvSxEnnEiY1hCqd0yoUuuYT3KMiuaTNiGN/fXplPPeXatqMaNKspCNSm+UI2dCMijNhSh2rwVKZ+ph86DvxowxKFRhTRHhjIup5n0uq902GsVwE/I/QrZ30AHVJ5v5go5ctxzPiN6wUzX6DTlXSjwMzuIT3nSY590yJYzMloA7//2jgKtvnqFx+cxXCMd+OeDj+hsgTKI04pjazAx8mIvibEEXDzqmaDKXo7+vkVomI4zRALWcokPQ2BlYNvaZg8y/gT8CPgAscXOwmqwnLEFSaN9teUdJKwFa2B9CxRhpphWsnog7A9yQtBrzF9k0V9scDMxHUMwj6y+u2P1Vh/xZCC79RInK61tcnHKz76TjPlStuqc3udBIMP0pE1weoR6V7xo7AkrYPlLQoUSyo9PumNssRtLCvAGNsL1Jl2wQaWKUzH9l1r/Nnekab54mkY2x/Ma2yrE3Qt/5EZwIwIGk2BRoaJ80W2s5MrCxlk4B1gKddoUI1kpB0AHBi2aqNpOXKVjj6mLzoO/GjDAqJxn0JJ+irhGLFHW7JRx6msVRGoEtsi1J1XahbNm8xnhG/YSXncA8iMgwRBfxZWWQm2Z8HfNENdLwLdJTjieXhAXSUIYz9Ssq5mkPWTk/9jyMekEsSq0VjCGe+lGqU2qxLOObbEMo05/SiDym0vrcDPuZCNdycTatEycFA0qGEAtK9dHIfbHurgt0Ygk41ltB8v5JYDVm0B+XiakLO8RfZREjS3bZXHK7v0BSSjiUmr5vYXi4dg0ttr1lhP0CLumxf7r1BJSIrKoHuStxbFnGNOouiWu467tQ9mJ2gUZTpuTf+vgpe9wbEqsM8RLR2vO0TS2x/ZPvLki6g/Frcqrivj4EoCw7U2JbdC4Y1abbQd0bzWS/9nYdYiZ3sz+g+pn70E1tHGWx/Pv37c4V+8Fy276prM4K4WNL7GjqZcxNJfFWlz4fsxDslWFa8NyQHXtKHCSfhp8BRknYgKBqrEhU1B/AvE+YF7pF0E7lS4xUP6/+zfXSio8xLRC9PJSpvDgfyk6hZCMe50okcBHYDVgEetv2ionhP7YPL9gRgQprAbEo4urVOvINDflyZnQafKDkYfARYtld0P0X5LgEuSZPALQjO9WOSrrC9Y0XT2WzfFEHwSRjO49UGazlyC26HOAYp4liF1yUtlUXGFYmKdYnFrRKRFZKV6wNzEI7Vd+iuOVDarDCG1ym/H0G77/uB9NlH2/5HjzGcmv4e0cOuC4lmMoma5EjSHM0YdPRSI5A0m2yOIxKinwNuJKg0R3nwUpV9jAL0nfhRiOISvqQNi0v4kwk3AOekpefaCqDAX8ooJ9MQ9iZWPzLMTKhVzEFEEKuc+G+3+IzModic4JLfo4IHNxTYLiqqXJcmF8PV/xuSngCWT5HUWkhaj1hFeoGgLqxGPCSHgvcTiZKLEBr0+Uq5+w6x7yIeJigjjSk6yeE/CzhL0pwEpaMK/1Ek1BlA0raEvv6UwKtpRSEby4LkKHQl+DpwZVqBy6r31k3oXkiTvqz/talXb7keOMw5WcAGGAfcqOCwQ0zCquoGNP6+tr+gyE1aU5GLcZMrko+za9D21WooVSvpB4S+/Wlp156S1rU93OfzdA2VJ83+uCzAo+qk2QsIrnsZFgPelOweI4I7Tw/vt+hjekPfiR9lqFrCJ4qNTG4cRdzoJro3r2vYnNEphJlt/y23fW1KVnoqLcuXwg1kwHK4VdKlRKRnn+Tk9UzWawp1J8jNQExC5h7G/tuem8cS8ogrE9Sw4wmu+1AUZJZ3FPLZ3vbvhtBPE7wI3CHpCroVRqp04t9ErH4sQbN79x7EasM7JT1GRAt3GuKYB4sfEwm5CymKkm1LJC6XwvYVkpYmchIgnNS6yU5judrU/5mStpKURaevtn1B3RewfZRCtWj9tGtX27dXmDf+vpK2IyLrVxH3uWMkfd0l6ii5NhvRXKp2c2AVp8RdSScDtzP8k9JpCY+2sM2ePeOA3d0+afZ79EiaBbD9gRR0WYEkS0kUq3qKoODUVoHtY3Siz4kfZZD0ALBSryX8yTSWa4iKn01UIVa0fbekLxJqOtPUEqOkh1xdqObPLiTNSrrW9vqSnqN76bdytSKtaGR0lKdTZPJtw0WXUneC3GuEU/g929cOU/+tzs2MqyrpO8Bjtk8YKm9dod60EnDrcPLfKz6rlVxqor89Q2jMv56zP7LMPtdudmLS9SJRDOu0OvuRgqLC6abE+XNFRQRzLiIR98G0vR1BHYIoNPNEwX4722dIWpLQTu8pV5vaHUJQIrLfYizhaPV0bNNKZkaH+Ec+L0HSku4oJvX8vsnuTmCzLPqeovaXV/H/k01jqdrE5d8oBQ2yyfhVZVz+6QWJvvJVYDHbu2cTwjIakaR3uaYQlqRP2j6pxWe3Tpot2b8IwYlfl6DPzW97nqZj6GP0oB+JH31ovYQ/gngYuEqRlFardexO5dE3AzdLmtYqyN0oaXfbXQVrJH0GKKOk7ARgu67gS9ZH8YHx9mFk0UyCR17SsO25+VziQH8c2DBNYmYa4hguAf4LzKFO0SOop3oNCrZPbkqJSFjEDXTMkyO8B8HtPw+4PG1/lZCum+xOfHIc/0VIZGb7yqrNHkHQFB5M2wcDFxP5COsS0qN57AOcQVTfXQ24p+GQPkTD6HQ6x2aynZWbn0BMpmYmouGH5MzPBFZPuQqbEso3vTBDgT7zJOWF1fKYKXPgAWz/SSHLWYZDgNsViekiuPHfbDCuaRnjiMnuOmn7MeI8KcsF+Fla5ToJOM2FegRtHPiENjffTOAARWG2jH7zKh3KzomEglsffQxAPxI/yiDpLEL5o9ES/giPpafWcUW7ES3INBJQFN45l/jNsyqVqxMcyI+URBgnRXMknWV7m5q+r0z/zpL6vIt4kKxE6ImvU9W25Xdopd09iP5bnZuKJNQdiQjqeIVs4UYehurDks6z/eHCvpldoSI0yM/YiAIlAqiiRGSJb8fURQ2T3XnEROR6IhK8UOp/T9t3DNf420DSo8T3+28ayzzA40SBo90zrrciEXS1bGKunIpItjpV6PcyYnVoTUoSU12h1tImOp0CBhu4o0pzu+1VE+f96vyY0vjPIK6TH5aMp6wGxuHEtZpNcD4G3GX7G2VjT22KUrU7EbKUZVK1CxATjjXSrpsciZfTLSTdYnuNwvlTp260NJF4uh0RVBln+7JBfnabSHz+Pn8USRve9pTKXeljGkM/Ej/6cH56TXH0ctZr2lnS44QT8BqhxHKmpKm2glyKtK0raRM6Jdl/b/uPFU3y0ZxaPWfbGwNIOhtYPXPyJK0I7D+UcRdwLBHp/lna3jntK9XuHgQuJyYGJo5rLYeUUHE42vbriU7wTnKR3qEgc+DThHETYrKwBbESNFw4EnhfkRJBTMQmIVF8TNyvd1Uke75MZ3Wg6Hi+3UlaUaG3/k+CVvC/YRx7W1wGnGn7D2lc7yP4/eOI82mtZDdjYWVt59z/ZXSCDxEJzacSv2dTtIpOu7ug2dFp3+uSipU3dyASXmcEeq6ipX6+LmkbOlHZ42yfU9eGmCTsAWQT3PF0rksAJG1JRHFfI+hXH7NdlVQ5veGVdGyyyeBS1KzwOQrp7UdU/f0xsGq69vf1MMgXN4HtvSbH5/QxfaEfie9jskPSTxyKDK21jjVKKsgVIjSNIjuS7rG9Qq99gxjLjA7Jvlba3W36J2gT/wf8hXCqFiMcvH2rIv2JF7wBMYm7DrgZeMX2kJM3FeomOxIO2XyEw3S+hzEXQw2r90qqLGIF4EJdg+L5MtQ8geGApIkuaLarU9zrDturpH13Au8vRooTD/3iKh63pAVt/7vlmBYmIvhQE52W9CdgheJ5mCgYd9teuqTNB21f3GY8Dce8EEH5eQdBsTjE9rMVtncB29u+X9JahBrPUJK+pxkokkv3A5YnJHbXIyo8X1ViuxKxqvshYrJ5gu3bJL2VSCitvf5K+jvb9tYNbRvr1ffRRxn6Tvwog0KWb386EpNZNG+yVe+T9KztuSSVPlBco8iiUVJBTqFx/QJxfGYlkhKhhpst6TdE8a78EvsctscOcSxZAultwHbu1u4+c6gOoqQfElHLr9h+Lu2bi+BHv2j7yz3G9UVgVtuHDXVSIelgYkn9r0RU/ByCkjTs+QBtKBHJfing746KrRsRFIxTbD9dsMvOHeg+f4ad198UCtWkK4jquhCUkc0IjfSbcxPWjwN7Evz9TPllNeJc+LHtUymBIhn0G4TTNku234VCZBqYP9IF27cV96Vz4i3AF5wqbyqShX8CPG57n4oxfYhYdcuP53u594tJ68WxlF3jlxBc72uIlaE5XFEEaGqczE1OKBL71ybO+xtcoSqjKIp2PHEve6nw3s7Fc05TMGm2jz6K6DvxowyS7ifKehcVLp6cjGMYcvQhRaTyD8eeFU2nd0iahW7O+jXAsUOlUeQ4wJsQyV9ZtcElCJm9K6vaNuz/QWCZAo2CxDm+vyzSmY0L+DzBPd7NoYs/IOLbciz/Ikqq/wi4IDnMD4/EJFed6r0Zpzqr3lu67C/pDoLXvARwEZG0uoLtzYd7bMONxMv+Lp3veh2h6f8M4Qw9lLP9ABFtXoFwcu8BflAX2U6ThN8SBck+C+wC/NsFXrm680fWIKr81uaPpPPw+wRtLAseLEZoxO/nkqq5kn5OJONuTDiI2xLR/t1KbA8kKE+nprHsBCxs+zsltl2T1DrHXNLfCRnfDHvlt13Cz5+ekCLsS5CjDQ8HNUbSb4nn5ydsr5ic+gnZalLBdjyR93QSJUmzffQxVPSd+FEGSTfaXqu35YiOofhw6ULdwyXxPI8C3kqoXSwO3DdUysi0juRoXO7Ejx/mvvPHa1Y68nqvAy8N1RmQ9CfbywzivQ0Jp+0624emlYEvewhJ2ul33IyQHNwUuBJ4L7BombM2OZFbedib+N2PGa3L8ZL2sX1IbvtW26vn6UiSbra9ZkX7s4HvupA/YrtSWz5xrDOZ2IdKorabOSVD5qhC2d85CDrQBiX9NqapJbrRRjApZ+bK/LZTom6yrdUV9yBzkqYFpFWulYgJYCZh7LJVrhRJP4SBqzilE3dNwaTZPvooop/YOvpwpUIN4Wy6FUAGLCOPIMYQlUoHo4N4ELFEenmKDm9MSAyOajiS7N6QNPcIRHuqjlfj5L0euFfSJ1xQlUnUikqJPoeKyzW57YfpJPoNCrZfJ2QmL0mR8i2IictjCtnAHYfSP4Ck39neXp2E1eIYqvS7X5U0lsgJ2TLtG6qk5mSBImn3awyMjG5S1aYHtqNb2jHjq/8z0Vj+QeQyVGHZPM3BUYNiuboPTE57nTLQoQSnGjpJ2S8mbvVTwMIV7V6QtBNBNTIxgXyhwnZuIgqcvxaze7fJJcFPz056A6xte/mGtuOIVaIfEisnu1Iv8TnNJc32Mf2i78SPPmRR+DVy+0wocEwu/DPPDW2JV20/KWkGSTPYvlLSj4Z1dNMungcmKmT3JjkBQ4lMJwzleDXBHsDZkv6PcFAgzs9ZgY9WNUo86L0ZyDselnM50VrOAs5SVL+tHEtL7Jn+btGy3a4EVeT7th9RFDgq5YhPhTgD+DlBLXm9h20TFCeUBylK3X8VOAaYi6ANVuEuhXJPPh9hqEXR8mO6UNI8wGF0zunjK9rtSCjeHE3ci69L+wbA9hKtBxWFg46ho34znpAb/XvbvqYhXC9pedv3NrCd1VEhWI5cq/0VSfMD6EwJ3yUm+otKOo2UNFtmqIFJs1s6lzRLBNP66GPQ6DvxowwjQbcYBIZSiejptDR9DXBa4jBXRa1GG85mZB4Kw185KgfbjwFrqVt+8yLbV/RoehrBg96CHA96OMakUEEaR8hY/pJIrixNYGwLdzSgP1/C2T6USNAsa3cvuZUGR2XQQ4djTJMBr9k+dhj761rByCUVPkNEU3thVyJ/JJtQXUPIpQ5pTJLWBP5m+0CAdK+aSKwoDdCNB7D9KPDhsveqIGk32yfktscQ/Pyy6Ps44HRi9QJi5XIcQRubXnEK4cg/Tr0cK8DLSupmkr5AFIaao6pj25cpkvyzpNk9q5JmicnT8UTU/aVcH/9I0fk++hgS+pz4UQJJRQ1aEzKN1yZnYHKOZb48d7Nl29mB/9FJAJubSBiabIm5ow1DOV4jibY86JZ932l7ZUnvJyYI+wGnViURDvIzBiQlqkRiMvfeI5TTbyabstRgIWl/IoflHLppfIO9D2TJ1rsTRZoeTBSFE4GtiQTUXWzfXtvRMCI5dgDvtf1Uytn4DfBFYBVguTLOfVpR2p2BVKNSlaLU5nRCN383YH7CKb/a9tdKbCdJeNbtm54g6SEikXciHU78ADnWZLsmcB/xex5IrOIcbvuGmv5HJGm2jz7aoh+JHz0o4y4vAXxL0v62f1Py/ohgKA6hu4uunDwMw5lu0DZBqymmRgc+oS0Pug2y1YfNCRnHe5KTOPSOpc8RqjpvV2h5Z5iToFJUIU+Bm4WIrA7X9x1p7JL+fj23r4vD3RJnpL97EsofEFzylVKfqxL84wGJpECZ1G4MaGjXyqPAUrnr5WNE4aaMklVVLfc8guJyOQ2pRrZ3lPQxwkl9AdjR1YWcnkz5JVkhtLHA9B70+LftpkUNn7L9PEFHLJXrzKMqaZaSEewV+QAAEs1JREFUVdCRuif30UeGfiR+lENRbvzy4YwwjgTU0VTOHKnsxJ1i2tdTGyRdSydBa0tSgpZLpOqmB0jagnB+FqXDgz6gxcO7ru9xwNuAJYGVieTeq2yvXtuwWd9zEwWqDqG7SuhzbSdM2WrEUMc0tUDSMdRrp3fld6i7UNTpwI22j07bdfKLjaV2JdUW7slHYCXdDaziKI52P/DplICNpLttr1jSf+uoeHIOTyac+OWAe4G9nHTsC7aLE9fHOsRvOwH4kqdjWV5JPyMi6xfQvfJT5mhfDSxCFIsbD1zjem33e5smzY62e3Ifkx/9SPwoR1r2HVHO83DA9nCooEzvaJugNU1CoYf/WULu721EhcXhzvXYjaBAPGz7RUXhmJ5RuiZI6kHPAGMVxYfWp5PQWOnEq7tQ0QxEZH6auIcrtLT3IjThP63qAjm3pL/rEdHL36bt7QhHtYg3FJVX/0tIgn4/996sNUN6xs0rqmZKQAsB6wJ/TNsbEw5x3jH8NXC1pP8QCjXjASS9gzjmZbhQ0ua2L2o4HgjndI/seid+25vp5JTk8YRrqmBPp5iVcN7fl9tXGi23/R5JMxPVezcCfi9pDttVq1wjmTTbRx+tME08APoYOSgkGoetlPxIoeC43UVUbZ2iut1TCyRdRCi8tErQmoZxMkGlGQ98kHD29qxt0RAaWNHz7SM1x5X0bWB7Oo7FOEln2D6oosmRuf9fI+gb25WbTnUYR0S9103bjxGUmC4n3vbJMIlytH52jSuKJ40v6fc7hOM/Bjjf9j3J/j10ipJNQu74NpbadaqIqigotXyWmJwmDycVbL8v6QpCTvJSd5a6ZyC48fmx5FcX95X0MnFeN1ldfLftZ9NnGjhS0gUVtndLeoL4/cYTeVDTddEhV1SxLYOk9Qna1QZE9P5Cys+1DCOWNNtHH23Rp9OMEqhck3o+gkf8CduVetxTAxRV8vKO219sD4vjNq1D0nZEBPJUotLo3HQStA6zfeMUHN6wQ7mqrJJmJCphDgsdTN0VPVcnJoy1FT2H8FkPACs7VdRVaE/fYXvZhu3HADvYPm24xjRSUPsCOQ8A62T0IknzAjeU/TbpHJjT9n9z+2Ynnm/Pp+3NkqpIXXVhu0aeVNJ9tpfLbc8A3JPfN9KQtLftw9L/29k+I/fewbb3rWi3GOGkrkfkeTw9nSe2NpbVlPQaMcE8hFDFeqVH3yOaNNtHH23Qj8SPHhQ1qQ08WUgUnZqxfM5xO4GofNcHYPsMSRcD3wauJZz5u9Pb6wHTlRNPJ6GVxD0eto4zWo6ioufqLlT0HLYPCvyDmCz8L22/iYjUdUHSXMRKy9uIJMjL0/ZXiUnGVO/E07JADvAD4PbkdAvYkIrfP0Xr/1vYV7yvHQpcNkTa1RWS/kAnQfRjxLEYEiR9FPhjFh1X6MtvZPvcEvMdCO15CMnTM3LvfQAY4MQnh3Y9wolfmUjIvHao457K0UZWcwHi99kQ+JKkN4DrbX+7ou8RS5rto4+26DvxowRlUYJpDCPmuE0neIVQqXgToXLyRr35NI2VJT2b/hcwa9oeziTn1hU9B4FngHsUxblMOBg3Sfpx+swsifNUwkm9npAi/BbxXT9qu0rxZGpD4wI5ALbHpYlpVpzuG7YfH8Lnd90wJB1MrFI9nbbnBb5qu1K72/YXUpJrpnhznO1zhjCmDN/N92P7aUnfBcqceFX8X7ad4a8EX/5g258d0kinHSxoe1xu+yRJXy4zTL/3w0SC/CIE5auuEvLtKYm6Z9IscGKaRDVKmu2jj7bo02n6mCYg6XU6RZ1EJC69SF+dBkkfAI4Czge+V6ZQ0Uc7SPoNET3LV/Scw/bYYfyMXerez/HD8/ShMcA/iQTR/9W1n9qQkoOzAjk3uKRATklOQhfKOOsNP7tLqSZP66mymVxQSW2A/DEv7J80xpLvVNyeMQU8ViaSpzcEFgMeJDTlT2A6RcpLGEe3rOautjctsX2YKMY1nij6ldHUPl/R97iS3XaFrn8hafYzxH1kWpGG7WMqR9+J76OPaRySxgOfzZL6+hg6UiL15wjHB1JFz+F2nBPFZDHbD9TY1DprUzPaOuVD4az3GEfxN7wLWNP2y2l7ViLnYYC6Sy4BdcBbDEMAQaE7/jTw07RrD2A+258ssc2CGflARjaWWWzPlLPNO/xzEI78BgS1BNuLD2XcUzPUQlYz5TasQjj62wGPAGfZ/skwjKOYNHsHMN72r2sb9tFHQ/Sd+D766KOPHFK0+/Ih8qebfM6WwBHAzLaXlLQKsZKyVcFuml2FGimnfBDjONv21rntbxDSkVlUdVdC3eawsvYjPLbZiXyW96ZdlwEHDTVfKXPiJd1C0OwmEJPRa6cDeuWQIWkZwnEfS1Qv/y3wtV6Tm5FMmu2jj7boO/F99NFHHwWk5fitPYJSfAq96E2IIlKZYktpQaDRBjXXlc/sZyGq4Gaa+9fSY+VE0gcJbXmIpNc/NBjX+sDSibO/AKGK80ib7za5IOnvBM1uDN2VReMf+6gpMa6RhFoUC0sJrOOB3Ww/lPY97B7VVFMOy+lErgrEysZOtgckzaYk5Sxpdk3iONQlzfbRRyv0E1v76KOPPgbieWBiemBPioi6UDF0iHjV9jOFJO3pKiFZLaqdFtBIVz6HU4DniAgpwI6Ek1Wpo+8o9tS04BMp2XQNYNk0vpmJnIn16to16PdK/r+9e42xqyrDOP5/aIWCUDESUYkVrIgpRAJiQIQYBCKJChZsGg3yAQleQTBEIxHiFaJgMGBEA6YRBFEiEI0XxLagkmhjK0atJUQpRsBLMEojWKE8flj7MGf2nDNzZs4+l5l5fl86s/c+a62ZNJn3rPOu9+0QeDbwKcUSSk3yxVQF4FdtX3+ScqC6m9Mp1X42SvoRcAu9/a4GeWg2YlYSxEdETHUbHbo7Nuz3kt4JLKl2ms+npDwsJG+d5l7HDpqVlbbXSnoHgEvX3OkCrMNsr2r7fqOkrh01a3nuu1MCq//MkJq0GjgC2FKt6RFJTXSSvqjt62XAGZRmXv161PanGhhn3mgdBgeQdEH79x2evQO4o0pnOg24AHihpGuB223/uMtLH5N0JpMPzT7W6cHaodkvA1+lvHGIaESC+IiImun++DfoPEq5yJ2Uj+fvBLp1a52XPIvOmTWzrSu/RdIxrproSDqaybuy9XU9G3xXbw5Oo1TOmXZNti2ptabn9vSTzMD25tqleyU10QdjMe3Ad9JTrnB19uBm4Oaq1Oga4KNAtyD+bMonPlcxcWi22//zVzBxaPYmqkOzPa4/YkbJiY+IqKl2xi8HVlF2RwGYKV82upP0ZuBQJv8+O+4USzoZ+Djl9/9jqrrytu/u8vwfKGkureojK4D7KTvarpdw7DLGlLKTtfsXAQdT6vlfTgnmbrZ9TbfX9EJSe7nB3Sidgq92j517pxvXVcfbxWiUVZzmemg2YrayEx8RMdU6Sj7tVcAJlJ223ZqcoMq3X1NrOHSL7Tc1Oc84kPQVYC/K7/J64O1M03XZ9l2StjBRV/5DnerKtzlllutpz9XfjZLrPm35UNtXVm8uHqe8YbjU9l2zmbeLzZQdXVHedDwIvLvfQRdjAF9Lk9pLk5vC9VXJaTaHZplIoXlL26HZC+c6d0Q32YmPiKiRtNn2a2qNljbbfk2Dc3RqODTtbvB81Wpo1Pbv3sAPbR9fe+5Vtrd1qy8/XbOn6jWt6jT3zvBs+8HEp4HtwHW2/977TxWLiSY3Z5tyaLaWj/82Su776ymdim8Brrd90BCWGotIduIjIiqSfkBptrOzagLzgKQPUqqj7N3wdM9IWtFqQFM1qFmouypPVv8+IekllIOAL+7w3IeBc4EvdLhnSknOKSRdSsllbh2UXSfpVtsdzxjMJldf0s9tH9eh6VNTzZ6ew+TGYncDX7X9VD/jRrOGdGg2YlayEx8RUZG0BvgspTzhF4HnAZ8GlgOft/3LBud6E3AdcA8lIDweOLeXeuXzjaRLKIcBT6R0JjVlZ/KS2nOnt8pOzianW9L9wOGtuvDVodj7uuWVz7Jhz8sG2RxJ0vWU6jitoPBdwC7b5wxqzujPXPLt2w7NrrV94kzPR/QiQXxERJsq1eMSSp71jUzsvrqpBjnVLv/bgQ1MVEX5xQx53wuCpD2AZZ0aabUHR7MJlKpa66vbzhfsC9zWrdb6LBv2tK/pO7bP6GVNPax5qe2nJf3G9uG1e1OuxfgY5aHZiHZJp4mImOx/lAZPewD7MIAGTLafkfQR29+mewOjBUXSscCBVH93JGH7hvpjXb6eyb8pdfdbB01PAjZJuho6NunquWFPbR1NVifaBBwJ7JK00vYfASS9HNjV4DzRgEEemo2YqwTxEREVSadQWtV/FzjS9hMDnO4nVdnCbzG5K+yCqyoi6UZgJXAfEwGqKZ1W2+0p6QhKxZhl1dfPBtHTHFa9E1hfjfk0sLHLOlq15Htu2MPkPPgmP7pu/VwXUZpT/an6/kC61x2PEWnvLRAxLpJOExFRkfQz4L22fz+EuR7scNkLsRZ9Vcd9lWf4g1OlxXTjenqMpKXAZZSa7Q9RAuMVlBKhF9cPh7bSICQdCFwNvI6Jhj3ntw4Z116zi/ImS8CeQOuNXV87sJL+QnnDSDXukurrXcCTTaVuRcTClZ34iIhKveThgOdaTOXmfge8CHh0uodsn9DLYJJOrmq0X0FJeTrI9o7q3nLgyupexxQZ29uBU3uZy/aSmZ+akyWUikf1tKGllJ8pImJa2YmPiBgBSXtRSiqusH1u1SX2ENsLJkde0vcoO937UNrPbwJ2tu7b7imQ7jBua0f9AeCV9R1+SUuAbbYPrl3/F/DTbuPOdT1zkcOREdGv7MRHRIzGOkq3zmOr7x8GbmVhHXT9LrA/pYRju+OZYVd+Bq3da3dK0bG9S1KnHap/0LkG/SjM5uBuRMQUCeIjIkZjpe21kt4BYPsJSQstsDsN+Jjt37ZflPRPSi771+Y4bitA3yrprHqVm+rQ6rYOr9th+545ztm01AqPiL4kiI+IGI3/VU2JDCBpJW2pJgvE/vUAHsD2b6vDpf36AHCbpLMpn2oAHEU5KLq6w/Pbexm0Led+YBZiFaKIGK4E8RERo/EJ4EfASyXdROkeutBKC+47zb09+xh3O4Dth4GjJb0ROLS69wPb6zu9yPbpPY7/OWCgQXxERL9ysDUiYkQkvYDSsVUswI6tkr4JbLB9Xe36OcDJttd2ed0y4P3AcZRPKn4OXGv7vwNecmv+X9s+YhhzRUTMVYL4iIgRkLTe9okzXZvPJO0P3E7pgtue7rI7sNr2X7u87tvADuAb1aV3AvvaXjPYFT87fyrHRMTYSzpNRMQQVbvMewH7SXo+E1VKlgMHjGxhA2D7b8Cxkk4ADqsuf9/2hhleepjtVW3fb5S0dSCLjIiYpxLER0QM13soTYheQtmdbgXxjwNfGtWiBsn2RmC6bqx1WyQdY/sXAJKOBn41kMV1tn2Ic0VEzEnSaSIiRkDSebavGfU6xpGkPwCHAH+uLq0A7geeptSGf3Wf44805z4iogkJ4iMiRkTSscCBtH0qWq95vhhJetl0920/1Of4I825j4hoQoL4iIgRkHQjsBK4D9hVXbbt80e3qvEh6Ugmdsrvtb2lwbG31nLuO16LiBhnyYmPiBiNo4BVzk7KFJIuBdYAt1WX1km61fZnGppi1Dn3ERF9y058RMQISLoVON/2o6Ney7iRdD9weCtHvepse5/tQxoaf6A59xERw5Cd+IiI0dgP2CppE7CzddH2qaNb0th4BFgGtA6a7gE83OD4pzQ4VkTESGQnPiJiBCS9odN12/cMey3jRtIdwGuBu6pLJwGbgL8ANHFuYJA59xERw5AgPiIixoqk91E+KTYlxeXJ9vu2v97n+PWc+7cBTebcR0QMXIL4iIghkrSDEpxOuUXJx14+5CWNDUlLgcuAs4GHKL+TFcA64GLbTzU0z0Bz7iMihiE58RERQ2R7n1GvYYxdAewDHGR7B4Ck5cCV1b0LGppn0Dn3EREDl534iIgYC5IeAF5ZL7spaQmwzfbBDc0z8Jz7iIhBy058RESMC3eqm297l6Qmd5zuBNYzkXO/scGxIyKGIkF8RESMi62SzrJ9Q/tFSWcC2/odfFg59xERw5B0moiIGAuSDqBUjHkS2FxdPgrYE1htu6+8dUlXUXLuL+yQc/+E7aZy7iMiBi5BfEREjBVJbwQOrb7dant9Q+MOJec+ImIYkk4TERFjxfYGYMNghh5Kzn1ExMDtNuoFREREDMlWSWfVLzaVcx8RMUxJp4mIiEVh0Dn3ERHDlCA+IiIWlUHl3EdEDFOC+IiIiIiIeSY58RERERER80yC+IiIiIiIeSZBfERERETEPJMgPiIiIiJinkkQHxERERExz/wfm1Gr25/E/VkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eeRm_1KnfgLJ" + }, + "source": [ + "### Display only positive correlation." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 659 + }, + "id": "tQfukbI5aSQo", + "outputId": "2dcf4ff5-1151-49aa-f242-8747db88fbd2" + }, + "source": [ + "klib.corr_plot(df_cleaned, split='pos', annot=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Displaying positive correlations. Use \"threshold\" to further limit the results.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x8--KKRqfokd" + }, + "source": [ + "### Display only negative correlation." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "id": "-FJsZveUdrEB", + "outputId": "c5257101-9f89-459a-a7a9-09839b397fcd" + }, + "source": [ + "klib.corr_plot(df_cleaned, split='neg', annot=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Displaying negative correlations. Use \"threshold\" to further limit the results.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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Unnamed: 0 qtr down TimeUnder PlayTimeDiff yrdln ydstogo ydsnet GoalToGo FirstDown Yards.Gained sp Touchdown Safety Onsidekick PassAttempt AirYards YardsAfterCatch QBHit InterceptionThrown RushAttempt Reception Fumble Sack Challenge.Replay Opp_Field_Goal_Prob Opp_Safety_Prob Opp_Touchdown_Prob Field_Goal_Prob Safety_Prob Touchdown_Prob ExPoint_Prob TwoPoint_Prob ExpPts EPA airEPA yacEPA Home_WP_pre Away_WP_pre Home_WP_post Away_WP_post Win_Prob WPA airWPA yacWPA
Unnamed: 01.00-0.00-0.00-0.00-0.01-0.00-0.000.000.00-0.000.000.000.000.00-0.00-0.00-0.00-0.00-0.00-0.000.00-0.000.00-0.00-0.00-0.00-0.00-0.00-0.00-0.00-0.000.00-0.000.000.00-0.000.00-0.000.00-0.000.00-0.000.000.000.00
qtr-0.001.000.01-0.03-0.060.00-0.010.010.01-0.02-0.020.000.000.000.040.010.02-0.010.010.01-0.04-0.000.000.010.01-0.16-0.10-0.17-0.07-0.18-0.11-0.010.04-0.00-0.000.02-0.020.04-0.030.04-0.03-0.03-0.010.04-0.04
down-0.000.011.00-0.020.020.01-0.24-0.01-0.010.28-0.050.17-0.010.000.00-0.06-0.03-0.040.020.01-0.30-0.060.000.020.020.250.070.220.170.22-0.45---0.330.010.15-0.110.00-0.000.00-0.00-0.030.070.10-0.08
TimeUnder-0.00-0.03-0.021.000.100.020.09-0.07-0.020.090.07-0.02-0.01-0.00-0.020.020.000.020.00-0.010.110.020.010.00-0.010.380.210.410.170.500.34-0.01-0.010.04-0.01-0.020.02-0.010.01-0.010.010.020.03-0.030.03
PlayTimeDiff-0.01-0.060.020.101.000.080.060.150.040.03-0.02-0.070.04-0.00-0.040.100.050.040.030.000.110.070.010.020.00-0.01-0.110.000.130.180.28-0.12-0.030.21-0.01-0.01-0.000.00-0.000.00-0.000.10-0.01-0.010.01
yrdln-0.000.000.010.020.081.000.08-0.19-0.410.150.09-0.34-0.19-0.030.020.050.080.050.030.01-0.020.050.010.02-0.020.05-0.300.050.050.250.07-0.28-0.080.01-0.04-0.110.04-0.010.01-0.000.00-0.02-0.03-0.100.04
ydstogo-0.00-0.01-0.240.090.060.081.00-0.11-0.09-0.030.05-0.17-0.040.02-0.050.270.160.120.080.040.120.200.020.060.010.260.190.250.240.310.16-0.24-0.060.01-0.04-0.110.06-0.00-0.00-0.00-0.00-0.02-0.00-0.070.05
ydsnet0.000.01-0.01-0.070.15-0.19-0.111.000.300.030.120.360.24-0.02-0.030.110.080.15-0.04-0.020.010.15-0.02-0.080.04-0.53-0.47-0.520.33-0.210.230.250.060.560.220.140.150.000.000.000.000.080.160.110.11
GoalToGo0.000.01-0.01-0.020.04-0.41-0.090.301.00-0.11-0.090.240.32-0.00-0.010.01-0.06-0.05-0.01-0.000.06-0.02-0.00-0.010.04-0.26-0.16-0.260.16-0.090.36-0.040.000.43-0.000.08-0.070.00-0.000.00-0.000.080.000.07-0.06
FirstDown-0.00-0.020.280.090.030.15-0.030.03-0.111.000.37-0.19-0.11-0.01-0.010.090.080.20-0.070.14-0.140.220.06-0.070.030.270.100.25-0.000.270.01-0.11-0.03-0.140.270.120.20-0.000.00-0.000.00-0.080.280.090.18
Yards.Gained0.00-0.02-0.050.07-0.020.090.050.12-0.090.371.000.040.21-0.02-0.020.190.250.49-0.13-0.05-0.050.41-0.04-0.210.040.170.120.17-0.000.140.08-0.09-0.02-0.050.530.160.48-0.000.00-0.000.00-0.050.430.110.39
sp0.000.000.17-0.02-0.07-0.34-0.170.360.24-0.190.041.000.610.07-0.01-0.09-0.010.03-0.040.01-0.10-0.00-0.01-0.040.04-0.27-0.17-0.270.19-0.28-0.150.550.060.150.200.170.130.00-0.010.00-0.010.090.170.130.11
Touchdown0.000.00-0.01-0.010.04-0.19-0.040.240.32-0.110.210.611.00-0.00-0.010.080.090.12-0.010.040.010.140.02-0.020.12-0.13-0.09-0.130.080.010.22-0.03-0.010.240.250.170.120.00-0.000.00-0.010.040.220.130.11
Safety0.000.000.00-0.00-0.00-0.030.02-0.02-0.00-0.01-0.020.07-0.001.00-0.00-0.01-0.01-0.010.030.010.00-0.010.040.050.030.030.070.03-0.020.00-0.01-0.00-0.00-0.03-0.01-0.00-0.010.00-0.00-0.000.00-0.01-0.01-0.00-0.01
Onsidekick-0.000.040.00-0.02-0.040.02-0.05-0.03-0.01-0.01-0.02-0.01-0.01-0.001.00-0.03-0.02-0.01-0.01-0.00-0.02-0.02-0.00-0.010.01-0.000.01-0.01-0.02-0.02-0.02-0.01-0.00-0.020.030.01-0.010.00-0.00-0.000.000.05-0.040.00-0.00
PassAttempt-0.000.01-0.060.020.100.050.270.110.010.090.19-0.090.08-0.01-0.031.000.520.360.070.13-0.540.69-0.04-0.140.060.040.010.040.130.140.18-0.130.020.120.08---0.000.00-0.000.00-0.130.06--
AirYards-0.000.02-0.030.000.050.080.160.08-0.060.080.25-0.010.09-0.01-0.020.521.000.060.070.17-0.280.22-0.03-0.070.060.040.010.040.060.080.06-0.07-0.020.030.100.77-0.470.00-0.000.00-0.00-0.090.080.53-0.37
YardsAfterCatch-0.00-0.01-0.040.020.040.050.120.15-0.050.200.490.030.12-0.01-0.010.360.061.00-0.01-0.03-0.190.52-0.01-0.050.010.060.030.060.020.070.05-0.05-0.010.000.35-0.220.540.00-0.000.00-0.00-0.030.28-0.160.43
QBHit-0.000.010.020.000.030.030.08-0.04-0.01-0.07-0.13-0.04-0.010.03-0.010.070.07-0.011.000.03-0.15-0.020.020.590.010.030.010.030.040.050.03-0.04-0.010.01-0.160.03-0.090.00-0.000.00-0.00-0.06-0.130.02-0.07
InterceptionThrown-0.000.010.01-0.010.000.010.04-0.02-0.000.14-0.050.010.040.01-0.000.130.17-0.030.031.00-0.07-0.06-0.00-0.020.070.000.000.000.020.010.01-0.02-0.000.01-0.330.10-0.450.00-0.000.00-0.00-0.04-0.220.07-0.30
RushAttempt0.00-0.04-0.300.110.11-0.020.120.010.06-0.14-0.05-0.100.010.00-0.02-0.54-0.28-0.19-0.15-0.071.00-0.370.02-0.11-0.020.000.000.010.090.130.35-0.10-0.000.25-0.04-0.010.01-0.000.00-0.000.000.16-0.04-0.010.00
Reception-0.00-0.00-0.060.020.070.050.200.15-0.020.220.41-0.000.14-0.01-0.020.690.220.52-0.02-0.06-0.371.00-0.01-0.100.030.050.020.050.070.100.12-0.090.010.060.36-0.250.67-0.000.00-0.000.00-0.080.28-0.180.54
Fumble0.000.000.000.010.010.010.02-0.02-0.000.06-0.04-0.010.020.04-0.00-0.04-0.03-0.010.02-0.000.02-0.011.000.190.140.020.010.020.010.030.02-0.02-0.000.01-0.21-0.05-0.060.00-0.00-0.000.00-0.02-0.14-0.04-0.04
Sack-0.000.010.020.000.020.020.06-0.08-0.01-0.07-0.21-0.04-0.020.05-0.01-0.14-0.07-0.050.59-0.02-0.11-0.100.191.000.020.030.010.030.020.040.02-0.03-0.000.00-0.21-0.030.000.00-0.000.00-0.00-0.04-0.16-0.020.00
Challenge.Replay-0.000.010.02-0.010.00-0.020.010.040.040.030.040.040.120.030.010.060.060.010.010.07-0.020.030.140.021.00-0.02-0.01-0.020.020.010.03-0.010.020.03-0.010.03-0.03-0.000.00-0.000.00-0.010.000.04-0.02
Opp_Field_Goal_Prob-0.00-0.160.250.38-0.010.050.26-0.53-0.260.270.17-0.27-0.130.03-0.000.040.040.060.030.000.000.050.020.03-0.021.000.801.00-0.180.700.01-0.21-0.05-0.630.03-0.010.03-0.010.01-0.010.01-0.170.07-0.010.04
Opp_Safety_Prob-0.00-0.100.070.21-0.11-0.300.19-0.47-0.160.100.12-0.17-0.090.070.010.010.010.030.010.000.000.020.010.01-0.010.801.000.79-0.300.33-0.17-0.12-0.03-0.650.030.010.03-0.010.00-0.010.00-0.150.050.010.02
Opp_Touchdown_Prob-0.00-0.170.220.410.000.050.25-0.52-0.260.250.17-0.27-0.130.03-0.010.040.040.060.030.000.010.050.020.03-0.021.000.791.00-0.160.730.04-0.20-0.05-0.600.03-0.010.03-0.010.01-0.010.01-0.160.07-0.010.04
Field_Goal_Prob-0.00-0.070.170.170.130.050.240.330.16-0.00-0.000.190.08-0.02-0.020.130.060.020.040.020.090.070.010.020.02-0.18-0.30-0.161.000.190.38-0.24-0.060.60-0.040.07-0.07-0.010.00-0.010.000.10-0.020.05-0.05
Safety_Prob-0.00-0.180.220.500.180.250.31-0.21-0.090.270.14-0.280.010.00-0.020.140.080.070.050.010.130.100.030.040.010.700.330.730.191.000.49-0.28-0.07-0.05-0.010.01-0.01-0.020.01-0.020.01-0.060.05-0.020.02
Touchdown_Prob-0.00-0.11-0.450.340.280.070.160.230.360.010.08-0.150.22-0.01-0.020.180.060.050.030.010.350.120.020.020.030.01-0.170.040.380.491.00-0.28-0.070.73-0.04-0.020.00-0.010.01-0.010.010.10-0.03-0.030.01
ExPoint_Prob0.00-0.01--0.01-0.12-0.28-0.240.25-0.04-0.11-0.090.55-0.03-0.00-0.01-0.13-0.07-0.05-0.04-0.02-0.10-0.09-0.02-0.03-0.01-0.21-0.12-0.20-0.24-0.28-0.281.00-0.01-0.05-0.00-0.000.000.01-0.010.01-0.010.09-0.01-0.000.00
TwoPoint_Prob-0.000.04--0.01-0.03-0.08-0.060.060.00-0.03-0.020.06-0.01-0.00-0.000.02-0.02-0.01-0.01-0.00-0.000.01-0.00-0.000.02-0.05-0.03-0.05-0.06-0.07-0.07-0.011.00-0.010.000.010.01-0.010.01-0.000.00-0.01-0.010.000.01
ExpPts0.00-0.00-0.330.040.210.010.010.560.43-0.14-0.050.150.24-0.03-0.020.120.030.000.010.010.250.060.010.000.03-0.63-0.65-0.600.60-0.050.73-0.05-0.011.00-0.050.01-0.03-0.00-0.000.00-0.000.18-0.070.00-0.03
EPA0.00-0.000.01-0.01-0.01-0.04-0.040.22-0.000.270.530.200.25-0.010.030.080.100.35-0.16-0.33-0.040.36-0.21-0.21-0.010.030.030.03-0.04-0.01-0.04-0.000.00-0.051.000.140.720.00-0.000.00-0.00-0.010.750.110.54
airEPA-0.000.020.15-0.02-0.01-0.11-0.110.140.080.120.160.170.17-0.000.01-0.77-0.220.030.10-0.01-0.25-0.05-0.030.03-0.010.01-0.010.070.01-0.02-0.000.010.010.141.00-0.590.01-0.010.01-0.01-0.020.130.70-0.47
yacEPA0.00-0.02-0.110.02-0.000.040.060.15-0.070.200.480.130.12-0.01-0.01--0.470.54-0.09-0.450.010.67-0.060.00-0.030.030.030.03-0.07-0.010.000.000.01-0.030.72-0.591.00-0.000.00-0.000.000.030.57-0.410.77
Home_WP_pre-0.000.040.00-0.010.00-0.01-0.000.000.00-0.00-0.000.000.000.000.00-0.000.000.000.000.00-0.00-0.000.000.00-0.00-0.01-0.01-0.01-0.01-0.02-0.010.01-0.01-0.000.000.01-0.001.00-1.000.99-0.980.00-0.00-0.010.01
Away_WP_pre0.00-0.03-0.000.01-0.000.01-0.000.00-0.000.000.00-0.01-0.00-0.00-0.000.00-0.00-0.00-0.00-0.000.000.00-0.00-0.000.000.010.000.010.000.010.01-0.010.01-0.00-0.00-0.010.00-1.001.00-0.980.99-0.000.000.01-0.01
Home_WP_post-0.000.040.00-0.010.00-0.00-0.000.000.00-0.00-0.000.000.00-0.00-0.00-0.000.000.000.000.00-0.00-0.00-0.000.00-0.00-0.01-0.01-0.01-0.01-0.02-0.010.01-0.000.000.000.01-0.000.99-0.981.00-1.000.00-0.00-0.010.00
Away_WP_post0.00-0.03-0.000.01-0.000.00-0.000.00-0.000.000.00-0.01-0.010.000.000.00-0.00-0.00-0.00-0.000.000.000.00-0.000.000.010.000.010.000.010.01-0.010.00-0.00-0.00-0.010.00-0.980.99-1.001.00-0.000.000.01-0.00
Win_Prob-0.00-0.03-0.030.020.10-0.02-0.020.080.08-0.08-0.050.090.04-0.010.05-0.13-0.09-0.03-0.06-0.040.16-0.08-0.02-0.04-0.01-0.17-0.15-0.160.10-0.060.100.09-0.010.18-0.01-0.020.030.00-0.000.00-0.001.00-0.03-0.030.02
WPA0.00-0.010.070.03-0.01-0.03-0.000.160.000.280.430.170.22-0.01-0.040.060.080.28-0.13-0.22-0.040.28-0.14-0.160.000.070.050.07-0.020.05-0.03-0.01-0.01-0.070.750.130.57-0.000.00-0.000.00-0.031.000.190.62
airWPA0.000.040.10-0.03-0.01-0.10-0.070.110.070.090.110.130.13-0.000.00-0.53-0.160.020.07-0.01-0.18-0.04-0.020.04-0.010.01-0.010.05-0.02-0.03-0.000.000.000.110.70-0.41-0.010.01-0.010.01-0.030.191.00-0.66
yacWPA0.00-0.04-0.080.030.010.040.050.11-0.060.180.390.110.11-0.01-0.00--0.370.43-0.07-0.300.000.54-0.040.00-0.020.040.020.04-0.050.020.010.000.01-0.030.54-0.470.770.01-0.010.00-0.000.020.62-0.661.00
" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c3r_lP35up7x" + }, + "source": [ + "# **Distribution Plot**\n", + "\n", + "Displays the distribution plot for columns of interest." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "id": "7Wsj4IuRuqDs", + "outputId": "0bb23acf-0584-4d68-d49a-3711083b7628" + }, + "source": [ + "klib.dist_plot(df_cleaned['Win_Prob'])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Dropped 11346 missing values from column Win_Prob.\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2055: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n", + " warnings.warn(msg, FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SxmfR9IT6YGc" + }, + "source": [ + "# **Categorical Plot**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 229 + }, + "id": "OibP0TQN3LlS", + "outputId": "44d3f7e4-c0ec-4f16-f8a9-a3dc7925ca35" + }, + "source": [ + "klib.cat_plot(df, figsize=(50,15))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSpec(6, 21)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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JkiRJkiRJkiRJktQVJjNLkiRJkiRJkiRJkiRJkiRJ6gqTmSVJkiRJkiRJkiRJkiRJkiR1hcnMkiRJkiRJkiRJkiRJkiRJkrrCZGZJkiRJkiRJkiRJkiRJkiRJXWEysyRJkiRJkiRJkiRJkiRJkqSuMJlZkiRJkiRJkiRJkiRJkiRJUleYzCxJkiRJkiRJkiRJkiRJkiSpK0xmliRJkiRJkiRJkiRJkiRJktQVJjNLkiRJkiRJkiRJ60lEbBMRt0TEAxFxf0ScWOJVRMyPiIfK87gSj4i4MCKWRMQ9EfHetn1NL+UfiojpbfFdI+Less2FERFDf6aSuiEi/rx8t9wXEZdHxKYRMTEiFpXvhCsiYuNSdpOyvKSs37a7tZckSZKkhsnMkiRJkiRJkiRJ0vqzAvhUZu4ATAaOj4gdgFOBmzJzEnBTWQbYD5hUHjOAi6BJfgZOB3YHdgNObyVAlzLHtm03dQjOS1KXRcTWwJ8B78vMHYENgcOA84DzM3M7YDlwTNnkGGB5iZ9fykmSJElS15nMLEmSJEmSJEnSOhIRsyPiqYi4ry22zkZfldR7MvPxzLyzvP4FsBjYGpgGzC3F5gIHldfTgEuzsRAYGxHjgX2B+ZlZZ+ZyYD4wtazbPDMXZmYCl7btS9LINwbYLCLGAG8AHgf2Aq4q6/t+v7S+d64CpjiSuyRJkqThwGRmSZLUM9Z3g7DTcUqSJAkgIsZGxFUR8a8RsTgi/sDfnZIGYQ6vHxF1XY6+KqmHRcS2wHuARcBWmfl4WfUEsFV5vTXwWNtmS0tsoPjSDnFJI1xmLgO+APw7TRLzs8AdwDOZuaIUa/9OWPk9UtY/C2zZd78RMSMibi+PGev3LCRJkiTJZGZJktRb5rB+G4SdjlOSJEkAFwDfy8x3ATvTjJ7o785RLCI2jYhbI+JfIuL+iPjbEp8YEYtKYvoVEbFxiW9SlpeU9du27eu0En8wIvZti08tsSURcWrfOqh3ZOaPgLpPeJ2Mvrr+ay9pfYqINwHfAk7KzOfa15URlXM9H9/kRGmEKdcY04CJwNuAN7IOfjNk5szMfF95zFzb/UmSJEnSqpjMLEmSesb6bBB2Ok5JkiQBRMQWwPuBWQCZ+VJmPoO/O0e7F4G9MnNnYBea93IycB5wfmZuBywHjinljwGWl/j5pRwRsQNwGPBumiSTr0XEhhGxIfBVmuT4HYDDS1mNHOtq9NXXMTlR6g0RsRFNIvNlmXl1CT9ZfhtQnp8q8WXANm2bTyixgeITOsRfw+REaUT6IPCzzPx5Zv4GuBrYg+a6ZEwp0/6dsPJ7pKzfAnh6aKssSZIkSa83ZtVFRraPf/dba9zL/bIDPuIUoCPUuvpcrOl+/GwNT35fqBM/F8PCkE/HWRqHWw3EM/s2/qzN56Lb/FyuP35fqBM/F+rEz4U66dXfFz36mZwI/Bz4RkTsTDNF84n4u3OlHn1f10pJPP9lWdyoPBLYC/hYic8FzqAZeXtaeQ1wFfCViIgSn5eZLwI/i4glNCN3AyzJzIcBImJeKfvAYOvq52L4y8yMiHX2PpXvhgGTEr1Pqb78zTm0yv8Bs4DFmfmltlXXAtOBc8vzNW3xE8r/B7sDz2bm4xFxI/C5ttke9gFOy8w6Ip4rHW0WAUcCfz/YenbzN6efq+HLz8Ww9+/A5Ih4A/BrYApwO3ALcDAwj9d/v0wHflLW31x+6w6K/4+oEz8X6sRrVHXi94U68XOhTvxcjC6jPplZGu78Uh551kXjkZ8LqbN13SA8wHFW2VAsSZKknjUGeC/wycxcFBEXAKe2F/B35+hURk++A9iOZhTlnwLPZOaKUqQ9MX1lMntmroiIZ4EtS3xh227bt+mb/L57P/UYMMldw9aTETG+JCOu7uire/aJLxiCekpaP/YAjgDujYi7S+wzNEnMV0bEMcCjwCFl3fXA/sAS4HngaICStHwWcFspd2ZmtmYxOw6YA2wG3FAePe3B62Z0Jflp+wNneg9dPaNcs1wF3AmsAO6iuYb4LjAvIs4usVllk1nAN0unuppm1hBJkqTXKPfBbgeWZeaBETGRppPUljT3x47IzJciYhOamed2pZnt4dDMfKRL1ZbU40xmliRJvW5dNQiv1nSckiRJGvGWAkszc1FZvoommdnfnaNcZr4M7BIRY4FvA+/qUj1Mcu9N62T01SGus6R1JDP/GegvQXZKh/IJHN/PvmYDszvEbwd2XItqSupRmXk6cHqf8MO8OgNIe9kXgI8ORb0kSVJPOxFYDGxels8Dzs/MeRHxdeAYmtnJjgGWZ+Z2EXFYKXdoNyosqfdt0O0KSJIkraVWgzC8vkH4yGhMpjQIAzcC+0TEuNIovA9wY1n3XERMLlN/Htm2L0mSJI0SmfkE8FhEbF9CU4AH8Henisx8hmba7j8AxkZEa8CI9sT0lUnuZf0WNKPTDJT83imuHhQRl9NM3b59RCwtI66eC+wdEQ8BHyzL0Iy++jDN6KsX04ysShlptTX66m28dvRVSZIkSZKk9SIiJgAHAJeU5QD2ohn0AWAucFB5Pa0sU9ZPKeUladBMZpYkST1jCBqEj6O5KFtCM2V0z0/HKUmjTUTMjoinIuK+tlgVEfMj4qHyPK7EIyIujIglEXFPRLy3bZvppfxDETG9Lb5rRNxbtrmwdVOuv2NI6lmfBC6LiHuAXYDP4e/OUS0i3lpGZCYiNgP2phmd5hbg4FKsb5J76/+Pg4Gbyyib1wKHRcQmZXrOScCtNJ+RSRExMSI2ppnu+9r1f2ZaHzLz8Mwcn5kbZeaEzJyVmU9n5pTMnJSZH2x9H2Tj+Mx8R2b+XhlZtbWf2Zm5XXl8o3tnJEmSJEmSRpEvA58GXinLWwLPZOaKsrwU2Lq83hp4DKCsf7aUf52ImBERt5fHjPVVeUm9a8yqi0iSJA0PmXl4P6ucjlOS1DIH+ApwaVvsVOCmzDw3Ik4ty6cA+9EkkU2imdb9ImD3iKhopmd9H5DAHRFxbWYuL2WOBRbRJDBOpUlC7O8YknpQZt5N8x3Ql787R6/xwNyI2JBmgIgrM/O6iHgAmBcRZwN3AbNK+VnANyNiCVDTJCeTmfdHxJU0o32vAI7PzJcBIuIEmhG9NwRmZ+b9Q3d6kiRJkiRJGu0i4kDgqcy8IyL2XJf7zsyZwMx1uU9JI4vJzJIkSZKkESMzfxQR2/YJTwP2LK/nAgtoEo2nAZeWRMSFETE2IsaXsvNbIyZGxHxgakQsADbPzIUlfinNVGo3DHAMSdIIkJn3AO/pEH8Y2K1D/AXgo/3s6xzgnA7x62k6ykjD0se/+61c020vO+AjTjErSZIkSdLwtwfwoYjYH9gU2By4ABgbEWPK6MsTgGWl/DJgG2BpRIwBtgCeHvpqSxoJTGaWJEmSJI10W2Xm4+X1E8BW5fXK6c+K1tRoA8WXdogPdIzXKdOntaZQm1lGI5AkSZIkSavw4HUz1rhzzdrY/sCZdsyRJEkjXmaeBpwGUEZm/ovM/HhE/BNwMDAPmA5cUza5tiz/pKy/uQwgI0mDZjKzJEmSJGnUyMyMiPV6I21Vx3AqNUmSJEmSJEmS1pyzBw25U4B5EXE2cBcwq8RnAd+MiCVADRzWpfpJGgFMZpYk9ZSImA0cCDyVmTuW2BnAscDPS7HPlOl5iYjTgGOAl4E/y8wbS3wqzXQoGwKXZOa5Q3kekiStK96wWy1PRsT4zHw8IsYDT5V4a/qzltbUaMuAPfvEF5T4hA7lBzqGJEmSJEmStEr9tIFVwBXAtsAjwCGZuTwigqada3/geeCozLyzbDMd+GzZ7dmZObfEdwXmAJsB1wMnlk75HY+xnk9XkjTMZeYCmrYRMvNhYLcOZV4APjqkFZM0YpnMLEnqNXOArwCX9omfn5lfaA9ExA40Pf/eDbwN+EFEvLOs/iqwN8308LdFxLWZ+cD6rLgkScPZmiZF90hCdGuas3N5/fRnJ0TEPGB34NmSjHwj8LmIGFfK7QOclpl1RDwXEZOBRcCRwN+v4hjqARGxIXA7sCwzD4yIiTTT5W0J3AEckZkvRcQmNL9DdwWeBg7NzEfKPuxEJ0mSJEnSIIzw+1FrYg6vbwM7FbgpM8+NiFPL8inAfsCk8tgduAjYvSQmnw68D0jgjtIGtryUOZbmvtb1wFTghgGOIUmSJA0Zk5klST0lM38UEduuZvFpwLzMfBH4WZnapNVbcEnpPUhJYJoGmMwsSVKPi4jLaUZVfktELKVpvDkXuDIijgEeBQ4pxa+nGb1mCc0INkcDlKTls4DbSrkzM7Mur4/j1RFsbigPBjiGesOJwGJg87J8Hk1nuXkR8XWaJOWLyvPyzNwuIg4r5Q61E50kSavHWUUkSZL6108b2DRenUFsLs0ImaeU+KWZmcDCiBhbZgvbE5jfupcVEfOBqRGxANg8MxeW+KXAQTT3tvo7hiRJPWVd3Xeww5XUHSYzS5JGihMi4kiaEfU+VXqYbw0sbCuztMQAHusT373TTiNiBjCjLM7MzJnrtNaSJGmdyszD+1k1pUPZBI7vZz+zgdkd4rcDO3aIP93pGBr+ImICcABwDnBymaZ1L+Bjpchc4AyaZOZp5TXAVcBXSnk70UlSBw9eN2ONG5DWxvYHzrThSJIkSSPFVpn5eHn9BLBVeb01r2/r2noV8aUd4gMd4zVsM5MkSdL6ZDKzJGkkuAg4i2a6rLOALwKfWBc7LjdivBkjSZI0cn0Z+DTw5rK8JfBMZq4oy+2NeysbBDNzRUQ8W8rbiU6SJEmSJK1XmZkRsV47DA50DNvMJI1EjsArScOHycySpJ6XmU+2XkfExcB1ZXEZsE1b0QklxgBxST0mIsYCl9CMlJo0nRkeBK4AtgUeAQ7JzOVl9MwLgP2B54GjMvPOsp/pwGfLbs/OzLklviswB9gMuB44sYzmKknqcRFxIPBUZt4REXt2sy42CEqSJElSb+nWLBTgTBSjzJMRMT4zH4+I8cBTJd5fG9gyYM8+8QUlPqFD+YGOIUmSBEBEzAZabSo7lljFINvkpYFs0O0KSJK0tsqNlZYPA/eV19cCh0XEJhExEZgE3ArcBkyKiIkRsTFwWCkrqTddAHwvM98F7AwsBk4FbsrMScBNZRlgP5rvgkk0o19eBCsvtE6nGS1zN+D0iBhXtrkIOLZtu6lDcE6SpKGxB/ChiHgEmAfsRfP/ytiIaHUAb2/cW9lQWNZvATzNwA2IdqKTJEmSJElr6lpgenk9HbimLX5kNCYDz2bm48CNwD4RMa7c494HuLGsey4iJpcEoyP77KvTMSSNcBGxTUTcEhEPRMT9EXFiiVcRMT8iHirP40o8IuLCiFgSEfdExHu7ewaShtAcXt9OPqg2eWlVTGaWJPWUiLgc+AmwfUQsjYhjgM9HxL0RcQ/wAeDPATLzfuBK4AHge8DxmflymTL8BJobOouBK0tZST0mIrYA3g/MAsjMlzLzGWAaMLcUmwscVF5PAy7NxkKaZLXxwL7A/MysM3M5MB+YWtZtnpkLy2jMl7btS5LU4zLztMyckJnb0nRwuzkzPw7cAhxcivVtKGw17h1cyid2opMkSZIkSWupnzawc4G9I+Ih4INlGZpZBB8GlgAXA8cBZGYNnEVzT+I24MwSo5S5pGzzU+CGEu/vGJJGvhXApzJzB2AycHxE7IAJipL6yMwfAXWf8GDb5KUBjVl1EUmSho/MPLxDeNYA5c8BzukQv57mRo+k3jYR+DnwjYjYGbgDOBHYqow0AfAEsFV5vTXwWNv2S0tsoPjSDvHXiYgZNDduAGZm5sw1PCdJUvedAsyLiLOBu3j19+Ys4JsRsYTmpt1h0HSii4hWJ7oVlE50ABHR6kS3ITDbTnSSJEmSJKmTftrAAKZ0KJvA8f3sZzYwu0P8dmDHDvGnOx1D0shX2tIeL69/ERGLadrBpgF7lmJzgQU090xXJigCCyNibESMb2uTkzS6DLZN/nXfFbaxq53JzJIkSeplY4D3Ap/MzEURcQGv9g4Hmpu6EZHruyLlwsqLK0nqUZm5gOamPJn5MLBbhzIvAB/tZ3s70UmSJEmSJEnqSRGxLfAeYBFrmaBocqI0+qxpm7xt7Gq3QbcrIEmSJK2FpcDSzFxUlvid2V0AACAASURBVK+iSW5+sjVVTXl+qqxfBmzTtv2EEhsoPqFDXJIkSZIkSZIkSep5EfEm4FvASZn5XPu6MgrzoBIUM3NmZr6vPExSlEauwbbJSwMymVmSJEk9KzOfAB6LiO1LaArwAHAtML3EpgPXlNfXAkdGYzLwbOlZfiOwT0SMi4hxwD7AjWXdcxExOSICOLJtX5IkSZKkUSoiZkfEUxFxX1usioj5EfFQeR5X4hERF0bEkoi4JyLe27bN9FL+oYiY3hbfNSLuLdtcWK5J+z2GJEmSJK2JiNiIJpH5ssy8uoRNUJS0OgbbJi8NyGRmSZIk9bpPApdFxD3ALsDngHOBvSPiIeCDZRngeuBhYAlwMXAcQGbWwFnAbeVxZolRylxStvkpcMMQnJMkSZIkaXibA0ztEzsVuCkzJwE3lWWA/YBJ5TEDuAiaxGTgdGB3YDfg9Lbk5IuAY9u2m7qKY0iSJEnSoJROk7OAxZn5pbZVJihKeo2IuBz4CbB9RCyNiGMYZJu8tCpjul0BSZIkaW1k5t3A+zqsmtKhbALH97Of2cDsDvHbgR3XspqSJEmSpBEkM38UEdv2CU8D9iyv5wILgFNK/NJyTbowIsaW0c32BOa3OtNGxHxgakQsADbPzIUlfilwEE3n2v6OIUkapSJiLM1gDDsCCXwCeBC4AtgWeAQ4JDOXl6S1C4D9geeBozLzzi5UW9J6FBEbArcDyzLzwIiYCMwDtgTuAI7IzJciYhPgUmBX4Gng0Mx8pOzjNOAY4GXgzzLzxqE/Ew2BPYAjgHsj4u4S+wxNQuKVJVnxUeCQsu56mv9DltD8P3L00FZXUrdk5uH9rBpUm3w3fPy738o13fayAz4S67IuGpgjM0uSJEmSJEmSJK29rdpGJXsC2Kq83hp4rK3c0hIbKL60Q3ygY7xGRMyIiNvLY8Yano/WkYiYHRFPRcR9bbEzImJZRNxdHvu3rTstIpZExIMRsW9bfGqJLYmIU9viEyNiUYlfEREbD93ZSRoGLgC+l5nvAnYGFjPI2QIkjTgn0nwXtJwHnJ+Z2wHLaZKUKc/LS/z8Uo6I2AE4DHg3zQwhXysJ0hphMvOfMzMyc6fM3KU8rs/MpzNzSmZOyswPtjpgZuP4zHxHZv5eGRBIkqR1omsjM9sTTJIkSZIkSZI0WkTE9jQjJLa8HfgbYCxwLPDzEv9MZl5ftvEeeI/KzIyINR75Z22PkZkzgZnr8/galDnAV2jau9qdn5lfaA/0SR56G/CDiHhnWf1VYG+aBPfbIuLazHyAVxOU5kXE12m+N0xQlEaBiNgCeD9wFEBmvgS8FBGDmi2graNMz3GkPem1ImICcABwDnByGZF9L+Bjpchc4Aya3wrTymuAq4CvlPLTgHmZ+SLws4hYAuwG/GSITkOSJI1CXUtm5tWeYJuX5f5utKzsCRYRh5Vyh/Z3MyczXx7qE5EkSd21LhuEI2IqzUgWGwKXZOa5Q3ISkta7iNiGpuF4K5opN2dm5gURUTHIaTcjYjrw2bLrszNz7lCeiyRJknpPZj4I7AIrB/tYBnybZlre1U5o9B74sPZkKyEsIsYDT5X4MmCbtnITSmwZryaateILSnxCh/IDHUPDWGb+KCK2Xc3i/SUPASzJzIcBImIeMC0iFtN/gpKkkW8izf3vb0TEzjSDhp3I4GcLeE0ycxnVvzWy/8zSSUZSb/gy8GngzWV5S+CZzFxRlttn/Vj5nZCZKyLi2VJ+a2Bh2z7bt5EkadSxA93Q6Eoysz3BJEnSurSuGoTL6v5GuJG6zouktbYC+FRm3hkRbwbuiIj5NCPX3JSZ55Zpek+lGammfdrN3WmuT3Yvyc+nA++jSYq+o3xXLB/yM5IkSVKvmgL8NDMfbW53d+Q98N5zLTAdOLc8X9MWP6Ekn+4OPFuSkW8EPhcR40q5fYDTMrOOiOciYjKwCDgS+PtVHEO96YSIOJJmJtNPlevKgZKH+iYg7s7ACUqvYXKiNCKNAd4LfDIzF0XEBTT3tlZak9kCHOFf6k0RcSDwVGbeERF7DtEx/X0hSZLWiW6NzGxPMEmStL6sTYMwdBjhBjCZWRoBymg0j5fXvyijV21N8+98z1JsldNulrLzM7MGKAnRU4HLh+xkJEmS1OsO47W/Hweb0LiSyQPdERGX01wbvCUiltJ0eDwXuDIijgEeBQ4pxa+nmfFlCc2sL0cDlKTls4DbSrkzW9cZwHHAHGAz4IbyYIBjqPdcBJxF00n2LOCLwCfW5wFNTlw7D143Y407ma+N7Q+caQd1DWQpsDQzF5Xlq2iSmQc7W4CkkWEP4EMRsT+wKc1M6RcAYyNiTMnJaf933/pOWBoRY4AtgKcZxHeFvy+kkSciNgV+BGxCk1t4VWaeHhETgXk0eXt3AEdk5ksRsQnNzKi70nyHHJqZj3Sl8pJ62gZDfcD2nmBDdLwZEXF7ecxY9RaShpuI2DAi7oqI68ryxIhYFBFLIuKKiNi4xDcpy0vK+m3b9nFaiT8YEft250wkDZFODcL3RMTstpGO+ptKr7/4a/j7Qup95XfCe2hGORvstJur9V1RjuP3hSRJkl6j3Mv6EPBPJXQR8A6aGYcep0loXG2ZOTMz31ceJhEMkcw8PDPHZ+ZGmTkhM2dl5tOZOSUzJ2XmB1uJydk4PjPfkZm/l5m3t+1ndmZuVx7faIvfnpk7lm1OKJ0s6e8Y6j2Z+WRmvpyZrwAX82pH+/6Sh/qLP01JUOoT1zD3mQt+yB/+j2/y346/amXsmV+8wCf++nr2nXEFn/jr63n2ly8CEI0LSzvHPRHx3tY2ETE9Ih4qj+lt8V0j4t6yzYUxwMgP6l2Z+QTwWERsX0JTaAbnaI3kD6+fLeDI8pmaTJktYCjrLGn9yczTym/TbWnay27OzI8DtwAHl2J9vxNa3xUHl/JZ4oeV9veJNLMX3jpEpyGp+14E9srMnWnuVUwtvxvOo5kVeTtgOXBMKX8MsLzEzy/lJGnQhjyZmVd7gj1C01tjL9p6gpUynXqCsSY9wbyR21tMWlU/TgQWty0P6gdSROxAc7H2bppRE78WERsOUd0lDaF13SDcH39fSL0tIt4EfAs4KTOfa19XbtSus5GW/L6QJElSB/sBd2bmk7BGCY2SRoAyUmrLh4H7yuv+koduAyaVdpONae55X1uuY/tLUNIw9uEp7+TiM/Z7Teziq/6FyTu9jRtnHsrknd7GxVfd3Vq1H81nYRLNaPwXAURERTMy/O40/3+c3jagw0XAsW3bTV2/Z6Qu+iRwWUTcQ3Mv/HM0I/nvHREPAR8sy9DMFvAwzWwBF9PMBCBp5DsFOLnMVLolMKvEZwFblvjJNCO7k5n3A1fSdI74HnB8Zr485LWW1BWlQ+4vy+JG5ZE0OX6tnnhzgYPK62llmbJ+ih3pJK2JIU9mtieYVsGkVb1GREwADgAuKcvB4H8gTQPmZeaLmfkzmhs0rUYhSSPL2jYI21AsjXARsRFNIvNlmXl1CT/ZakRezWk3/a7oQRGxfUTc3fZ4LiJOiogzImJZW3z/tm06dpSMiKkltiQiTm2Ld+yMKUmS1MfhtM0otAYJjZJ6TERcDvwE2D4ilkbEMcDny6i59wAfAP4c+k8eKtPCnwDcSNOOcmUpC/0nKGkY+/0dx7PFmzd5TeymRY9y0JR3AnDQlHfyg4WPtlZNAy4tiSULaQaJGg/sC8zPzDozlwPzaUbOGw9snpkLS7vqpbzalqIRJjPvLh3pd8rMgzJz+ZrMFiBpZMnMBZl5YHn9cGbuVmYD+WhmvljiL5Tl7cr6h9u2P6d8V2yfmTd06zwkdUcZjPJumjaz+cBPgWfKdQm8dtbSlTOalvXP0lyX9N2ns5lKGlA3Rmbujz3BRjmTVtWPLwOfBl4py1sy+B9IqzUVvD+cpBFhbRuEO45wMyQ1l7Teld+Ls4DFmfmltlWDnXbzRmCfiBhXRjvap8Q0jGXmg5m5S2buAuwKPA98u6w+v7UuM6+H/jtKls6SX6XpQLMDcHgpC/13xpQkSQIgIt4I7A1c3RYeVELjEFdZ0jqQmYdn5vjM3KgM+DMrM48oiYQ7ZeaHyvVmq3zH5KHMvD4z31nWndMW75igpN7z9DO/5reqNwDw1nGb8fQzv26t6q+dY6D40g7x17FtRJIkSX2VDpW70AzosxvwrnWwT2czlTSgMd08eGYuABaU1w/TIek0M18APtrP9ucA53Rap57USlp9c1le7aTViGhPWl3Yts9+k1ZppuECmOl/ksNTRBwIPJWZd0TEnuv7eOVz4GdB6lFtDcJ/0hb+fETsQjPtzSOtdZl5f0S0GoRX0NYgHBGtEW42BGa3jXAjqfftARwB3Ft6kwN8hmaazSvLyFiPAoeUddcD+9N0kHseOBogM+uIOIumAwTAma3RbdQzpgA/zcxHB5jpbGVHSeBnpYNt65p1SWuUkoiYB0yLiMU0nTE/VsrMBc6gTP0rSZIEkJm/os/oRJl5xADlvQcuSaNURDAUc3PbNiJJkqT+ZOYzEXEL8Ac0s4OMKXlc7bOWtmY0XRoRY4AtgKe7UmFJPa2rycxSi0mr6scewIfKVN+bApsDFzD4H0hOBS+NAuuqQbiMyHn9Oq+gpK7LzH+GftsBp3Qon8Dx/exrNjB73dVOQ+ww2kbyB06IiCOB24FPlal5B+oo2XfUq90ZuDPma9i5UpIkSZLUyZZjN+Op+nl+q3oDT9XPU43drLWqv3aOZcCefeILSnxCh/KSJEnSgCLircBvSiLzZjQDip0H3AIcDMzj9TOdTgd+UtbfXNrYJGlQNuh2BaSilbT6CM1/envRlrRaynRKWsWk1ZErM08rU+5tS5NwcnNmfpxXfyBB5x9I8NofSNcCh0XEJhExEZgE3DpEpyFJkqRhJCI2Bj4E/FMJXQS8A9gFeBz44vqug1OpDR8RsWlE3BoR/xIR90fE35b4xIhYFBFLIuKK8rmhXFNcUeKLImLbtn2dVuIPRsS+bfGpJbYkIk4d6nPUmouIDSPiroi4riz7uRjFImKbiLglIh4o3xcnlngVEfMj4qHyPK7EIyIuLO/xPRHx3rZ9TS/lH4qI6W3xXSPi3rLNhTHA9AGSJGlk2mu33+E7N/0bAN+56d+YsvvvtFZdCxxZfmNMBp7NzMdpZpjbJyLGld8h+wA3lnXPRcTk8pviSF5tS5HUQyJi+4i4u+3xXEScFBFnRMSytvj+bdsM6lq0v+tdSdKoNR64JSLuoZmldH5mXgecApwczUyWWwKzSvlZwJYlfjLg/U5Ja8RkZg0LJq1qkAb1Aykz7weuBB4Avgccn5kvD3mtJUmSNBzsB9yZmU8CZOaTmflyZr4CXAzsVsoNNOpVp/jT9N8ZU8PXi8BembkzTUL71JIYcB5wfmZuBywHjinljwGWl/j5pRwRsQPNtey7ganA10oi7IbAV2k+dzsAh5ey6g0nAovblv1cjG4raEbv3wGYDBxf3rdTgZsycxJwE6821uxHc19qEs1o/BdBk/wMnE4zqv9uwOmtBOhS5ti27aYOwXlJkqQuOfnvbubwv7yGny17hj866h+56vv/yrEH78yP717GvjOu4Cf/soxjD965Vfx64GFgCc2163EAmVkDZ9EkmdwGnFlilDKXlG1+CtwwZCcnaZ3JzAczc5fM3AXYFXge+HZZfX5rXZl1ck2vRfu73pUkjUKZeU9mviczd8rMHTPzzBJ/ODN3y8ztMvOjmfliib9Qlrcr6x/u7hlI6lVjVl1E6qpTgHkRcTZwF69NWv1mSVqtaS7IyMz7I6KVtLoCk1ZHjMxcQDM1GuWHz24dyrwAfLSf7c8Bzll/NZQkSVKPOBy4vLUQEePLiFUAHwbuK6+vBf4xIr4EvI1XO0oGMKl0nlxGcy3ysczMiOhvijUNU6VT7C/L4kblkTSzBX2sxOcCZ9AkGU4rrwGuAr5SRjmbBswrN29/Vq5VW9csS1o3byNiXin7wPo7K60LETEBOIDmOvLk8j77uRjFyv8Vj5fXv4iIxcDWNO/dnqXYXJp7F6eU+KXle2ZhRIyNiPGl7PxWklFEzKfpSLEA2DwzF5b4pcBBmHQkSdKI9aW/3KtjfM45B7wuVn5THN+pfGbOBmZ3iN8O7LhWlZQ03EwBfpqZjw4wkcugrkXLtU1/17uSJEnSkDGZWcOOSauSJEmS1oeIeCOwN/AnbeHPR8QuNAmsj7TWDdRRMiJOoJnKd0NgdpkJBPrvjKlhrIxMdAewHc0IRT8FnsnMFaXIUpqERcrzYwCZuSIinqWZLWZrYGHbbtu3eaxPfPd+6jGDZvRWgJmZOXMtTqunPHjdjOx2Hfra5w8nMuOjO/OrX/9mr9lX38O5J+35yqF/eQ3fn3nobx68bga3zD6cD3zi8ta/fT8Xo0xEbAu8B1gEbNXWKeYJYKvyeuXnomi9/wPFl3aIdzq+nwtJkiRpdDqMtk76wAkRcSRwO81MMssZ/LXolvR/H+Q1vBaRJEl9RcT2wBVtobcDfwOMpZmF7ucl/pnWTBJSf0xmliRJkiSNCpn5K5oGmvbYEQOU79hRstxsed0Nl/46Y2p4K0nqu0TEWJppWt/VpXrMBGwEHAZuufVRttxiU3bc7q0suvc/uloXPxfDT0S8CfgWcFJmPtc+GloZpX+9J+f7uZAkSZJGn4jYGPgQcFoJXQScRdNB/yzgi8An1mcdvBaRJEl9ZeaDwC6wcvCYZTRtLUcD52fmF7pYPfUYk5klSZIkSdKol5nPRMQtwB8AYyNiTBmVaALNzTfK8zbA0ogYA2wBPN0Wb2nfpr+4hqk7Fz/Jzbf+Oz+843Jeeullfvn8S5xz8Y957pcvsuLlVxiz4QY88fSvwM/FqBMRG9EkMl+WmVeX8JMRMT4zH4+I8cBTJd7f+78M2LNPfEGJT+hQXpIkSZIA9gPuzMwnAVrPABFxMXBdWRzstejT9H8fRJIkaTCmAD/NzEfbB4GQVtcG3a6AJEmSJElSN0TEW8uIzETEZsDewGLgFuDgUmw6cE15fW1Zpqy/OTOzxA+LiE0iYiIwCbgVuA2YFBETywhKh5WyGsY+NX03fjjnY9w863C++Om92H2nt/GFv2ieb/x/PwPgOzf9G/i5GFWiufs+C1icmV9qW9X+/vf9vjgyGpOBZzPzceBGYJ+IGBcR44B9gBvLuuciYnI51pFt+5IkSZKkw4HLWwulM2XLh4H7yutBXYuW69f+7oNIkiQNxmG0/V4BToiIeyJidrkXKg3IkZklSZIkSdJoNR6YW6Y+2wC4MjOvi4gHgHkRcTZwF00CI+X5mxGxBKhpbsyRmfdHxJXAA8AK4PjMfBkgIk6gSV7cEJidmfcP3elpXfqLo3bj5M/fzAX/cDu/+/Ytwc/FaLMHcARwb0Tc/f/Zu/N4uYo67+OfLwQVUCCgIJAoyJJ5AGWVZITHAYEQAQE3FBGiMsR5CAOKM8MyzKBsA24ILoyBBIKyb0MGAiETQWQ0kATZUYkRNRkkI4mA4gb+nj+qOuncnHvT3ffc7tN9v+/Xq1/prnNO3ercumep+lVVTjsdOB+4XtKxwM+BI/K2mcBBwELgJdKyikTEMklnkwIJAM6KiGX5/fHAFcC6wB35ZWZmZmZmw5yk9UkDsD9Zl/x5SbsAATxd29bis+gpFLeDmJmZmTUkD5Y6FDgtJ10CnE26Vzkb+BLwiYLjJgGT8scpETFl6EtrVeVgZjMz6yqSpgGHAEsjYqectjFwHbAVqcHmiIhYnmezuojUgfwS8LGIeDAfMxE4I2d7TkRMb+f3MDMzM7POi4hHgF0L0hcBexak/wH4YD95nQucW5A+kxTUaF1o7Fu3YOxbtwBg9Bs34IYvH75i25hDpvwRXC+Gi4i4D+hvbcT9CvYPYHI/eU0DphWkzwd2GkQxzczMzMysB0XE74BN+qQdPcD+TT2L9tcOYmZmZtaEdwMPRsSzALV/ASRdCtxWdFAOXnYAswFp1iEzM7NucgUwoU/aqcCciNgOmJM/Q7pZ2i6/JpFGftWCn88ExpIaZ870khZmZmZmZmZmZmZmZmZmZmZmZk07Erim9kHS5nXb3gs81vYSWddxMLOZmXWViLiXtHRzvcOA2szK04HD69KvjGQusFG+YToQmB0RyyJiOTCb1QOkzczMzMzMzMzMzMzMzMzMzMysH5LWBw4Abq5L/rykRyU9AuwLfLojhbOuMqLTBegVR91+U7Ry3FUHv7+/5SnNzKxxm0XEM/n9r4DN8vstgV/W7bc4p/WXvhpJk0izOgNMyUtcmFnFSFobmA8siYhDJG0NXEtaem8BcHRE/EnSq4Ergd2B54APRcTTOY/TgGOBV4ATI2JWTp8AXASsDVwWEee39cuZmZmZmZmZmZmZmZmZmZlVUET8jtQvX592dIeKY13MMzObmVlPiYgAWhpg0k9+UyJij/xyILNZdZ0EPFn3+QLgwojYFlhOClIm/7s8p1+Y90PSDsCHgR1JM7V/Q9LaOUj668C7gR2AI/O+ZmZmZmZmZmZmZmZmZmZmZlYCz8xcIa3O7gye4dnMhr1nJW0eEc9I2hxYmtOXAKPr9huV05YA+/RJv6cN5TSzISBpFHAwcC5wsiQB7wI+kneZDnwWuAQ4LL8HuBH4Wt7/MODaiPgj8DNJC4E9834LI2JR/lnX5n2fGOKvZWZmZmZmJfvxbZNKG/zcjDGHTHHbrZmZmZmZmZmZmdkAPDOzmZn1ghnAxPx+InBrXfoxSsYBz0fEM8AsYLykkZJGAuNzmpl1p68A/wT8JX/eBPhNRLycPy8GtszvtwR+CZC3P5/3X5He55j+0lcjaZKk+fk1abBfyszMzMzMzMzMzMzMzMzMzGw48MzMZmbWVSRdQ5pV+fWSFgNnAucD10s6Fvg5cETefSZwELAQeAn4OEBELJN0NjAv73dWRCxr25cws9JIOgRYGhELJO3TybJExBRgSifLYGZmZmZmZmZmZmZmZmZmZtZtHMxsZmZdJSKO7GfTfgX7BjC5n3ymAdNKLJqZdcZewKGSDgJeA2wAXARsJGlEnn15FLAk778EGA0sljQC2BB4ri69pv6Y/tLNzMzMzMzMzMzMzMzMzMzMbJDW6nQBzMzMzMxaFRGnRcSoiNgK+DDwnYg4Crgb+EDebSJwa34/I38mb/9OHvgwA/iwpFdL2hrYDniANIP7dpK2lvSq/DNmtOGrmZmZmZmZmVmPkDRN0lJJj9WlbSxptqSn8r8jc7okXSxpoaRHJO1Wd8zEvP9TkibWpe8u6dF8zMWS1N5vaGadJmltST+UdFv+vLWk+/N54brctklu/7wup98vaatOltvMzMzMzKzGwcxmZmbWEyQ9nTttHpI0P6eV1ilkXecU4GRJC4FNgKk5fSqwSU4/GTgVICIeB64HngDuBCZHxCt5ZucTgFnAk8D1eV8zMzMzM7OmlfXsamZd5wpgQp+0U4E5EbEdMCd/Bng3aZD1dsAk4BJI5wrgTGAssCdwZu18kfc5ru64vj/LzHrfSaT2y5oLgAsjYltgOXBsTj8WWJ7TL8z7mZmZmZmZdZyDmc3MzKyX7BsRu0TEHvlzmZ1CVnERcU9EHJLfL4qIPSNi24j4YET8Maf/IX/eNm9fVHf8uRGxTUSMiYg76tJnRsT2edu57f9mZmZmZmbWYwb17Gpm3Sci7gWW9Uk+DJie308HDq9LvzKSucBGkjYHDgRmR8SyiFgOzAYm5G0bRMTcvPrUlXV5mdkwIGkUcDBwWf4s4F3AjXmXvueY2rnnRmA/z+ZuZmZmZmZVMKLTBTAzMzM46vabopXjrjr4/W5kHNhhwD75/XTgHtKMvSs6hYC5kmqdQvuQO4UAJM0mzWRzTXuLbWZmZmZmZsNIU8+uEfFMR0ppZmXbrO7v+VfAZvn9lsAv6/ZbnNMGSl9ckL4aSZNIgyMApkTElMF8ATOrjK8A/wS8Ln/eBPhNXnUOVj0vrDiXRMTLkp7P+/+6PkOfL8zMzIYvSaNJgyQ3A4J0L3BRnhjsOmAr4GngiIhYngdGXQQcBLwEfCwiHuxE2c2suzmY2czMrEe0GhANPRMUHcBdkgL4Zm5cLatTaBVuyDUzMzMzM7MWlfHsukows59RzbpfREQ+Lwz1z5kC+Bxh1kMkHQIsjYgFkvYpK1+fL8zMzIa1l4HPRMSDkl4HLMiTgH2MtLLU+ZJOJa0sdQqrriw1lrSy1NiOlNzMupqDmc3MzKxX7B0RSyRtCsyW9KP6jWV2Crkh18ysO0l6GngReAV4OSL2aGUmAUkTgTNytudExPScvjtwBbAuMBM4Kc+kaGZmZlZT+rOrn1HNutaztdnW84phS3P6EmB03X6jctoSVs7iXku/J6ePKtjfzIaHvYBDJR0EvAbYgNSesZGkEXl25vrzQu0cs1jSCGBD4Ln2F9vMzMyqKg+4fia/f1HSk6TB1V5ZysyGlIOZzczMrCdExJL871JJtwB7Ul6nkJmZ9Y59I6J+6dRTaWImgRz8fCawB2lmxQWSZkTE8rzPccD9pGDmCcAd7flaZmZm1g1KenY1s94wA5gInJ//vbUu/QRJ15KeRZ7P54dZwHmSRub9xgOnRcQySS9IGkd6FjkG+Go7v4iZdU5EnAacBpBnZv6HiDhK0g3AB4BrWf0cMxH4Qd7+HQ/ENrPh5Me3TerIOW/MIVN6YZVcG4YkbQXsSnrW8MpSZjak1up0AczMzMwGS9L6eYkbJK1P6sx5jJUNs7B6g+0xSsaRO4WAWcB4SSNzx9D4nGZmZr3rMNIMAuR/D69LvzKSuaQZjTYHDgRmR8SyHMA8G5iQt20QEXNzJ+CVdXlZRUkaLeluSU9IelzSSTl9Y0mzJT2V/x2Z0yXpYkkLJT0iabe6vCbm/Z/Ks3fX0neX9Gg+5uI867eZmQ1DJT67mlmXkXQNKXBwjKTFko4lBTEfIOkp4T8RKwAAIABJREFUYP/8GdLAyEXAQuBS4HiAiFgGnA3My6+zchp5n8vyMT/FgyrNLA3UPlnSQmATYGpOnwpsktNPJg3qtoqT9HRuW3hI0vyc5rYLMzMbUpJeC9wEfCoiXqjflvtBml5ZKiL2yC8HMpvZajwzs5mZmfWCzYBbcvvaCODqiLhT0jzg+txB9HPgiLz/TOAgUgfPS8DHIXUKSap1CsGqnUJmZtb9ArgrL93+zdxY1uxMAgOlLy5IX41nH6iUl4HPRMSDObhsgaTZwMfwjN1mZla+Up5dzaz7RMSR/Wzar2DfACb3k880YFpB+nxgp8GU0cy6X0TcQ15pMCIWkVaA6LvPH4APtrVgVhavNmZmZm0jaR1SIPNVEXFzTvbKUmY2pBzMbGZmZl0vN8zuXJD+HCV1CpmZWU/YOyKWSNoUmC3pR/UbIyJyoPOQysHLDmCugBzI/kx+/6KkJ0lB6IcB++TdppM6g0+hbsZuYK6k2ozd+5Bn7AbIAdETJN1DnrE7p9dm7HaHoJnZMFTms6uZmZmZDXtuuzAzsyGRZ+ifCjwZEV+u21RbWep8Vl9Z6gRJ15IG0nhlKTNryVqdLoCZmZmZmZlZO0TEkvzvUuAW0gxFz+YOHRqcSWCg9FEF6dYlJG0F7EqahajtM3abmZmZmZmZmfWjttrYgrziF3RotTFJ8/NrUtE+ZtZdJE2TtFTSY3VpG0uaLemp/O/InC5JF0taKOkRSbt1ruQ2xPYCjgbeJemh/DqIFMR8gKSngP3zZ0iz+i8irSx1KXB8B8psHSTpaUmP5royP6cVnkvMBuJgZjMzMzMzM+t5ktaX9Lrae2A88BgrZxKA1WcSOCY30I5j5UwCs4DxkkbmhpfxwKy87QVJ4/KsBcfU5WUVJ+m1pCXzPhURL9RvyzMZDfmM3e4QNDMzMzMzM7N+7B0RuwHvBiZLemf9xna1XUTElIjYI7+86phZb7gCmNAn7VRgTkRsB8zJnyGdg7bLr0nAJW0qo7VZRNwXEYqIt0XELvk1MyKei4j9ImK7iNi/Ntt/JJMjYpuIeGtEzO/0d7CO2DfXlT3y5/7OJWb9cjCzmZmZmZmZDQebAfdJehh4ALg9Iu6kyZkEcuPc2cC8/Dqr1mCX97ksH/NTvBxnV5C0DimQ+aqIuDknt33GbncImpmZmZmZmVkRrzZmZkMlIu4FlvVJPgyYnt9PBw6vS78yB67OBTaqnYfMzAr0dy4x65eDmc3MzMzMzKznRcSiiNg5v3aMiHNzetMzCUTEtIjYNr8ur0ufHxE75WNOyLPiWIXlWbSnAk9GxJfrNnnGbjMzM2tZM8urDrRUs6SJef+nJE2sS989578wH6v2f0szMzNrB682ZmYdsFk+NwD8ijRRCMCWwC/r9luc01bjVejMhp0A7pK0oO5vvr9zySp8vrB6IzpdADMzMzMzMzOzDtkLOBp4VNJDOe100gzd10s6Fvg5cETeNhM4iDT79kvAxyHN2C2pNmM3rD5j9xXAuqTZuj1jt5mZ2fCwb0T8uu5zbXnV8yWdmj+fwqpLNY8lLdU8VtLGwJnAHqROwQWSZkTE8rzPccD9pPuTCfgew8zMrFdtBtySxy6NAK6OiDslzcNtF1ZA0mjgSlLdCWBKRFyU7y+vA7YCngaOiIjlOYj9IlK9eQn4WEQ8mPOaCJyRsz4nIqZjw0pEhKSmJ+3IK8959Tmz4WPviFgiaVNgtqQf1W8c6Fzi84XVa3sws2+czMzMzMysUyRNAw4BlkbETjnNzyJmw1RE3Af0N5PhfgX7BzC5n7ymAdMK0ucDOw2imGZmZtYbDgP2ye+nA/eQgplXLNUMzJVUW6p5H2B2LchI0mxggqR7gA3yss5IupK0VKuDjszMzHpQRCwCdi5Ifw63XVixl4HPRMSDeVbvBfle8mOUN7jOetuzkjaPiGfys8nSnL4EGF2336icZmbDXEQsyf8ulXQLsCf9n0vM+rVWB35m7cZpB2AcMFnSDqyclWA7YE7+DKveOE0i3ThRd+M0lvQHcGZtWTYzMzMzM7N+XEGatayen0XMzMzMzKxMzSyv2t9SzQOlLy5IX4WXaTUzMzMbniLimdqkHBHxIvAk6X7xMNKgOvK/h+f3KwbX5QFztcF1B5IH1+UA5tms3rZuvWkGMDG/nwjcWpd+jJJxwPN1zzhmNkxJWj8PnkHS+sB44DH6P5eY9avtwcy+cbIikkZLulvSE5Iel3RSTt9Y0mxJT+V/R+Z0SbpY0kJJj0jarS6viXn/p/KMedalXC/MzMysbBFxL7CsT7KfRczMzMzMrEx7R8RupAGSkyW9s35jnjGx6aWamxERUyJij/zycq1mXeZdx17De064kcNPvAlJ88F9I2Zm1jxJWwG7AvdT3uC6vj/Dg+i6mKRrgB8AYyQtlnQscD5wgKSngP3zZ4CZwCJgIXApcHwHimxm1bMZcJ+kh4EHgNsj4k76P5eY9WtEJ394u26cSLOoAUxxo11leakTK+J6YWZmZu0wJM8i4OcRMzMzM7PhqMnlVftbqnkJsE+f9Hty+qiC/c2sx1x57iGM3PA1jDlkyh45qbaylPtGzMxsjSS9FrgJ+FREvCBpxbaICEmlDK7Lbd5u9+5SEXFkP5v2K9g3gMlDWyIz6zYRsQjYuSD9OQrOJWYDafvMzDV9b5zqt5U5K4FnH+gOnrHbirhemJmZWbuVPUOan0fMzLrPM//7W445/TYOPv4GDjn+Bq6c8RgAv3nxD3ziX2Zy4KTr+MS/zGQwM+FJ2l3So/mYi1Xfo2hmZl2theVV+1uqeRYwXtLIfM0ZD8zK216QNC5fP47BS7WaDRfuGzEzs4ZIWocUj3NVRNyck5/N1weaGFxXlG5mZmY2JDoSzOwbJxuIlzqxIq4XZmZmNoT8LGJmZiusvfZanPKJcdz+jQ9y7RcP46rbH2fhL5Zz6Y0PM+5tWzBryocY97YtIM2EB6vOhDeJNBMedTPhjSXNyHlmLQA673Nc3XEOKqk4SdMkLZX0WF1aaUu9O8DdrKc0u7xq4VLNEbEMOBuYl19n5TTyPpflY34K3NGG72VmbSTg2H+dyfs+dUtt1Sdw34iZmTUgP09OBZ6MiC/XbSplcF1bvoSZmZkNS20PZvaNkw3EM3ZbEdcLMzMzG2J+FjEzsxU23Xg9dtz29QC8dr1Xsc3okTz73O+Yc//POXy/7QFq/7Y0E17etkFEzM3PtFfW5WXVdQWrB53XlnrfDpiDA9zNjLS8akTsnF87RsS5Of25iNgvIraLiP1rgcn5+jE5IraJiLdGxPy6vKZFxLb5dXld+vyI2Ckfc0K+nphZD7n684dy80Xv49LPTgCYLOmd9dvdN2JmZgPYCzgaeJekh/LrIModXGdmZmZWuhEd+Jm1G6dHJT2U004n3ShdL+lY4OfAEXnbTOAg0o3TS8DHId04SardOIFvnLreQDN2R8QzTcySt0+f9HuGstw2tFwvzMzMrEySriHdF7xe0mJSQJGfRczMrNDiZ1/kyZ/+mp3HbMpzv/k9m268HgBvGLkutD4T3pb5fd90q7CIuDevGlXvMFa2N0wntTWcQl2AOzBXUi3AfR9ygDuApFqA+z3kAPecXgtw90yrZmZmw9Rmm6wPwCYbrQtwC2kglPtGzMxsjSLiPtIk/0X2K9g/gMn95DUNmFZe6czMzMz61/aZmSPivohQRLwtInbJr5llzkpg3cczdlsR1wtrlqSn87K8D0man9OaXvbXzMx6V0QcGRGbR8Q6ETEqIqb6WcTMzIr87vd/5sR/+y9OO+6vee16r1plW3pcLWcmvIF4ye/KK2up96YC3F0vzMzMettLf/gzv33pTyvek/o0HsN9I2ZmZmZmZtbDOjEzs1kRz9htRVwvrBX7RsSv6z7Xlv09X9Kp+fMprLrs71jSkr5j211YMzMzMzOrnj+//BdO/LfZvGefbRj/jq2BNCve0mUvsenG67F02UvQ+kx4S/L7vvuvJi/z7aW+u0BEhKQhD3DPP8v1wszMrIc995vfc8K5swF45ZW/ANweEXdKmof7RszMzMzMzKxHOZjZKsFLnVgR1wsrSVPL/tbNqmVmXUDSaOBK0ix4AUyJiIskbQxcB2wFPA0cERHL86z/F5E6eF4CPhYRD+a8JgJn5KzPiYjpOX134ApgXVLn0En53GFmZmY9KCI44+Lvss3okXz88LetSH/Xnm/mP+b8hEkf3IX/mPMTWHUmvBMkXUsaIPl8Xvp7FnBebXUY0kx4p+WgkhfyrHn3A8cAX23T17NylbXUe8MB7mZmZtb7Rr9xA2796vtXfB5zyJRzASLiOdw3YmZmZmZmZj1qrU4XwMzMrEQB3CVpQd1Su80u+2tm3eVl4DMRsQMwDpgsaQdWzsq+HTAnf4ZVZ2WfRJqVnRz8fCYpAGlP4My6wKNLgOPqjpvQhu9lZmZmHfLgE89y690LmfvI/3D4iTdx+Ik38d35v+C4D+zM9x9awoGTruMHDy+BtGoQpMFOi0gz4V0KHA9pJjygNhPePFadCe944LJ8zE+BO9r1/axUpSz1nre9IGlcHnx3TF1eZmZmZmZmZmZmZmY9zzMzm5lZL9k7IpZI2hSYLelH9RtbWfY3B0XXAqOn5OV8zawicuDHM/n9i5KeJA1MaGpW9rzv7FqAkaTZwARJ9wAbRMTcnH4lcDgOODIzM+tZu+/4Rn70n8cVbrvi3INXvB9zyJRl0NpMeBExH9iphOJam0i6hnTP+HpJi0kD4c6nvKXej2flaiB34PtNMzMzMzMzMzMzMxtGPDOzmZn1jIhYkv9dCtxCml312RyoSIPL/vbNc0pE7JFfDmQ2qzBJWwG7kpZrb3ZW9oHSFxekF/38SZLm59ekon3MzMzMrDtFxJERsXlErBMRoyJiakQ8FxH7RcR2EbF/LTA5kskRsU1EvDUHr9fymRYR2+bX5XXp8yNip3zMCTlI3nqMpNGS7pb0hKTHJZ2U0z8raYmkh/LroLpjTpO0UNKPJR3YudKb2VCR9LSkR/Pf//yctrGk2ZKeyv+OzOmSdHE+Lzwiabe6fCbm/Z+SNLG/n2dmvWWA+4umzyNmZmZmZmad5GBmMzPrCZLWl/S62nvScr2P0fyyv9aFyuwQljQhpy2UdGonvo81T9JrgZuAT0XEC/XbciDIkAeDePCDmZmZmZmtwcvAZyJiB2AcMFnSDnnbhRGxS37NBMjbPgzsCEwAviFp7U4U3MyG3L7573+P/PlUYE5EbAfMyZ8B3g1sl1+TgEsgBS2SVg0YS5rg4cxa4KKZ9bz+7i+aOo+YmZmZmZl1moOZzcysV2wG3CfpYeAB4PaIuJO07O8Bkp4C9s+fIS37u4i07O+lpCV9rXuV0iGcO4W/TmrQ3QE4si4fqyhJ65ACma+KiJtzcrOzsg+UPqog3cy6TDsGvkjaWtL9Of06Sa9q77c0MzOzKouIZyLiwfz+ReBJ+ln5JTsMuDYi/hgRPyO1Yew59CU1swo4DJie308HDq9LvzKvAjAX2Ci3exwIzI6IZRGxHJhNavMysx43wP1Fs+cRMzMzMwAkTZO0VNJjdWle9cHMhpyDmc3MrCdExKKI2Dm/doyIc3N608v+WvcpsUN4T2Bhrk9/Aq7N+1pFSRIwFXgyIr5ct6nZWdlnAeMljcwP3+OBWXnbC5LG5Z91TF1eZtZd2jHw5YKc17bAcuDYdn05MzMz6y6StgJ2Be7PSSfkTr9pdbOpbgn8su6wxQz8rGtm3SmAuyQtkDQpp21Wt4rcr0gTOUD/54WGzheSJkman1+T+m43s+7W5/6i2fNI37x8vjAzMxu+rmD1wZFe9cHMhpyDmc3MzKynDLJD2A253Wcv4GjgXX1mVW1qVvY80OFsYF5+nVUb/JD3uSwf81PgjnZ8MTMr11APfMkDHt4F3JiPr5/1yCqqrBkmJE3M+z8laWJd+u6SHs3HXJzriZmZDXOSXktaXeZTEfECqaNvG2AX4BngS03m52dUs+62d0TsRgoCmCzpnfUbIyJIAc+DFhFTImKP/JpSRp5mVg0F9xcrtHIe8fmiOrzamJmZtVtE3Ass65PsVR/MbMiN6HQBzMzMzMrSt8FW0iWkANXI/34J+MRgf05uvHUDbgVExH1Af4Fh+xXsH8DkfvKaBkwrSJ8P7DSIYppZxfQZ+LIXaeDLMcB80uzNy0mBznPrDqsf4NJ34MtYYBPgNxHxcsH+Vl1XAF8DrqxLq80wcX7u2DsVOIVVZ5gYSwo8GytpY+BMYA/SPccCSTNyPboEOI5U12aSZrPwoBjrST++bVIpQVatGHPIFA8UsK4haR3Sc+tVEXEzQEQ8W7f9UuC2/HEJMLru8FE5bRV+RjXrbhGxJP+7VNItpAGUz0raPCKeyYEAS/Pu/Z0XlgD79Em/Z4iLbmYVUXR/QfPnEauu2mpjD0p6HandYXbedmFEfLF+5z6rjW0B/Jek7fPmrwMHkNqt5uX2iydYudrYtZL+nbTamGfWNDOzes2u+vAMfeQB2LVB2FM8YKo3SBpN6mPZjNRHMiUiLpL0WVL/yP/mXU+vrY5q1h/PzGxmZmY9ob8O4Yh4JSL+QpqFd8+8+0AdP27INTPrYWXPhNhiGTx7YkWUNMPEgcDsiFiWA5hnAxPytg0iYm4eTHMlnq3bzGxYyzP0TwWejIgv16XXz1j0XqC2YsAM4MOSXi1pa9KAmgfaVV4zG3qS1s+BaUhaHxhPOgfMAGorfkwEbs3vZwDH5FVDxgHP54CCWcB4SSPzyiLjc5qZ9bj+7i9o/jxiFeXVxszMrGpaXT3GKz/0rNrAqx2AcaQVh3bI2y6MiF3yy4HMtkaemdnMbJgazKxZnvXKqmagDuG6hti+HcJXS/oyaWaCWoewgO1yJ/ES0uwFH2nPtzAzs6FW4kyIRenPkYJbR+TZmfsdEOPZEyuv2RkmBkpfXJBeyLNSmJkNC3sBRwOPSnoop50OHClpF1JH4NPAJwEi4nFJ1wNPkDqGJkfEK20vtZkNpc2AW1LTFiOAqyPiTknzgOslHQv8HDgi7z8TOIgUmPYS8HGAiFgm6WxgXt7vrIjoO2jPzHpTf/cX59PEecS6Q6dXG3PbhZnZsOZVH6xQ7k95Jr9/UdKaBl6Z9cvBzGbDQFlBq63m48BXM2uD0jqEJZ1AmrlmbWBaRDzezi9iZmZDY6gHvkRESLob+ABpZpv6WY+sS+Xfa8vPU03+LAe5m5n1uIi4j3Qv0Ve/M9NExLnAuUNWKDPrqIhYBOxckP4csF9BegCT+8lrGjCt7DKaWbUNcH8BTZ5HrNr6rjYm6RLgbFL/x9mk1cY+MZRlcNuFmdmwVlv14XxWX/XhBEnXkgbJeNWHYazBgVd9j/FgKVvBwcxmZjYoDnK3KiizQzgvb+IlTszMek87Br6cAlwr6Rzgh6Tgaes+zc4wsQTYp0/6PTl9VMH+ZmZmZmZmZmYNq8pqY2ZmNjxIuobU5v16SYuBM/GqD7YGrQ688mApq+dgZjMzMzMzM+t57Rj4kmdV23MQxbRqaGqGCUmzgPMkjcz7jQdOy0t9vyBpHGkWgmOAr7bzi5iZmZmZmZlZd/NqY2Zm1m4RcWQ/m7zqgxVqcuCVWb8czGxmZmZmZmZmw1IZM0zkoOWzgXl5v7MiYll+fzxwBbAucEd+mZmZmZmZmZk1yquNmZmZWWW1MPDKrF8OZjYzMzMzMzOzYamsGSYiYhowrSB9PrDTYMpoZmZmZmZmZsOXVxszMzOzimtq4JXZQBzMbGZmZmZmZmZmZmZmZmZmZmZmZmZmDWtl4JVZf9bqdAHMzMzMzMzMzMzMzMzMzMzMzMzMzMxsePLMzGZmZmZmw8xRt98UrRx31cHvLxpVa2ZmZmZmZmZmZmZmZmZmZtYyz8xsZmZmZmZmZmZmZmZmZmZmZmZmZmZmHeFgZjMzMzMzMzMzMzMzMzMzMzMzMzMzM+sIBzObmZmZmZmZmZmZmZmZmZmZmZmZmZlZRziY2czMzMzMzMzMzMzMzMzMzMzMzMzMzDpiRKcLYGbDy49vmxStHjvmkCkqsyxWHa4XZmZmZmZmZmZmZmZmZmZmZmZmw5NnZjYzMzMzMzMzMzMzMzMzMzMzMzMzM7OO8MzMZmZmZmZmZmZmZmZmZmZmZmZmbTKY1YsHwysfm5lZVXlmZjMzMzMzMzMzMzMzMzMzMzMzMzMzM+sIBzObmZmZmZmZmZmZmZmZmZmZmZmZmZlZRziY2czMzMzMzMzMzMzMzMzMzMzMzMzMzDrCwcxmZmZmZmZmZmZmZmZmZmZmZmZmZmbWEQ5mNjMzMzMzMzMzMzMzMzMzMzMzMzMzs47o+mBmSRMk/VjSQkmndro8Vg2uF1bE9cL6cp2wIq4XVsT1woq4XlgR1wsr4nphRVwvrIjrhRVxvbAirhdWxPXCirheWBHXC+vLdcKKuF5YEdcLK+J6YUVcL6xZIzpdgMGQtDbwdeAAYDEwT9KMiHiisyWzTnK9sCKuF9aX64QVcb2wIq4XVsT1wopUqV4cdftN0eqxVx38fg02n/o8hrsq1QurDtcLK+J6YUVcL6yI64UVcb2wIq4XVsT1wvpynbAirhdWxPXCirheWBHXC2tFVwczA3sCCyNiEYCka4HDAFf64c31woq4XlhfrhNWxPXCirheFPjxbZNaDpQcc8iUXghydL2wIq4XVsT1woq4XlgR1wsr4nphRVwvrIjrhRVxvbAirhfWl+uEFXG9sCKuF1bE9cKKuF5Y8yKia1/AB4DL6j4fDXytzz6TgPn5NamFn9H0MVXNo0plKev7uF64XrheuF4MZZ1wvahuWbq9XlTxu1fhVdXv1o564b/R6pfF9aIaeVStLK4X1cijamXpRL2o6nevwquq360K9aJK/zdVKksny+N6Ud2yVL1eDLZOVOk6WpWyVOn7DPd6UaXfRZXK0ql60Ynv5fK4Xrg83VkvqnLerdK5uyp5tFonBlsveu13UaWydHO96NR364XyVPkaMth64b/R6pfF9aIaeVStLK4X1cijamVp9rUWPS4ipkTEHvk1pYUsJpVQjKrkUVY+VcmjZa4XQ5JPVfJomevFkORTlTxa5noxJPlUJY+WlVAvBqOj332IdfV3G2S98N/o0OXTzeeLXvt/7LXv0zLXi9LzKCufbq4Xg9XV1+A16OrvNsT1okr/N1UqC1SvPKtwveiYqpVnhR5quygrn6rkUWY+TeuhelGl30WVytKSYXQNAZenYa4XHVW18qzg68iQ5FOVPFrmNq0hyacqebTM15GOqVJZVuPzRel5lJVPN58veu3/sde+T8tcL0rPo6x8OlIvuj2YeQkwuu7zqJxmw5vrhRVxvbC+XCesiOuFFXG9sCKuF1bE9cKKuF5YEdcLK+J6YUVcL6yI64UVcb2wIq4XVsT1wvpynbAirhdWxPXCirheWBHXC2tatwczzwO2k7S1pFcBHwZmdLhM1nmuF1bE9cL6cp2wIq4XVsT1woq4XlgR1wsr4nphRVwvrIjrhRVxvbAirhdWxPXCirheWBHXC+vLdcKKuF5YEdcLK+J6YUVcL6xpIzpdgMGIiJclnQDMAtYGpkXE4yX/mDKWwahKHmXlU5U8CrledCyfquRRyPWiY/lUJY/VtKlOQHX+D1wvGtDGejEY7V52vp0q+d266BpSVj5VyaOsfLr5fNFr/4+99n1W43rRsTzKyqeb68VgVfIaXJJKfreK1Isq/d9UqSzQofK4XqymSmWB3q4XVbqOVqUsVfo+qxlm9aJKv4sqlWU1FbiO+Lw9sF6+jgzEv4eB9XK9qMp5t0rn7qrksZouqhNl5dNrZenmerEmPm/3z9eQ9uRTlTzKyqebzxe99v/Ya99nNa4XHcujrHw6cp1RRHTi55qZmZmZmZmZmZmZmZmZmZmZmZmZmdkwt1anC2BmZmZmZmZmZmZmZmZmZmZmZmZmZmbDk4OZzczMzMzMzMzMzMzMzMzMzMzMzMzMrCMczGxmZmZmZmZmZmZmZmZmZmZmZmZmZmYd4WBmM0PSiE6XwXrTcKtbkjYueK3TxPEfrXu/V59tJ5RZVjMz612SNul0GczMOkXS+pLWyu+3l3RoM/fkZmZmZmZm1jpJIyW9rdPlGCwloweZh9vozJok6aw+n9eWdFWnymNm1osk3Szp4Fo7ulmVuFKarYGkDQbY9qZ2lmUwJP2npDcXpO8PPFTSz3iNpA+WkVe3amfwrqS7JX2nn9eckn7GGn+n7ahbXeRB4H+BnwBP5fdPS3pQ0u4NHH9y3fuv9tn2iUYLIWlPSW/P73eQdLKkgxo9voH8d6tIHlMGm4d1jqTdBnp1unztIGkvSevn9x+V9OWi82mDeW0p6R2S3ll7NXjcBfnfQV2/c7DYHEmP5c9vk3RGi3m9OV9DkLSupNc1eNz7Bnq1UpYuNlfSDZIOkqRWM+m1gMBu/z6S9pb08fz+DZK27nSZekFRHZD0+k6UpRPyeXZMp8tRsnuB10jaErgLOBq4oqMlqoii6/1wf4a3pEp1o+j65mve0MgBOh+V9K/585sk7dlkHm8dmtJ1RrffL5ZlMPedkjaU9KHcFnVyfr/R0JW24XJt3Oky9KoqXUPyz/Z1xKwDJN0jaYN8vn0QuFTSlztdrsGIiABmDjKbstrotpH06vx+H0knVuH62qgqtmlJOr7TZWiVemCwwBqMlnQaQK73N5P6XDsi9y88Jel5SS9IelHSCx0qy6slfUTS6ZL+tfbqRFmGmqS1NECczgDHvSH//0yRNK32GkQ51mty/yHpp1IK6t8iP7e/SS3EKZWRRyeopIFBktYuI58yaRADwCS9R4MLRP4G8BHgKUnnd1sfgdtvhoZKjF0YVDnSfbiVSdKBwOHAljlpCXBrRNzZRB5/BRzWJ48ZEfFkmWW1NZP0YETslt/PiYj9irY1kM+mwGSTkNvXAAAgAElEQVRgx5z0OPCNiHi2weMHuhGNiDh7DccfBZwNTAU+D7wB+ArwZuD4iFjQSDkK8l0bOBA4EhgPfC8iPtDAcScDz0fE1D7pxwKvi4ivtFKedpB0X0Tsnd9/KyKOrtvWTJ04eaDtETFgQ4+Kg2PHAf8ELI2ItzdSjoJ8m/qdllm3urleAEi6FLgxImblz+OB9wOXAxdFxNg1HP/DiNi17/uizwPkcSbwbmAEMBsYC9wNHADMiohzW/pyq/6MSyPiuHbmIelLwLSIeLwubfdWz13dJjfYvB/YivS7BSAizurvmKqTdHd++xpgD+BhQMDbgPkR8dedKlu7SHoE2Jn0na8ALgOOiIi/aTKfC4APAU8Ar+TkiIhDGzj20fzzFzR6/eonn+8C/wh8s+489lhE7NRkPscBk4CNI2IbSdsB/15//zXAsZfnt5sC7wC+kz/vC3w/Ig5ppiydkjt974yIF5UCwncDzomIB5vIQ8D+pIEwbweuB66IiJ80WZYFwP8FRgL/DcwD/hQRRzWRx+eBc4DfA3eS6tunI+LbTZblfcAFpN+v8isiouEGzTK+T6fk6/sewJiI2F7SFsANEbHXGg5dU74HRMTsJvb/K9Iz6v0R8du69AmNPO/mjsybIuK/WyrwynzKeObeF/gW6Tr0IDApIp7O2xq+p+9mkt4DfBF4VURsLWkX4KxGrh9VVvv9Sfp7YN2I+LykhyJil06XrdOK6nYn67uk1wDHA3sDAdwHXBIRf2hzOTYETiOdVzbNZVkK3AqcHxG/aWd5OqFKdaOfsiyIiEYGCA9FedYDPgO8KSKOy/enYyLitk6Up0ySLgH+ArwrIv6PpJHAXc20J0n6HvBq0vPMVRHxfItl2Qw4D9giIt4taQfgr/u2DxUcN6i2tYL8SrtflPQOVn+Gv7LZfNptMPedko4BziQNJlqSk0eR2qQ+1+j3l/QW4H3AaNIz7k+AqyOioaARSWdExDn5/Q7AfwDrkJ4hPhQR9zeSTz5+yPppmr0Xr6oqXUMGKI+vI20i6UXSvVShZp7hy1KV38GaApgi4uZ2lWUo1PovJP0tMDoizpT0SEQ0FaAz2HZwSf/JwHWwqeddSdOBr0XEvGaOqzu+rDa6h0jX561IAda3AjtGRGmT2AyVIWzT+tcm6kXfe0aRngHPg8bvGZUG/kVEzMv3GBOAH0VEw0HvuU1rFDCn1g6V0z8REQ0FXEp6BVgEXAtcExFPNPrzu0H+u7kKeJTUtj+zk/3DkhYC76lCnI6kO4HngQWs7AsiIr7UsUKVSNLVwN+Rvts8YANS//oXmsjj+8D3WP3/6KYmy/IOUp/dayPiTZJ2Bj4ZEQMOhBiKfqrcxnkm8CzpGR7Suajha2wZeXSKpKdIE+hdDtwR0VqQo6RFwE3A5YM5bw72WV/SPcCh+fgFpDbI/46IAds3CvL5NvDXpO80LSJ+1MzxdflsSIoL+mfgl8ClwLcj4s+t5NcuZbXflNT3WEa/7l7AZ0kxViPqyvGWNudRSuzCYLVtBtFuoDSj6rHAe4EtcvIS0gPB1Eb+WCV9BdgeuBJYnJNHASdKendEnNRAHqeQThbXAg/U5XGNpGsj4vwGv8/GwAnA/5CCDE8nncyeBM6LiOUN5PEW4Iycx/nAhXV5/GP9TfYa8lkL+BjpAXQUKxsh/z0i7mkkjw6qHynbdwaHhkbR5pPG1aQ/9tqFbHfgfklHNdiZ/7uCtPWAvwU2IQWT9isirpJ0GynY9ElSA+65wKWtXPAl/Q1ppM5BpHq6F7B1RLzUYBZHkQJv+/oWMJ8UDFtV69e937HPtmZGVjc002R/oi6IM/8+/oUUjPF3EXFHs/m1+jstuW51c70AGBd1AboRcZekL0bEJ3Mj3JpEP++LPvfnA8AupM7EXwGjIuIFSV8E7if9bgYlBhnI3GIeTwJT8rX6clIDzbAIZM5uZWUDxR87XJZSRMS+AJJuBnaLiEfz551IN9rDwcsREZIOIzWKT1UavNGsw0mNwa3UjTuB5cBrtepsAs0+rK0XEQ9o1QlGXm6hPJOBPUnnKyLiKaUBYWsUEbXZPe4CdoiIZ/LnzemuGTn/JSJukLQ3qbPjC8AlpMEpDcnX39nA7Byw+W3geEkPA6dGxA8azEoR8VKul9+oBQQ29W1gfET8k6T3Ak+TghLuzWVqxucZfKNxGd+nU94L7EoKuiUi/kcNzlq+BlOBhmZgkHQi6W/0SWCqpJMi4ta8+TzS+WRNjgbeKekNwHWk6/kPmylwGc/c2eeBAyPicUkfIP29HB0Rc2nunr6bfZZ0zr0HICIeUgVmRyqBJP016dmidl2t3Mwb7STp3aTnvC0lXVy3aQNau16X5UrgRVauSvMR0rNfu2dzvJ7UubRPRPwKQNIbgYl52/g2l6dtqlQ3csDgjsCGfYJ9NiC1dXTK5aTnsNpgyyXADUAvBKGNjTT444cAEbFc0quaySAi/m8OCvsEsEDSA6SOwWYDNK8g/V//c/78E9K9woDBzAyyba1AKfeLkr4FbEPqcF0x4JSV7cJVNpj7zn8Gdo8+g0CUAuXvp4Hvn+85DyE9M7wd+CEpqHmupOMb7Et4H2lAJaTnqZMi4o4cgPQVUlDBGpXVTzOAhu/Fq6hK15BcHl9HKiAiXgcg6WzgGdK9nUj35pt3qFhV+R28J/9bGNxEmnm0m43IbXBHsPJ63orBtoN/Mf/7PuCNrGx/OpIUwNWsscBRkn5O6qettZk2FPxVYhvdXyLi5dy+9tWI+GrtHq4LDFWb1t8CjU728jlSEPjjrGzzWZsm7iVVN5GQpPqJhE6VtGs0MJGQpPNIg3kfBE6X9JWIqD0PnwA0OnvsI6Q2tiOBGZJ+B1wDXNto7EYVadUVOy8CvkkKULtX0m7NBIWV7NkqBDJnoyJiQqcLMYR2yH3aRwF3AKeSrgcNBzOT+qpOKaEsF5Imf5sBEBEPq4EVUYeon+okUh/gcy0eX1YenbI9KwcGXSyppYFBpADNDwOX5fi1aaTzZsMzrZf0rL9hrud/C1wZeQBYE8enHxrxUaXZy48ErpAUrIyheLGRPJRmvf4o6ZryQ9JAkr1JbaL7NFumNiurv6+MvsdB9+uS2gc+TZ+BGE0qI4+yYhcGxcHMq/oW8BtSh159p+hE0sPFhxrI46CI2L5voqTrSI2wjXSsHksaTblK8LTSLFaPk4KKG/Ft0oi13UknoEdJIwoOIF0oD2sgjytIN78bAnNJJ7+zSB0504B3NViWqcDPgX8jBdq9QBoRdYakt9bdqFdRGcGFXwIO79NRP0PSLaQb8TWexKJuRF1+yDuJdMG+NuffiB1IndUPkEbBbkY6DzQ1qkbSYuAXpBPwP+QRJj9rIpAZYETRAIGI+JPU+lJLbTLQ773h4N2I+NxgC6I0gvcMUqPOuRFx9xoO6S+fwf5OS6lbdHe9AHgmd3Rcmz9/CHhWabbrv/R/2Ap/lW9WBWxTd+MqoNERUy9HxCvAS5J+WrsBj4jfS2qkDKtQWo7j/wG1B7TvkgaiNPy7zQ8EO5MGCv0eeCwiljZTjoi4jPRwMQb4OPCIpP8mBc23VO+7TC83UIypBTIDRMRjkv5PJwvURi8qLZf2UVJw31qkASHNWpSPa6WB/4yI+EdJt0ZEI/eG/fm1pG3I18EcGPhMC/n8MZ/zyfmMoIlraza61kCUPUt3dRDXHjAPBqZExO2SzhnogL76NEA8C/w9qdFtF1KHXaPBimUEBNaeOQ8mzbryfIuX9DIajbs5wPFPuQGh9je2/poOqJE0o79NpEGRjTqOFJzyW0lbATdK2ioiLqLx4N/FEbGHpO1J90nfzvdJ15Aa2RppgCzjmRvSbMSPA0TEjZKeBG7O93LDZQmrPxf8TfbCd/8UaYalW3Kw+ltIHYzD2f+QBoceSmrMrHmR1MDZKTtFxA51n++W1IkZpbaKiAvqE3JQ8wWSPtGB8rRTlerGGFIA40asDPaplWXQA2oHYZuI+JCkIwFyR0k3tE804s/5Oly7v3gDjbVdrCIPQDyDVJcuBnbN/0enR+OzTL4+Iq7Pz0fkYJ01dryU0bbWR1n3i3uQOq678bra8n0nOcCrIP0vNH6/eBywS0S8kvtDZkbEPpK+SQpwW+OqZX1sEXnSh0gDcNdt4thB99OUeC9eRVW6hoCvI1VzaETsXPf5khy8OdAKpEOlEr+DIQpuqpKzgFmkmQXn5eewp1rIZ1Dt4BHxXQBJX4qIPeo2/aek+c3klevJJFL/dktKbKP7c67DE1l5juuWpdQH06bVX4CZgGau6TuS+tLXJ60W8ZKkiU3eS5YxkdB7gF3zve5ngaslvSUiPk1zA+sjIh4jDRz4Z6UBWx8G7pP0i4hoaOBWBfWNd1hO6n/+Eukes9G4lFJo5eCo+bnd8T+o64Np4lmnTN/PsTWPrnnXrrRO7pc+nBRE9+fauaMJt0k6KJqYMb0/EfHLPrcMzQQHltlP9UvSQJ/BKCOPjihrYFAO8L0UuFRpkr+rgQsl3QicHRELG8imjGf9sgaAka9DN5KuiZ8iDSD6R0kXrykGL8eqjSHFSb6nrr5e1+w9U4eU1X5TRt/joPt1SavXNz1h5RDkUVbswqA4mHlVuxd0ii4mjfpvdFTHHyS9PVZfbubtQKPLZP6FFPDV9+Foc5prUN4iIg7KD1uLI2KfnP69JkYkvC4iLgFQmvmgdhM5VdIJTZRl99qDOulGem5E/Kuke0mjVqoczLyp0vIzqntP/vyGBvPYIApmHIs081Uzoz43Bk4mnZCnk2ayXOMM2/nYqaTG3uMj4gf5YfFzwMOSPhURdzVaDuBG0k3kh4BXJN1K853ea0naLCJWGQmttKxk1W2kNPp5rfy+9jAjUuB/Q7TqjBWriYgT13D8PFId/ALwg5y2YtRqNDdCteXfacl1q5vrBaSZxM4kPdBCGjH8EdKN0xENHF9GAOefJK2XA9FXLJ+otERI052SpAD3dYBv5M9H57S/XdOBObDxFNIItKeA/yXNhLK9pJdIgzmmR0RD5cqdrH+VX78GHgZOlvTJiPhwM1+qC/VyA8Ujki5j5SwZR5FmFhgOPkQ6RxwbEb+S9CaaG2Fe8xLwkKQ5rNqYNuB1JPsBabmbhkce92MyMIU0KGMJ8DPSg06zvivpdGBdSQeQlp7/zybzmCNpFik4EtL/83+1UJZOWZI76g8gBVG9mnTP0YwfkBogDo+IxXXp8yX9exP5nMTgAwJvk/Qj0mCW/5eDZBp9LqpXRqNxNwc4Xp/rxUaSjiMNary0wWP/L+nv8bd90kUajNaotSLitwAR8bSkfUgBzW+m8Q6XyMf/hLSyzNmS3kaaPWAmsG0DeZTxzA2pI/CNOWCRXCf2I83OtU0T+XSzxyV9BFhbaVbNE0kzgnW13Hld68BeC/h1g9fEnhVp9pjHSLORT+90eeo8KGlcpBnRkTSWFBTVbj+X9E+kZ5Nnc1k2I60w9ssOlKdtIuJh0rP71c0MWB0iH4iIoyWdHhHndbgs9f6Ugx9rwRfb0COr5ZACj28htXeeSwrQOKOZDPJ1/OOkDpvZpA6wB5WWD/8Bjc8y+bsc7FP7fx5HAx2tg21bK1DW/eJjpNkgWxng2WmDue88l3Ruv4uV5883kZ5tBlxVsI8RpM7AVwOvBYiIX+TAhka8RSmIWMCounYyaK4Trox+mrLuxSunYtcQ8HWkan6nNKvitaTvfiTFq4+2Q9V+B90+CL9QRNxACs6tfV5EWq23WWW1g6+fg0QXASitQtTMAB1yAO7XI+KtgyhHWW10Hwf+jjSp0c/y9/nWIMrVToO5t/gN8Pa+fYYAkhp+VouIXwAfVJplcLakCxs9tk4ZEwmNiIiX83G/kfQe0oqkNwDNrJCy6qj0iAeAByR9hpUTE3WdyKt5Vkj94KiXWHXVpqAzM+rvDXxM0s9I17KmZovvAt8kre74MGlG7jfTfN/VSaSZz/9ImnSt2VVIa34p6R1A5OeQk0grFjaqzH6qRcA9km5n1b6RL7c5j45QSQODcpzBwaRr6lakgRJXkZ7ZZpJmgF6TMp71P0caAHbfYAaASTqU9F22Jc0MvWdELJW0HvAEA8Tg5TbzBRHx3qLtfQaEVVVZ7Tdl9D2W0a97t6QvkK4t9eVYY8xXXYxYy3nUKSt2YVA0uAEDvUXSXNIJ66ZaYFX+I/4gcHJErHH23FxJLiEtS1J7KBlNanydHBEL+ju2Lo8JwNdIJ6z6Rr9tgRMiopHle1Ga0fNvclkeBXbOnc6bAN+LVWe/6S+PBaRGhg1JSzlMiIj5krYFbm70xijnc0RE/DT/H30lIt6Ztz3RSFk6RWnZmH41MmpTaZavd/QNPFYKTv5+RPxVA3l8gbQs0hTg67VAgkZJ+jRwcX7Qqk9/a86vqYcbSSItLXAkaTm5DUkjXmY2UjZJx5A6yz9DXlqIFHj5BdJIuyp1sK5C0uUDba8L3F9TPhPrPn6OFARbn8+A/weS7qH/gOOIiKZGqLb6Oy2zbnVzvagKSa+OiNUaYyW9Hti82UZASQ/HqrNoFKb1c+w1pGvi96LPDYekTUk3Qssb+b3mxqVDSMvvTc2NM7VtP46IMQ19oS6lNEPdtqQA0Z5qoJD0Glad/fte4JKIaCXYsasoDf74Q6QZp7YnBerf0WwnYJ/ryQoN/m09BpxH6lj+x4I8mmqQy99prWhwCaOC49ciXXvGk+r5rIhotHG7Pp/3kRogAO6NiFtaKU8n5IaGCcCjkWa52xx4azODgySp73m3hXKsDVwQEf8wmHxyXhuTRgS/kr/fBrUA0ibyKLr/iohoesbMPsEMXUMpwL/+b6Oh5dsl3QF8PgpWMpB0b6P3apK+Q3oufqgubQRpxZ6jImKNo94l/TAimp1Nr28eg37mzvnsD/xvDsKoT98o59PIjDpdLf89/jMrO2NmkWah6OrgCklXkzp5XwHmkZYWvygi2t7oVjWSvgfsFxF/6nRZYEVbyRjSKkGQ2r5+TFqavm33upJGkpYuPYy0ylCQOkdmkK6Fy9pRjk6StBdppbo3k4IIa88bja4QVEYZniANhL2D1DbRt4O+I7+HfP09gzQj2F3AXsDHIuKeTpSnLPm+exywDNiP9P89J5qcjUbSd0kr8t0QEb/vs+3oiGgoyCZf378K7ETqHHwD8MG+1+mC4wqfhWpabUca7P2ipLtJHasPsGoH0qGt5tlOrd535mNHkpZj3jInLcl5NDohx0mkZ8L7Sc90F0TE5UqDIm9q5N5VaZategsirS6yGSng9esNlmXQ/TRl3YtXWRWuIbkcvo5UiNJqPheRvm+QJv34VEQ83YGyVOp3IOlrwHasGty0MCL+vhPlKUtu27wE2CwidlIa8HRoRDS72lgp7eD5HD6FFLwl0jnqkxExq8l8ppP6pvoOqG70+EG30RXkOZIUFN81E4IMok3rHGBGfX9Q3bYLIuKUFsqyPum6NbbJ/sv7gX0jzeq8Vl0syYbA3RGx28A5gKTbgC9EnkG8Lv0c0qomDQU+SfpIRFzdaNm7jaTzSPdPv8mfRwKfiYimBl72IqXg3tVERMszyFedpBWDANr8c19PupfZn3Tuugs4sZl7yrL6qfqLV2okTqnMPDpFaeLRbwGXx6oDg5B0SvRZbW2AfBaRgl2nRsT3+2y7OBoYDD3YZ/3c73ZiRLQyqKZvXtNJ3+Xegm37RcScNRw/6L6aXlBG32NJ/bpFgdgNxXz1c2xTeVSNg5nr5IfrC0hLVCwnXZQ2IgVOnRoRP2sirzdS12DXQmf9WqSR+fWNfvP6BguuIY8jga/kj8eTAoWC9MD+uYiY0kAe+5Fm4/wLaSmuT8P/Z++8w2Spqq7/W5ccJAeVHAVRkIyAr4KiJBFJCghKUkQlvQoqKiBKEkREQAmSg4SXHCRHyXBJAipBEAniJ6KASFjfH/vUnZq+PTOnumumu+f2ep773K7qPrtPT1fX2WfvtddmOSIZuJPtizLnsjbRJukNIqD1edt3pCDkt2zvlfu5ehGSvkz8/b7JYILmIcCvbf8qw8Y7xN/vLQYTWFutJivsTk8EFM5pZXyyMQ0RpN6SUHqaK3PcekTC8APEZ3oYONjtS993DJI2tX1+C+O6yllo9TttsNHStdXL10UK2H2TqOab1P0g10GQ9C+a/L5p83feDiTdSyQQH0/HiwLn5QRmap7HdsA5tidT8JA0q+2ebI2Ti/EeoFCooyxo+7FOz2UsoSj2+ggwO5HUuYtou7f1GM5hTUINewuCsFNG9mYtkf+2ZfL7XyUlNEk/tP2D0vFUwKlj+TfpBqTvZYlS0n7mnL2IpEsYprNCVfKEopvKalXGDGFndSa/Nk5t124L8/gwQbaZ2faCkpYjElm7jPVcehGS5idUaCbb20paw/atGTZmdsWizGFstbXnHsLmCq5Wpd7TkLS5Qz1r2HO9BkkTbX9IoQS3ArG3uGesiLHdDEmnEt1gLqakjOcOqbAM5eMWGEtfV9JSwPzA7eX7lKR1c8hqvQ5FF4U9gHsotU21/fcxnMOuROxyUeCvDU+POSmuDIUwxGrE3vx22y91ai51oq5YlKRpGVAweswtKLQqVGveJgocRBQ2THDFApsaSMi1+ItNyLTApO4B4x6JNFz20yZTUxxh/DLEevWQ7Ufrnl/FubSdpxnv6IY1JM2jv470MSS67TtQdP6cJOzQKrmpm6AocPoW8KvCv5D0kO0PVLRTWxw8+ReFmNSjVf2KZONRglz9Z2IPlUWuHoUY3Q3ARkRs7R7gReBW23sON66P+qAahIRSHgQ3FAGm5+az/Wwb8xs3Ma1m+xRJ9451XrL03qcAuzWQqw+vQnSrcS4HEIJAv2uWL+11pH3EgUQH+vUkvR/4sO0TK9qZj4FCOwCaET5HsDFZvDs3Bt7NaHfP3AnUVRhUR26ijr2+pDttd7xLj6TDSB216vj7jhUk/cz27kP5WlV9rLqQ8tnzMvi+8/TQI7oLkm6xveZQfKWx5in1ycxDIG1sWwq4KCrw1mVy9YGXK9go2oyVbdxZ9SaSfjCy/ZZCNetDRACxZdn75JT/o2rALn2mOTsdJKgKST8Y5mnbzmqRJ2lDYC9gmXTqYaL6smrr9LaRrouCqPpJQjV1s5psf8f2QW2Mn55oSdmTCXRJT9uu3BKslU2YpL1sH5oeDyIdSDrQ9nerzmOI98n+Tkfr2uqV60LS/cAvmTx4n6sQeCHRmuT/gLO7wcFJRS0nMVjBYDs3UZQZxsbmwJW2/yXp+8DywI+qBlZSgGAJYPriXNXNZ69DoWhd/vwdv0bahaINzk+AaW0vIulDwA87tdkYSxT3fknfAGawfagylc8b7DxJ8w1bdqJO0g5VA0IN438H3E50A5nUVs8VldAUFbB/sH1QIkWcA0y0vV8FG5sQBWPzEPetjhWEtAJFhfxKwPtsL6lo0X2u7TUyxhaBlE2I9eT0dLwl8ILtPSrO5VhiP3Iug0lv2Yrdkk4DFgMmMrA2ugWi+/yEWl/xd7iZCCL/ZehRk9m4g2iffnE7ibVOoO7rWtIcrkGZTNIuto9p106ytVQVsoqklQhF5reJ+0YloosGWm9NOgVcRLSQ1HhJAA2HZnuQTiaH6oKkh4nYx5mEgtaNrayv4xHqQhUWDS7gmQt4lyuICdQ0h12BrxHtQj9ErC8Xped6/jeRA0l3OKMj3VhA0rG2v9oF8xj2ex8P60Qdiavkf55KtAIWsTZ/sYVEcVtrUo0k5Nr8xZSIXzkd3mn7xao2OoF2/M60n/8l0e3tL2ns/ESL+F3G6neT/MSfEDmV7xCdRFYB/kAItEwcZnijrQWBVxyt4Bcm9mqP2H644pzaInh3M7ppDYH+OtItUBRm78Tkhc1jRvzqxu8g5U8edka31l6DpLtsr1wmISoVmrZgazkGlCxv9gidGhrGbjLc81ViWsleS+TqUYjR3Wd7eUk7EqrM+0p6wD1QtFtHTEvSNG4omJM0Vy7nQFG8egQRt94V+D7RmeePhO+a3Z0k3d/mJ+JRT7RCjOvHtIaHovv4yk7kcQUJ/G7byww/ctTm04xc3RGRMoXw00eADwP/ImLkNzlTgLDboehqchKwj+3lEr/pPtsfrGDjEKLrwe8ZnI+oWkTS7h51NSKfsTQwLTAV8GrFe19tpM269sydgNoUkyvZWZRQ2/4wsR7cBuxh+4mKdtra6yu6UU8D/IbBebese/dQZFMqrq/JzkzE7+T1quM7BUkr2r5HNRWR15R7/AawL9Hpr8iRO8dPk/QF26dLalqg5ooiJJI2IDiJZT7JD6vY6AZMPfJLpiwkZ/YzpMCSpGeBi3KdSEnbEhfpVUSwDGAt4EBJ+ztDgUzSJwk15D+WbMwPLJ6SxdlS5ERLPoDnCdW/BYkbUUtkZkmLECS0hwmFjGykoPhLDXZ+X9VB7wCaVbXNRLS7m5Noyz4ibF8KXNrqJCStbfu69HiRcnJP0iY5G/B0Q98KWJ9ofbAGsIjrrb76KlCJzNyMAEuQZnoRGvklteHzwKHp8XcY/DdbF6iFzEzGdzoa11aPXhdv2T621cG2N1YUxGwCHK8gcf+GIDZ3pBWi7WslLUGoI0GoLFVVMPi+7XMTUeHjRELpWCA70ZGCdLsR6+FEQknjNqKbwrhHIvweDryXUF1YiCBbdCR4UzP2JZKKNwDYnpj8hCkBSgGErQm/AiCrlVwDVio9nh7YHJijoo3TEpGnUIW5EfhlY5B4GEzvepRAtgfOkPQdwoe+wtXbLR1KFMBUapHdRfgs4SffC2D7r5LelTOw2KRLOtx2+bq4RNLdLcxleuDvDL7Xmii6ycVKwPtbJciUcBJBTNw8HX8hnVunihHbz0iD3LVeUVRr+bpWtH0+gQiibA/8CFg0FQxsYfu2TDuNv3EB30n+Sh3KrlcR+9WR5vFRYk18meh2cyswu6Q3gW1sPzPc+Jovx1UAACAASURBVBLuJoowyn7NnMBPiet83PoYik4o6wPzSfp56alZiE5AvY5fEaS6+4GbUvL5lY7OqEtQkJYlzZyOa1FKbxUqFfAQ9/RpiST/iAU8NWMnYEXb/05EtfMkLWz7SMZ2jz/mKCXBr5f0E2KNL7fJHPMkuO2vdgPJnVhrhsJ4WSe+AuwJvC3pP+lc1cTVT4FPOnXZSUnGs4g1ekRooNPCDJKWZ+A3NwswY4V5/IyIIV0MYPt+Sdltw8uow1+UtAUR+7iB+ExHSfqW7fNamdMYo5391MlEQvyO8smUyD+J6Pg4LCQtCxxHXBdXAHvb/kd6LlfB6hgi1jAb8DsiQb2OomD/WCJ5PSIkfZv4nbyRyP/fJHzP/SWdmOP/puv6WILgPSnXI2lMCd6jgW5cQ9L79teR7sBFRDz/Gjq37+6678D225Iek7Sgx4FIRQNekrQYiVwjaTNayEVL2o3wz4vY0+mSjrN9VKaJTw/zXNWY1iTSshpERjLG1R2jm1rRsnwLYJ8WxncS7cS01gJOA6ZXdBH9su2n0tNXEd2YcnAc4ZvNTHTk3hvYDtgQ+AWRtxppLu8Hfk6Q6RYE7gPmUaiS7+aM7qH9mFY2zgCuVQifQHxXlURTasYESbOXfNI56BDPyvZJwElpH7UF4Z9+GcjKH/QA5rJ9TsoP4RBsrOpHbEwIxVRW44dJpN/Vgbkb4uGzEITkXPyC4HKcS8S+tmWgo1EuTkv/H1ZxXDPUtmfuAM4limZPoD2/8kzgaCIHB/H9nEU1vkIde/2i0KtMMM2+d9uu5fdel52xRiIyT0X4BHV09a0j97gbcd9ppTvRTOn/tr8PSb8kYmlrEb+XzQjuVs7YYXkFY81V6pOZS5C0N0GaO5uBL3R+4GxJZ9s+OMPMPkQCZJAKs0JN8g5CqWIkHAl8ouSMFzYWAS4nqndGhKSvEO1UlSqQvgQ8BBwk6VBnqO9JutD2xunxZ4hF7gbgYIXy68mZcxnKzkGSDsq10wnYnhTwSESS3Qin+WyGD4ZMgupRdz6MgU3Z+QzeoH2PETbgkv4CPE0ET7/pUEh9smYiM1RI9I0RuXqskU3UKVVNiUjaFAn23KonDfG42XE7GNZW3ddWj18Xl0jaBbiAwcH77MU9BTxOUrQt+jwRHJmeCEK0DEVbzC1tn5H5+qEUDBaXVFXBoNhYbAAcZ/syST+qMB7i3rsy0QZwLUXx0YEVbfQyDiAI3Nc4FBjWIpzp8YA3bf+zIVk8pbQO2Y0oRrnA9sOKquBs1fMCTTZHP5N0DzCc/9GIY4hK4EJhdRvivr5j5vjTJO1EFG5Vvv9psJrEkQQR7VbgRlVvkfdCDxOZAf5r25KK5M9MIw1ogpkkLepUUZ72EZXt2N6uhfduxEOEAk3LnWES5k7B2gInS9q9oo1nJK0OWNI0xG+wV66Vdq7rI4jg9szAZcDGtm9Jv7tyxflI2J/Yjz7MgH84FRWCLA3E2UFPEWSTHPyMIE39LV3bP7W9hqR1CHWJT2ba2ZxQ4znU9hVpfk/aXitzfC/jr0TiayOio0iBfxEtwnsatn9O+NAF/px8pykekj5AJELmSMcvAdu6orpkjWi5gKdmTCiI3bafkvQxgtC8EOOczMzkca0y0aIjSfBuIblPCetBTYmraQoic7L5h+Rn5eJTRMx6fuJ6LH5zr1CxSL+morW6/MV9CDW5F2GSgt81QC+QmdvxO2dqJDID2L69wr7mGGA/gqCzI3CLpI1sP07sWXMwTcm/O6RILDsK9qsQAbYB3k8kA58CFk0+6ExEricnVncSbRK8uxhdt4ZAfx3pIsxoe+9OTqD4DiRNsP1O+TmlotwOYXbgYUl3MliNr9e71H2NIIwupRAKe5LW4tc7AKvafhUmKWzeRsQvRkRNsaxJUPsiI7XE6AjC02+BW23fleLIf2zBTifQjm9xKPCpFDvfDLha0ja2b6faXu1dTp2SJR1g++x0/hJJuZ2Kfk2oOD8maRXga7ZXTTHxEwnC0Ejox7QyYPsQhTpzQTI/wPZvOzilw4HbJBWCW5vTofykpBMI//QFomhoM1JMZZzgVUlzMlAYsxowYqFAA54g9g0tkZkJ33FmgktX3jO/Qt7vfBJs/0nSVI6O9ydJuo/ICeaOvyf9X0lpdhh7vSr00paYXAkz2j6tdHy6pG9VtNH2Xr/de3VdpFPFxbA1wcM5QNICwHtsZ5FfO4lUILiQpGlt/7dNc7XkHql+rwLA9q/S/3V0Tlzd9rKK7h37SzqcKBLPwT0McNcmmyaQ3Q26DvTJzIOxA7CMJ29T8lMiYZtDZi4k3BvxDvlO9dREG7ZGPEt+wA7g68Rmagbgz8Ditp9PxOrrCad4JJTb5+wNrG37SUU1+7WE2kIO6rLTEaQFYU/iZn4KsEJRfZeJOtSd2yWunkdUon2OUFy5iNEhi2XZHENyde2Q9CDNP6eAeXPt1JA08hCPmx3X9T7NUNu11cvXRcIX0/9lx7fS4p6SZlsSbYJuAT5r++YK42chAofzEdWVVxPrwf8S6nRZZGbqVTB4VtKviAq2QyRNR3X12f/Y/o8kJE1n+1FJ7xt52LjBm7b/LmlCCsBfL+lnnZ5UTXhY0lbAVAoV8F0J5aRxD0fr5ZtKx08Qn78SGojAE4ikXVU/f2Xb5STqdZKyWzgC/yWqkfdhYA2ocv9rTIL+gwjKHU71JOjdkn4DXMhgYnUl5ZUO4px0z5wtBcO3B46vaGMP4AZJTxD+yUKEolglKJT1jgXmtf0BhUraRrarFKTMBfw+JenK30fVJN3fJX2BqJSHWCurVjnvTJDl5yP2VlcRa2YvoJ3rehrbDwJI+pvtW9LYexVtGnOxDPGbnAnY3/Zrkr5YMciyHeGTNAsmb5lpYyrbf0uPnybtNW1fXWVttH2+pN8CB0jaPs1riiimcbTovV/SmcR6sWCZiDYeoCbt1BisdjGl4jhgT9vXAyTS7vGE4kwnUEcBTx14QdKHbE+EUKyWtCGRrM5uZdqL6NJkd1eQ3FVzi/JuRSLoFGpMNzg6y1XB3SmhXrRO/wJRMJMF26dIOo0KxddDoC4Scl3+4gQPbjX7d1rrwtMJtON3XiHpMkLQpVAWXIBQIbsy8/3fZbt47WGKQt0rJW1Dvq/2H0X3y1mJa2Jj2xcqBBSqJOzftv26pP8S3S7/DmD71QYSwHCog+DdlejSNQT660i34FJJ69u+vNMTIVTJti8O0m/vYjKUWEcJ3+/Q+44qUlzzE+nvO8H2v1o0JQbfq9+mxQLDZvtCV2+z3a7ISC0xOtvnUupcmv7em1a10yG041tMWxTf2j5P0iPA/ykE6qrEcMpqqo3FSNNm2pihiJ3YvlOhfojt4zVEa/hm8+jHtPKQSNq5BKxRhe1TFYrqRY5iE9u/79B05iSu55eB/we8ZHs8dFkrsCexRi8m6VaiC30lAjHwGjBR0rUMvudk5d4ScfhGSSc7qfO3iNcUnREnSjqUEHtpaU+YcqcHETmz8ppWhVzYy0IvbYnJlci/Vyi675xN3DM/R4i3VEHbe31J8xIFEe+1vZ5C+f/DzhAjTaiLdHoMwWNcm/B3/k0oV6+cOb7TeAK4VdLFDC4QrCoQWEfu8QnC37uMwddo9lxqyse+nv5/TdJ7ic/xnpyBtruqa3afzDwY7xCVlY2L0nvSczn4MXCvpKsYCNgtSBC4cgirEMmSuySdzeCg3+fJIyAXeDMRAF+T9Ljt5wFs/6NIFGWg/LqpnVpx2X5JUu7fpE47Yw5Fi7RNiKTfB91CG1bXoO5Mm8RV27tL2gP4GHEDPpQgyXwOuKzK59KAovBkTxHk+RyMFbl6NLAJQVpubPezAPB8rhFF9f/OwOLAA8CvK244PqRQc26m7FxJWaCd77TOa4vevi7aXuQlPUVsQM8m2gK9lc6vkOznVNeeRpAAbyMUbL5LfI8bF8n5HLheBYMtgHWBw2y/rGiHVrXS8S+SZiMCXldL+geTr9fjGS8r2oHfDJwh6UWaF8r0Ir5BEGDfINq4XMUUQjZSVOzuxeRB9arqRWVf4i1CMWrz5i8dEm9LWsyhdoVC3aNKkvd/icK5lyq+L1B7EnQWImBVVrKo3EayU7B9mEKN4xVCSeoHtq+uaOPKFOBaKp161K21VTueuF8XlcEPJPJjlc3zfi28bzNsT6jwHEF8n78jVPyyka7POtpOdQLtXNflYFqj8kNuwgZHC9zNFZ12rpZ0RO7YEu4CHrI9WdGKpP0ybdwt6USiLehGRMcfJM1ItVZ/JD91D0X771MI1Y0pCesSHYCmBRaR9CHghy0UG3QV1EY7tSkAMxVEZgDbN3SYSFVHAU8d2Ja09yqQ9ubbpvmNewyRfP8ncE+VfWRN6BaSe1HgOw9B+L8uHa9F+CE94VsOB0kHE0mqgkS8m6Q1bGcrRQFfJci+RWL4JiL5kg3b76S4Ujtk5lpIyDX6i1cmgkmRDGslSdoptOx32t5V0nrAZ4jvAuL7OLoKoVHSrE6t2lMx96ZEp8JhFahK2JmIT75DqH9/VdLJaS475c6DyPOcSRTzXQucIulKItmbS2Cpg+Dd1eiyNQT660i3YDfgO6kY4E3yu1GOBp6VdIztXRRiT5fRGZ8TqE9dsdugEDDZFFgYmLoo+miBPHwScIekC4jr5jNUy40X86lrX9iWyEhdMTpJ8zO4u9bNwG62m4mjdRvaiWm9KendJY7Dw5I+TnQHXKzCHI6WNLPtf9suOhMiaXFCUTMHj0v6PnE/3wSYmGxMQz6RrR/TyoBCjfcoolv5tMTf5tUOrSFIOs32NpT8v9K5MYXtz6b3X5rwc69XKP/OP9ZzGQ04xDc+SuRFBDzmBiHKDFyc/rWL1xJPqNX83TbEveHrRGHLArRehHISsC+RG1mL4BlVJUb3stBLu2JyjeTfclGRqaCWTT17/ZOJ73SfdPwH4Ddk+js1kk5Xtb2CQjG84BFm54u6AI+nfxOo0D20Ccq5R4iuwVV5Mk+nf9NSIefWgDrysZcmPs1PiOJeE37wiNBg4bTJkMlTqg2ye4YjNuqQtC7wC6ItS5mIvDjw9ZIiwEh2Ziech3LA7reuoOSbHJDGoN/FVaq8FKoFq9l+U9L8xYYmESjv8GD1vaFsvE2QpQRMByxk+7l0E7vb9rKZc6nFTieQyNZvEImt8g+mUvBFk6s7H1nxmniZSAiIUGwtlBwFrGl79lxbyd40xHW6JdHSZu4q4+uAIprxsTSH9Qm1jB2Ay1shjY8VJF0KfMdJ4a50/oPAgbaHU7Qtv/43RCDvZmA94M+2d6swj/tsL58/87FBu9dWL14Xkta2fZ2GUNxwptKGpBsYrGharqhzzgZJ0oO2P5geT0VUei5o+z85cyjZGbaSvGpVm6Q1gSVsn5QInDMXhS1VkTayswJXuv3WIT2BlHh5nXDItyY+/xm2q1YGdh0k7dBYbSrpYNvf7tScxgqK4rffAN8kAglfBP7mNttwpt/+511B2SwFgU8iqkcLlZDtyoSnEcZfRRRNtKSkL+kLtk8f6t7TQiVtT0PR1n4J29cUwWxXVLSR9AEmr9Q/taKNu2yvXPY5JE20/aEqdupAItbcOtK5EWwsQhRQLEypsLfXiZsjQaG4eE3j71PSYsCmtg9twebMRAB1Vdv/M9LrS+PmILottNx1I/maOxHX9/1EQeDbCpXpedyiYkbyQd9l+5URXzxOkGIGaxNKnMVvfJIv2atQtFFbtvT/zMAVtj/S6bl1GokQcC9RAAmhKLZikQzr0JzWIZLaImJnlQp4+qgHKTi+EnBJOrUhUXS9MHBuK2tFG3P5JrAEIQxxEJFUONN2VmvxUZjPVUQ76efS8XuAk21/qhPzqROK1s0fsv1OOp4KuC8nTpv29XM3xqolLQO86AHFudy5HAy8ROyPyoo6IyotpXmfartlErKkoximmN6ZKl7JloD5CaL4mun0zbYvaHV+UxIUnZuecLSPL59fEPi+7Spk5HbnMjVRKGxCgGEVYCsiSXm07awi8yEI3hdXIXh3M7ppDUnz6a8jXQBJRQxzEds/TL/h97iJUvkYzedQglC5InCw7fM7MY80l64i6dUFRbHHPwnC0CSBBJfEnirYWoHBa+h9LdioZV8o6RpCgOcgovvYi0SHu+zuNjXF6K4mhEDKe7mtba9TxU6vQdIniHj5/Q3nZwO+ZvvHYziX2QjxoCIedbCju+yswNKNvssQNvoxrQwoVJA/T6iRr0QUgS3pakWXdc7nXtsrlI6nAh60/f4OzGVDgifyP8BswO3EffLXYz2XOjFUfr1Abp69TtSRv0u/7bY74km6x/aKDRyAe2yvmDH2ENt7S9rcofLfR5tI12vLe/268m7p3l/42wckf/vdtrOKtyTdQRRd3uUgNc8NXNWN/KNG1BEHGg0kf7MoOKo6ttZ8rKLQcHqnQvGM1w/HB8jiKdWJPpm5AWmDvQqDA0t32a6iTNcVSDerv7pB6VXSfIRTnVtp2Mz2bMnGbW3OsRY73Q4NVnc+usWb10eHe94jVHOrufJuQZR8k6j42cf2tVXnVgcaCLCfsj1XJ+aRg2IhGeK57OR/g8M5NXBneTOUMf7eKq8fLYzmtdUr14Wk/W3vK+mkJk/b9vZNzld9j2mcUX3aZFPd0nUiad/hnneFlvLJ1krA+2wvqWhrca7tNUYYWpCehptHVguZ8YA6yI3dCEmXE8TsM9LxL4iWbTt0dmajj1IA4oGCMDDcGtNk/CxE5fR8wEWEisTXCJXkB2x/psJcFgH+SlS8AzwGLGv7rszxFxAV6tfTQusuSV+x/auh7j059xxJ59jeIj0+pBxUknSV7U8OPbp7oFCm/DIwh+3FFOotv7Sd3f40/R0/RgTGLyeKpm6xXaklm6QrCOWAc1MwYzNgB9vrZYy9xfaaTfyEltSYmq1nVdc4SfcT1e0PUuq8M5If3Q1Qbyvw9Bwk/cDV1aN6DpJut71aQ4Bs0prUq5B0p+1VJN1O7MP/Djxse/EOT63jUBTf708p4A7s5wqF1qMwp3Hp4/YaJN0ErF/Eq1Lw/TJCwf2esU7SqotI7pIesb106XgCcU9ZephhPQEFmfljxb467b9vyFkHFB0Fj7F9U8P5jwBftb1Vxbk0K3a2M9vmSroFWNstFjxL+uJwz9s+paK9ni0OasfvlLSs7QfS42mAvYlcy0PAj9xGQVtVSPoUQT4r53kutP3bsZrDUJA0jwe3Ju5pdNsakubQX0c6DEnHktpW2146+aFX5ca8appDmRgl4PuEMu+V0BliVJpXV5H06oKkh2x/oAY7iwF/sf2GpLWADxJklZcr2qllX6g2RUZqjNFNRmZph+AyluiGmFbaa36diFEeRfwGNwEeJTpUdaWQ0nBIvs78wLW2nyqd334cEFvvtr1SQ95kzAW+JH2HILDPQKiLF7nv/wLHdeK+nfJnNxMEyr+O9fuPFobIrxfIyrMX+SFJD9KkULVqvLOG/N2nSR3xbC+iNjriSfodEcs7j1B2f5YoqHjfsANj7IPAsoRf3nFeSSuQ9DjwE9u/LJ271PaGFe1sTgil/UvS94AVgANcoWgq+QX/cRSivI/IqV6Rw+Eo2biBUOm+OuXdVgMOsT0sH6yJnbb8bUlbE8rSKxBinJsB33OPkN7bjQOV7BxKqB+/TuwTlgX2sH16BRsfIArOCl7LS8C2th+uYKPlfGzJxlTABkwu6NRzgmF9MvMoQtJxtr881HGmjf1s7zfUcR+9AdWk7lyyNzcxsJLKyTD2pgI+QGzA2w421DCf79g+qNPzGAqS/mh7iSGe+1NuMKRd0qmkvwBDLjzdsCjVeW11+3UxGpAkQi1vK2BD2/NmjCmU8CHuMeVNduX7TR2QNBFYHri3KlEmJTQnU6lm4PPktpDpadRBbuxWKCqTLwZ+TSS6XnYFlfpeRolE9lvg5wSZ+DzbWS3yJF0E/AO4Dfg40bpURDC4UitXhTrnRrafTcf/QxRg5RboNE3+V036t4MGMl7jGtuV3QyaId0zVyE6qbSklJoCVMsRynrLSZoXON0V1VokLUoU461OXGtPEqovLamEtAJJH07vvzsDbZ4gFJU+64xuMyVbd9heteYpjgnUhgJPXaSSRErYiwi0zU8E7h8n1qOTM23MQrRtm58I8p1Zeu4Y27vk2BnG/hVVgjvD2Hna9oLt2ul2KFqbXgt8m/hedwWmsb1zRyfWJhRtX48i1sajCd/xeNs/6OjE+pgM49nH7TVIehT4YJF8Uah33G97qV7yo0YDKVm8BINbiP7J9jc6N6t6IGlL4GCiIFGEute3bf8mY+zdtlca4rlayExVIOlUQuHyYgYrO7cUG1Mbajpp/CnAL5xZHNpNaNPvnLQPk3Q4MCfRAWhjYE7b22bYmJXwFzcm9rgmVDAvIhL2I5LZJP0MWBI4FSiIUvMThME/5sYcJK3r1K1TIcpyOKG4/RCR3Hwhw0azIv17iTiZPA6K9PtryPAYz+vIcCjuBw2xmvur7OFrmEPbxKjRQLeQ9OqGpOOAo9zQ0bQFOxMJkvfCRGHExcAyttevaKetfaGkxYF5PXmXsDWB52w/nmmnrhjdtcSaWtxLtiS66nX93qlN36LtWFSycw7RlXsGgnj2CKG4uhGhYrlNho2242ulz7MJsEAbn+dAgth4L/Bp4GdOHQiq5pq7EYpCqU8AJwDPEx1ovzSWa0jDfA5yFxWcpPtIQVa80+OoSK4dSHqPozP8Qs2ez81pSFrQ9tM15O9q64gnaWXivjUbcABRWHOo8xThf0Iows9MiS9Q/N8J3kBVpP3G/cT8v2L7v634Thro1rAmQV79CfCDKjmj9L1+BJgduAW4G/ivKygEKzpQHEVwaB4C5gY2d0MXggw7bfvbkpYifCURxTGPVJlDJ1FXHEipOEzSZ4kuQ3sCN1X8O/6OEHe8Ph1/DDjQ1Tp5NMvHfsGlgqUMG5cD/2FyQaccwbC9nLoqqUHJXdKBtr+bO486MPXIL5kyoYZKjsbjTPxqhOMc3DPCcRZUD7G6bRt12ukl2J7Qro1EavwB0Rp7Qjr1FhEcaEs1zKE8fr+ipWI34KtEy6Zuxd2SdrJ9fPmkpB2p9htdTlLR8kfADOk413mcinA8NcLrOoaar61uvy6KYP2mTF7tVOk3qqjA24pI3MxBKK1+M2es7amqvNcwc9jL9qEaotWqK7RYJZx4S3KyPVPuQNuLVHif8YyvkciNALb/KGmezk6pPTQk9HYELgRuBfaXNMd4SOhl4EcpUfu/xMZ1FmCPCuMX9YDC/wlEUG9B2/9pYS47Axcq2pStSNxvs5MEdZGW02btSGA14t5zG5EkfiJnGi0+1214IwVjAFB0b6g6/9dtvyPprUQefZEIkFfFn21/It23J7gFpUxJO9g+seHcwba/nWliWsLfmRp4V+n8K0SleBUcqVDEuYrBCuL3VrTTCcxtu5yIPVnS7pljTyaq6yEIS3MSRIyNgV8SpI4cnAFcQHTN2AKYCTgb+J6kJTODGScBfwTOB7aXtCmwle03iN/9iEjBvqZPAdmKRCU/vJmdGXLt9Di+AexD/B7OAn5LBMR7FgqVk0eIFtbnS7qUCu3UxjskLUnsKxZm8H5lTNvElTDufNwexhnAHYpiOYiE+JnJB/j9WExAzbs+QYcTbLa/rlB2LFqSH+eKLUS7FbbPUigCFYnwvW0/nzn8XcM8N03VuSRCyFcJQjXADcCvnK9u9Hj6N2GEuY00j7KajiT9jYpqOgmrAl+Q9BSRVCuu417oftCO31mOUX4cWNn2m4mQkpuYPYdQG/tYcT1KejfR1vkcQm13JKxve8nJJif9huggl1tAfSBJwZVQVXuOuD9uQuR6Ns6w8RLQSJyYjyAfGRgPRfodX0Ogv450Id5UCJ0U8eC5KSXUxwK2txvL96uA1yRNC0xUKME9R6xfvY41gS8pxEneoPW17x3bb6XfzS9sHyUpWzERQKGAfq2jAKbVfeHPiOKaRvwzPffpTDt1xei2J+LHRxC/q98BX2rBTifQjm9RRywKQv18i5Rrfw74RMpZ3UK+j3Iy7cfXis+zbpuf59PA8um3sh+x7i5qew+6OGdcAdsQ98WvE/mSBYjca0dg+zsKxdMlgOlL528aetToQKEsexixXxJwlKRv2T5vrOcyGkj5sn0Z2BfeSKgZj3j/tv1c+r9dIZYLid96u/m7N23/s8jzFNNsZUIeKJL9N1DVv/me7W9JusgVurl2GV6z/TlJewE3p99BK3/Lt9P/GxA++WWSflTRhmy/JmkH4NjEpagkLgU8DHyUKK4R0Sm3FV+wDn/7BULtfWqCq7RCj+TLoKY4EAMxrA0IVeTG320OZiqIzAC2b6jCh0ljngDayscC87cRd/o8cGh6/B2ii0yBdYlOBWOGPpl5aOw0wvGIsH3PcMeZNi4Z7rgC6iBW12GjTjtTGvYgggEr234SJhF+jpW0h+0jhh2dAdvd8l10+0Zrd+ACReuF4ne9EkG2+WyukRpIp8+1S2QfK9R0bXX7dQGhEvNP4rp4Y4TXTgZFNfXmwNMEoWR/4O4qJEFJa9u+Lj1epLhfpONNnN8+r6i8uzv3vYfBOZJ+BcymUF/bHjh+hDEAJDLDd4HFgQcIFZ6hyEfjGXWQG7sN91Cq/k3/b5D+jZeEXlNImp4gDy9OJDFPtL1WC6YmJfYdLY3+0iKRGdt3SdoVuJqo2vyEK3SAUCgpHkS0TCwH9Kp+j2cSainFevp54n6YUxk9o6TliY3rDOmx6D1i4o2Svkt8hnWAXYCqe4C7FcphxxO/tX8TxPCqeFLSlYRSyXUtjAfYVNJ/bJ8BIOloKnwftm8k/iYXOKmftIEPEsHwtRkI6jgddzv+LukLDFbgyWppSj2kEoCFPaAS81NFW70DJG1HkBRyghmL2S6SDxdK2ge4TlKV9np3EYHsZr7hbBXs3kRahQAAIABJREFUvEz8LSZT1JP0TAU7PQuHatA+6V/PQ9IPCIWne4BDFco5x9OCXz6OcS6RZD2BgQB+JzEefdyeRLqfX8FA6+edbRd7wWx1mTbn0E7iYVSR9tEdaUU/GkjJrhls/9uhXPUiEdNaUtKrmQmTP0la3/blDbbXA3IKERtxLJFAOiYdb5PO7TjSwPR5lnQFJaRhcBywpwer6RxPKONUwadqmEun0I7fOatCzWgCMF1BRi8XuGdgYduHlE8kUvMhknJVVP8jaWVProy9MrHfbQUr2S4K547QEN2JmuBbwDrAt5zUSiU9OZ6K97thDUnz6K8j3YWfE4S9eST9mNS2uhMTUajl75aIrSRC2uHukDIzscZNRZeQ9GpE212SEt5UdI/YlgHCcKVCqUQePppQwScVUFfdF87rJirTth+UtHAFO7XE6BI5b1LsJF3HuwA/rmqrA2jHt6gjFjUJySe53I7W5RV9lDria3V9nqltvwVg+2VJnwaOk3Qu4df3NGz/ORV9LEysn4/Z/m+n5qMQM9uNUAefSAgy3EZnYsrfI669F9Pc5gauAcYFmZno4PoQQfaHWDNPIooJh0Uil85h+yfp+FmC4CjCF/9l5hwEYPvSdPxPoJX83cOStgKmSvmzXYlClGxIuoRh4mS2c2LqtxHk7F7OrRffyaGS7iXEapp1wBkJzya+wjrE/nI6qpOIpegmujWwQzpXlfNzm0NBf1LRdPpcVVX12/K3JR1AFEY9zsB11iv5siy14UxcolD/fh34arqvVo0bPKHoClLuQpEVG5O05xDngcpK01dI+qTtqyqMmfSWQzxudjzq6JOZh0BRuTPU8VBIwdMdCWfmSpfaz0j6nu0RKztS8mYHgsTx3nT6WYIkd6LzFSkmoSBSS5rH9ostEqvbtlGnnSkQ2wDr2H6pOGH7ibQBvIrBbbd7HV2dvEyEh9UlrUW0fwC4rCCQjiF6gdxbJ7r6ukiY3/a6bYzfkVCIORa4xPYbFQIpBQ5jwNk9n8GO7/fIDJzbviStaR+0naUKPYytwxIh7xWiyvAHtq/OHH4qEeQ7imjt8XN6R3GgTtRBbuwqjKfEXQs4hSAi30wE+t9PvjJUGe0q/DcLhsxIBGdOlJQbDIEIKO1L+CNrEdXhrVQSz2j7tNLx6ZK+lTn2OaDY1D1felwc9wr2JtaDB4GvAJcTpLMsKHa4B6Uk3S8TGXmWFonASxH33q8R18SlwNm2b6lgY1PgYknvENW7L7eYMDw6BZdOBs5wayqrmxOK5h0LfreBZgo8uSoMdZBKAF6VtKbtWxL5+P8lO+8ov1R9OkkTbL+Txv44BZZvIhS4c/AI0U7uj41PVCQhnwosRKgPNOLMCnZ6DjUFwrsRnwM+5FDHmJNQUswqoJuC8JbtYzs9iRLGnY/ba5A0i+1XFF1TnqAUaNeU0y2lKTS0wicAOb52F+MQQhWwUFw5k0gYz0Coxe6dYWN34DJJWzC40P/DhP9YFSt7cAvP6yRlkUJSYedCkqatwc9rW00njfuzopvEmsR1dKt7R92oHb/zRgaIVrdLmtf2Cwpl5ZeGGVfGnxWKW6cURWeKVtpfItrD5+BLhADIu4C/pHMLEHvdL2XagEgK70nssWeRpIL8ROZ+1/bhCkXoI5Kvui+9EeMcEf01ZHiM83VkRNg+Q9GGu2hbvbE717Z62YLInOb2D0UBfEfgAcXI1wkxk3GBtPYtx4AK+c2u2C49YTtCBOLHtp+UtAgDxJAquFbRDer/SvfuKhiuWDqrSL+OGJ2kBYDvE3yBCwgV3/0JsvdZwwztJrTjW9QRi4Iglc/sKOabFJeUtBiQq3xYR3ytrs/zuKSPOkQgii65OygURnu+OELSBkQh9uPEGrKIpK/YvqJDU9qNKIq73fZakpYiOnh0AhMKInPC3xkf6v4FykIYEJ1cc1VvdybyDwVetD2fQlzot8Q1lYP5JP18qCed37m4jo54h1V8fTNMm0jVqyu6HgyC80XQOokfFA9sXyPpk7TGFdiCuEYOS4Ug7yGKT6tgN0K19gLbDyvEJ68fYQwwqePQfAwWY4JQ/Z4xdwKSFrD9TDN/G1gs/6OwBfGb68V8WVHMsRewDINFtqqSsb9DxMf+meJLr1EqIMvE9oR/Vvyebk7nclBnUe7thCjnBIKHUKVLkYd43Ox41KHW/PfxCUnr2r4yPZ6NaA2yMhHM3cNNFJua2DiBuNHcSZBPb7S9Z3ru3lRhMZKNswiVqFMYCLbNT7RSm8P25zI/T2M1iojg8vLEdz9iMKkOG3XamZIh6SHbH6j6XLdimGCeCHWYfrHFCBiPQeFevy4kHQcc5SYV+5njpyKqAbckHM/rgU8ACzhVWWfYuM/28o2Pmx1n2rvN9oerjKkTku4vJzRz19LxhuR07kC0UxWx6T2hxUBsV0HS1whiYlkdZUvbxww/snch6UHbH0yPpwbu7NR1Lemjwz1fBEQz7Nxje8WGz3aP7RUrzucQ4B9EYN4EMW124CdpPuNq3WtEWgcetr1Um3YmfQ91If02jwS2dkZ3iQb//11Ee7ZbSYGnVr5LSUsSyY7Nif3WSRWKY5B0IfDlhoDvuIekkxpOfbtEKjnD9scz7SxLEOuXJPbI29v+QwoabWl7yCBvycahwFW2r2k4vy7hQy2RYWMz4EHbjzV5bmPbF+Z8nikZdd37uw2NfmIr69B4RemevCtBYLyAkjJZp9bX8ezj9goUhUqfJpS6nyo/RQS7x223lFwolGqeI0g0ItR33mP7B8MO7GIoWrWvXMQZilhBIlDcbHvNTDvTAVsxUOj/MHCmW+gWo1Ah2tz24+l4UeC83H2SpFOBpYGLgVeL866mYIOkCwhCd1lNZ0Xb2Z3Ykp0fED5rkcjamGhXWrWF7RSHtO/4NvAZYJ50+gXiuz2kyppVShYDPOtQeK4yl30bTh1j+2/J7qG2c9rJl+1tRCguLmz73VXGdiP6a0gexuM60mtQFMd8zPY/0vEcRO621rhJhfk8SZMcSK//ZiTtRnQ5Lta+zxIt3I/q0Hz+BcxE3KNepxqZo8jVX+fo9lM+vyMhPJWbq28rRifpeqJY6DaCgLUuoQy7R9V1rRdRRywq4z3KxUrDva7t+Frp8yxB+M472H6s6ueRNAOA7debPDef7Wdz7HQrFAqZG9r+UzpejBAVaytm3sZ87rK9ciLVruoQonrY9jIdmMtPgGUZKGb4HPCA7ZyC1K6HpNsIFeVb0vEaBPF0xDy1pLttr1Q6/q7tA9PjO22vkjmHP1MizzbCFbopt4t0b5jb9u8bzr8f+JszuqtKWpPwP7cg9lRl2J3rVJGNdB1MtP2qQuhxBeBIDxSIVbE1FTAvJeFX20/XNtnh3/uLBAl7JQZ3xn6FKObNIpane+S6tp9qOL89sI/tLEKzpPOBr/ZqvkzSVURX2W8SxQxfJH4Xle6Hkn7twYVOMwMX5ebN6kC6Lne13ZaAadpnfIbIn1WKr0t6m4inFd2OXyueAqa3XalTSrvok5lLKCe+Ein5eULFZxPgo7Y3zrDxgO1l0+OpidZ4cxHEtNtzSGSS/mB7yarPNXntO0DjDXx+giCdFUyqw0addqZkDEfgm1LJfX300W2Q9HtgceBJghxQBMiWbcHWdISa0ZaEmsG1trfKGFdeyxoJHZXvFZKOJRI/5zI4IZhdqamo9DyESEKJCoHDIticxkAQvCcdj3di45QASRM90Kq1OFeZeN9LqOO3OQpzWgR4riAepIDovI2b4WHG/45QHTsPuI7oKnKw7fdVnMeT6WGxSSkrUkwRPqOki4BvtBNAUbRQ/YUnb6vciq2PEkHRdYkAy29sn58xrkjOqfR/gZa/y7Sp35hQ6i+UyL+bsy5JuoEI9t7FYBJf16rQJiLMULDtqmoOfZQgaVbi2p5EcgF+65Jq13iHom3nUsTvtKNtO9uFpJcJlW+Ie8NHSsdd/VsfbTTck6GBPNHJ9TUlZshJwPQxelAPFsmPFdRQYDvUuV5C4/xVakHZbH82gq1FgOcLIoVC9erdufuIkp2PE91eniDuVQsB27mkkjzC+EbSKVC97Wgi0u7PgKLyzcD+BQGugp3HgOUa9lcTq+6PxhJ1+Z2SVkmvvysl2dcFHrV9eR3zzEW3+nnpWljM9kOdnEed6K8hw2M8riO9BknbEoUE5xJrzGaE6m8rar91zGfO0uH0RPHLHL1OcJf0APBh26+m45mINuqVciOJsLQf4QtMTYcKJBRdAS4A/svgLhTTAp/NJRK3G6Nr4rf9BVjQqeNVN6PbY1qSTnXFwqRuQrf6OnWgIA+XjkUIwqw8zLDRnM8FhMDG7sDahBjLNLbX79B8NgXWSIc3276gE/MYDSgU/k8FZk2n/gF80RmK9pL+ZHvxJucnAH+qwGuqJV+nEGb5JrAwg4mz2aqxks4mCipvajj/EYKEOiJvoDRmB9sn5r6+m5B8jOWI3M7JRFHIFraHFexoYucbRKecF4BiHa3E46jpe900J8c2zPj1gZ8BGzh1r5T0bYK0vp7tvww3vmRnJeAiolioJ/JlZWhAZKvM0byr6lqhKD6d0/YuKSZ0GXC87cYipuFsrEQqXGbwdVHl2souuhjGxk1EEWfX+4kjoU9mLqGBADYocJsbyJX0qBuqwlIw9ZPAPM5TmrqdUIU+v7jI0iK7ObCn7VUzP8//Egqf33JSCZX0pCu0da/DRp12pmSUKiEme4oOVEL00Ucfg5E20x9h8sKNcuu4Vm2/iwiQnZrx2oLE0UjgELCm7dkrvnczR82uUKkp6U/Ap91CK0FJTxEbimYttsY9sVHSgwzfkrIyUb7bkD7jsk5OaSIqPuAOVLWPFRrW9HKFYyWFkJrndDewekFiS+S2W3M3fZJWBh4hWjAeQASbDrV9e4XxzxSJAEWF8qaEutN+U1LhQtpsLk8oD5eLSLIDCIrK7MWJNamopK0UlEl2ngLuA84BLi4SUp2AQrlkO2AD4GrgRNv3SnovkRxbKMNG0+CWu1iFNu2jGjEToWQ6p+2ZM+0sRVRklxMcF1ddm5Od+YA7bP+7dH5Sl6OxsDGM7e1yg0wpob0vcBXx94AouF2HIC2N6Hf1OtSkbSfQybadbWGo33iBbv6tjzYSsewZ28+l446us2nvtC/wdQbaob5NKLT/cCzn0kegzkKo8YZUtHc0A51DtgS+Znv1jk6sDUh6BFjF9r8azs9KrM/Zimc17COm9oBC9HRAQfZ9zPYbQ4+sF4mE/S43FFZImgd4xRXVphUqip/1QAei2Yg291XbnY4Z6vA7Ux5kPSJ5dzWwKlGUvg5Brvlxho1dib9VVgJ2CBu1+XkKlfBNgAWIteoPhAL5K2Npo5vRX0OGx3hcR3oRkpYB1kqH17lB4bDT0DjoKpNivCt7oJBneuAuV1QlTjGtPQgC8dvFedt/r2inUEJfxPYBkhYgVNHvrGhnLUpdKGxfV3F8WzE69bDYS80xrXZjUY1qpCLuCddBftxV0iyESurjDeeXzSFbNrG3JrAK8JBTcWHmuHEZ01III0F8joWImLQJjszTtnfp1NwKpNjTrMCV7mFBgG5Eyg0eYvub6bdGRZ/7GOD/2f5ew/kfAXPZ3jnTzu22V6sw9aHs3E/EXRvXs3uGHDS5jUFq0w3PZRUUSlrb9nWl39cguIJ4WadQ8PgURTLP2j6xFdJ54iusWtWnaLBRx/f6buDHwHttr6coAv6wK5DNFQXhvyKEf3Yk1pINXKEQW9LDycaDDJC7eyaGXvxWJf2WED/6K9HhK0uZusHWocAswIqEUFclsrmioP1bTP63zOYISToCmIZQmy7nhu+tYONkYFHgCgYT1Ct1LesG9MnMJSiqGX9KOLBfIyrkC2LNJDb/CDZOB05vdJ4VbWeOzSGcSlqYULBcCyiq52YjNijftv1k85FNbc0PHAE8Qzi191clftVho047ffTRRx/dCrXfMmx+otVk0T5nT6AI6Jzp1FJpBBtdR+KQdKvtNUZ+ZR+NkLQE0e7mmYanFiDUr0a8JrodirZYCxEbJoCvEGSbZsHOPkYJaq6QPWZKQYq20p+w/f8k/Q+R5PsG8CFgadubZdhYw/atkqYbS+JD3aiDcCupKbG3ysY52ZmljgS7pNWZvCK5UlBd0o1Exf15bmihKGkbd0hRaSyRipt2I5I+5wCHO6MFmKS9iYT52URnHIgEx+eBs20fnPn+uxL75EeI3+Zuti9Kz2UFD+uwMYL9p20vmPnax4jg5csN52cnEmRZHZF6Geqytp19jB7qWGdrns+eBNnty0WMKxG9jiUSgm211OujOuoqhBqPSHHaIwnlKwO3Aru7ovJwNyH9Bj8B7OzUDST5j8cSBK/DKthqax+hweIiR9n+Ru57N9iZG9gLWIZQuQTy1ZEkHUfcf/6v4fxngU/a/mqmnaOI62RBYGWC0GuCkHGn7aYJ5G5DG37ng8TaMh3R9XJ+268o1IjvyMyv/JO4Dz1OtM8+t5FknmGjFj9P0m5E17QbgfWJQs+Xgc8Cu9i+IcPGrsnGTa3a6Hb015DhMR7XkV5FKlAprxFj0lK8yTzK+94JhNrvV8cqBjdaSP7FFwk1Ywhyzcm2f1bRzh3OFPUawc6xBJlkbdtLpzXgKo+xsmu7MTqNE7GXNnyLWuJIaU/8eyK2WHQtOouIjWXFXSVtQShhvkiQjb7kVMhTIS42SXVR0k7ps11AiOJdUiFGNy5jWmourjQJtrcbq7kAKARY5nJD0b9CFfWFKuTFGubyL4YXPhpzgZzRQDtEYkVHgBOIfdj96fRyRLfJHcvFEGMB1VCoJOkxD9HdZ7jnGl63v+19h/h92RXEyzqFlBu6khC7+R/iPnx/VU6Gouh4HaeC6hbnUsf3egXRmWof28tJmhq4r4XP8xFiDfkdoVRdtQi7sopxN0HShkQ3rQWAowgy8v62GwuYhhpfjs8I+D4hMnUlVO5SfovtNXNfP4SNZp3JnBvXSjZq6VrWxO6ltjdsx0bl9+yTmQfQ5Is9xvbfUmXEoc5sNaIaVK8kLd3ExkVVbDTY24gka2773Z2yUaedPvroo49ug9pvGXYWcIbtS9PxY8BxwIzAUra3zrBxsu0vtfL+Q9ibnggwNSYEqygzHwm8G7iQwVVgXV/t2WlIuhT4jlNXg9L5DwIH2v50Z2ZWHxTdJ74CfDyduho4wfbbQ4/qo25IuppQQ7w4HX8G2NX2x0cYtyawaEFMlXQeMEd6+kfOVCspEx4kHQ38zfZ+6Ti3Q0rRUqiWNmDdAElzAX93i5s2STMC7wf+XIUAIGkv24dK+nmz523vWsHWacBiwEQGKtVdxUZdkLQaEdRYmmgLOhXwarcHeyXNAexJKAqdAhzpahX2fwCWsf1mw/lpCVWhEbsHpdc/SCgE/Dsl5M8DTrN9pKT7bC8/RjaGUroRsKTt6TI/zx8I1ah/NpyfFbg79+/Sy2gMWEqdbdvZx+ihjnW25vncRyQPXmo4PzdBMBjxXtBHvWiXZNFH70HSzkR8dqZ06t+E+syxFe20tI8ojZ+0/rfjx0u6ilCv+SawM0Gm+pvtvTPHD5mQlPSwMzsHKZTvh4TtU3LsdAo1+J3l73OQb1dhX3cfoYb0CeBzwEaE6tVZhGLzv4YZXtioxc8ryNm23057q8ttf0zSgkSuJtf/bctGt6O/hvTR7Ug5ycOB9xLkl4WAR3Lv7aMwn+sZIKS9RXRLOcz2HzoxnzqRiNoFkeNm2/e1YONgIl7zfwzOJWSr0iU7hYpjeW2qRbihFUJHqzG6XkcNvkXbcaRkZwJBpl6f6CI9UdITVcjgkiYC69l+TtH96FQid3NBhZhW+Xq8C1g/cVFmAm7PJbL1Y1pjA0nXAds1+jTJ9zmpCsGsxjkdADwHnEbEQrcmVOd/MNZzGQ0oClHmA85lsDJpFVLhokROG+D3blBSb3N+I+5Z030PYFfC77iAwetZtqK+pMuAo21f3nB+PWLfvV6mnQnAZrbPyX3vbkLi621FdHy4Oe2lPubqgjknEt2gLqNF1VpJ+9H+93qX7ZUb1oTs+KwGihtEFBK/SeTeKnX/lfTT9Bkupg2fq1eh4QtoXJEL83FCUOhauoALI2nm9P61FHFIeo9Tx8exwtQjv2TKwVBsdNvPD8GCnwyS9iJupGcTrH0I1auzJGWpXimUsz6fbNzRio0mn+HiFFxeLL1HdvvdOm3UaaePPvroowuxKrC1pFaVSN7nRGROeM324QCSbs60UbfqyWnAo8CngB8SG+OqhTWzAK8R1eUFTAQk+xge8zYSmQFsP5iCdz0P2++kDeQtxHXxWJ/I3BHsDJwh6RfEvesZIKeQb39C2bHA+4AvEaSI75Ja9WVgKg20mP448OXSc7l7ljcVimrzNSPhdoI8WwWJaHsw8P+AA4j771zABEnbOqNtYkrQ/TzZ+B7RyvYFYGFJe1cgTxT3+TrUJVYC3t8qIbuAQqn+ICLxUy6uqaJA8wtin3Vumte2QFcrlSjU6zchips+2GLw4R0iadtIJngPpZZXGZhQvL/tpyR9DDgvBfCbKQSNlo15Cb+kMfklQoUgFz8G7k3kp6IDwoKEcuIBFez0Mu6WdDmD23bepaRK0KlgWx+jgjrW2ToxTSORGSAlcUfsaNZH/egTziZHqbirUNodhG73LUeC7V8Cv1So5JFDEB0Cre4jJk2lxfdtxJyOVrO7OZT1bkwEkVzMOMxzEyrYucBDdDZJCdeuRU1+538lzWj7NYKQXNielXy/07bfIdqmX5XWhfWIxOBhwNwZNur086YmEsPTkbqn2X664npVh42uRX8NaY7xvo70GA4AVgOusb28pLWAL3RwPpcyQEAhPf5Iun9O7Ny0WkOJtAVBzH6q9NxMtl9tHDMCClXmlUrnDFQlDb4paao0tiicrBIDGQ47jfSCGmN0PYuafIs64kgk3+IISeem/1+g+l54qoLIY/vOdC+5VNIC5Pu0ExTqyRMAFcR2269KqqIUOq5jWun3uhOTd/obawXZdzXzc2z/WSFC0gls1FCUcayk+4FxQWYm4v5/Z/A9PyufXMS9bD8BPDEakxuJyJxwD4PX+W+VTQBV8hm7A5cplOGLXM1KwIeJ7i9ZSHnYvYgYcM/B9vPAT0vHTxMFJVXxdPo3bfrXCooC5na+11clzcmAj7Ia8M/hh5TezH5XhfcaDkURTlkNvRWfa0wx1P6qQO4+y/Wq/W8HLEV0bij8zUpcGEnzAgcC77W9nqT3EwVdJ1aw8QEitzxHOn4J2Nb2wxljP0N01zo6Hd/BQPxlL6KgbMzQV2bOhDLb1aoG1as6bGS8R3b73dG0UaedPvroo49OQ+23DPu97feXjucoKvkkPWJ76QwbjxIJnqaBnKrVdEVVoKQHbC+bEi03u8U2P31Ug6Q/DrXuS/qT7cXHek51IwUgTyEC3SLawXzR9k0dnNYUi6rVmppc1fP/nFonS7rV9hqZdvYhlDFeIgKvK9i2pMWBU3LspADiJ4BDaBK86/YkgaS7CQL4rESQfz3btyu6vpzlPHWP+wky4qzA9cCytp9QtFK91hXbVA3xHgu6QivWlCTYtd2qXUm3APsCRwCfJoIDE1xBdULS3bZXKta0dC5bxaUTkPQOUcn9FoMDNNlV9pLWJYjcf2RwgmNx4Os5RPlk5zpgz3JyVdEC7dfA1ranGiMbJxLqJ7c0ee5M21vlfJ70+tkJYnS5I9JvXUElqJehGtUHuhElMtUUjzrW2ZrnM6SSzXDP9dHHWELSp21folDabUZCayVx1vWQtELVuEEa15Lqi6TXgD8Rvs1i6TFULA5Xakcs6bcEceivwHm2F8scfyOh0ndnw/mViTbo/5NpZ9I9TNK1LilUd/v9rSa/czrbbzQ5PxehGDdZsXaT1w7pn1dZ2+vw8yTtRnQruwP4CHCI7ZMSweb8nOuiDht99Cam1HWkG1GKBdwPLJ8IPbUo9LY4nzMJItLFxD12Q+ABgrR3ru1DOzGvViHpSSYnZ5OOCxLit22fMcbz2ppQ+F+BiD1vBnzP9rkt2JoboviywpixiNFNCb5F23GkIexuAKxh+7sVxvwO2MYllVdFYeCFwJrO6BQm6SmC5CTib7KGQ+l5ZuAWV+iaNJ5jWulvfTNB3pwkfGP7/DGex5A5uE7l59Lf5mhCFNFEPvhrtlcf67l0G0aINbWVA1CbHTTbgaTpCDHND6RTDwNn2v5PRTsHE3HB3zBY9TpbUbhT0IASMQQJeRrg37Zn7dysWoeik8VRxHf6EEEY3cz2UB0p+yhBgzti7U/kDSehai5Y0Xl9N9svp+PZiThQFWXmx2y/r8r7NrFxBXASsI/t5ZK/c18VnzGtEfvYvj4df4zo9D3iGiHpVuDztp9JxxMJUZSZiHxcVge2utAnM5egGtrVJhLZpxqJa4ngdlXOBVyHjfT6Oj5PXS18a7HTRx999NELSAGpsmpkFukrVTht44aWconIdqrtVTJs/Au4i+ZkZrti2yNJd9peRdJNwC7A80T78Srtt+YnnPKCJHEz4RT+peJcliMSPxCE6vurjO9FSDoLuM728Q3ndyRac3+uMzOrD5LuAbay/Vg6XpIgbjZt8dtHvZD0BdunS9qz2fMeob1SnYR7RfXxewh/99V0bklg5iqECknL9eL9QaU2UmooYMkNtmlwW6oHy5vcqgE7SR8mguE32X5R0rLAt4GP2F6ggp3rgQ8RXWvK7ZU2yrWR7Nxje8Xy59Iw7cCHsHETQXg/gVjPngO+1KkE5lhC0UpuFQYnOO5yBSX8tJ6/5VBCaHxuDdu3joWN0YRKhWR99C4krU78zme2vWDyIb9ie5cOT62jqGudrWkub1NKnJSfAqa3PS6UKvsYH0hk1u8yWBUsm2TbjdAwxWmSjredozbYdP9QYKR9RMlO06Lwkp3c4vANiVjDAkT8YRZgf9sXZ44uoSaoAAAgAElEQVRfhVCpOpnBilfbEsmcO4YY2min7I8P8r/bTaBPKZC0ZGNcrCa7Lfl5kpYBlgYesv1oi+/dto0+ehfjcR3pNUi6BtiY6PY0F9EafCWPcUFfaT43Aes7FQAlEuNlwLrAPS6JnYwHJCLwjbmfK5G2NmVyRdYftvDeSxEkDBEE4uyOk5JEkGO+TlLRJUi5R+XMpc4Y3ZSMboojpdjCa7b/2HB+GmCLdgj7kmYkunQ+2YaNcRPTKsfJOzyPXxIqwd8rSKzp3rA/8G7bXx5u/CjNaWHgSCLfauBWYHfbT431XEYDkhYlPt9qxOe7jfh8I/426rq3apgOmoTCaa4wyIzAnsCCtr+s6DzZ2KE5d06LAM/bfj0dT09cg09VsNHsb+gq+f5uQPoNfgZYzfa3K46dm1CYXYbBPI6q3InVmdxPySpUTHNYiOiiOQ/hXzzmBqHTViHpUttZqt2KDkr7AkWB7Y3AD21nq0R3GnX87pvZaCGfehLwE9u/b2Med9leucGHrLQeqknBZrNzw71/6fgXtr+eHt/uMRY67EQryW5GHe1qdweuldRU9WoMbUA9n6euFr512emjjz766FooWocdTrRyf5FwRh8hnOIc7Eu0pfoxUBAKViQC3rtl2vhTVad7BByXKtC+T6hFzEz1dkUnAWcSSgQQbfxOIlpeZUGhZLMTA+04Tpd0nO2jKs6l17A7cIFCSaKcUJ0W+GzHZlUvpimIzAC2/6Bx0mq1RzBT+r9ZW6KcqsdHJW1g+7LyyUQmeGyIMU1h+/Ym57KT2Cq1Fop4xmS2ur2Fa7nV5esNz7XSqvCd9Lj4Y2S3x1a0gdwQmAjsrVC425FI/FVVat2v4uuHwhuJkPtHSV8nyLgzV7SxDTAVsafagyC7bFrT/Loajnaak/3GKtoYsggpN3lUh426IGkNgvD6DnFd/whYVNERaQvbt43lfDqBFAj/BpMHXysVG3QhjiDiDxcD2L5f0hSvetjuOlvzXFpSz+qjjw7hdKJ96IPU15q807iQUClE0vm2J/lDOUTmhMMIX/EKomAtu813Gblk5Qw7RUL4n8BaLYy/MxGavwZ8KZ1+GFjV9otVTA3xuNnxuEMqgDyOKKC7AtjbSR1QqVh+JBt1rE11+nmOdqwjtmQdbRt99DTG4zrSa7gfeI2IA2xNKOVWjSfUiXkoFXsDbxIkxtclTaZu3+uw/TdJe1cYchGxnt/D4L9TJUiag8jRnFU6N00FstAeBFlw5YJEl0h2x0raw/YRI4yvJUbXDOqgQuhYo644kqQPAsfTno8ymXhGiUDclvK4o/NENpF5CohpXSppfduXd3ge/0v8nf+kUKgEWA64m4iVjzkSefUznXjvMcKZhPJ0kf/8PKFCvWrG2LmHK7rNLbglOhwWHTSvo6GDJpBFZiby4PcAhSLqs8C5QGUycxpXVlZ9J51bufnLJ4ftRVp4365DWvsulLQvIb5TBWcQytQbAjsDXwSyuy4ASDqN6Co1kQHleAMjkpkVQmUHAo8DiwBfzi3AroDcmA5El4OHgC3S8TbEdbtJzXMaTdThC02QNHvJL5iD6lza1YCJqWigiJFVLWB9VdKcDOS5VyN84ip4QtL3iSIMCE7OE5ljZy8fFETmhLkrzqNt9MnMg3EpoUgzsfEJSTfkGLB9ZVK2aVn1qg4bCW1/npps1Gmnjz766KObcQDhrFxje3lJaxFOQhbS/X8ToiqwIN09BGxi+6F2JydpJicVtgpzOiE9vBFotTpzbtvlNuYnS9q9oo0diCRioSJ3CFGRO67JzLZfAFZP11LRQugy29d1cFp1425JJxDJHYikwt0dnM+UhssBbO/f+EQiJI+EPYDLJG3G4CKM1YmAwFii16+b5SS9QmxyZ0iPScfTDz1sEGYlAmRFcqSstFllU78B0Xr1PynZ8gzwgVYUHmzfqFDcW8L2NUmRoBUS227AjMT6eACwNhFsqjKXgizzOqGgMUWgDlJJem3biZ+abNTyeQjC6xZEEvsyYGPbt2igzVtHVLrGGBcCJwKXMM6IFbafaShsqRJL6aOPPvoo42+jkFzqNMo3yFb3+csTLY03IPzPswjFw9pINamAeVi1s3JBYzNUKWhMpOV9R3zh8JgnJdBVekw6HvPkTwdwDFHMeDtB8LhF0kaOduxZRct1+IvU5OfV4XfW6Lv20bsYj+tIr2GtVOD7DnAKgIbuJjsWOAO4Q9JF6fjTwJmSZgJaVnPrRhTKcrYvqTBsftvr1vD29xJF7P8g1uHZgOclvQDsZPue4QYThJ51bL9UnLD9hKQv8P/Zu+84Wao6/eOfB+6VCyho76IYQATMApJczIhrBLOAKIrgKrgoQde4609lTSiGFdRVV1AU0RUVw4JiIKOkS1ZRQAkmlBGJIuH5/XGquX2bmZ7q6ZoOM8/79eJ1u6q6Tn9nqOk+fep7vgeOo3zW9NLIGJ16VAiVVLtC6KRqqF8A8CkG76MMnECcMa3eVFaeNeXv5u3VBI9bWZEUttYw46nuR+5STWRoF626yHbdxLDGqVR1fTV3LUzQbwGScbWG7S92bH9J0ptqnrsq5W9iThNtOyyxfRyApAPaxQFs/2K6Ijo9bGR7Z0m7VOffpD4b6Irp7+0N23+v3ntmJWktyqSpX1XbOwKrV4e/X92HHmtV7kTbKpSiX3+bQ1P/YPtzkva1fSJwoqQz+2xjK+ARcxz72A94ZDXRa0NKn3Cg7wnVewK2/1T9+/s+Tt+oc3I78O6OiRuLyYeBn0j6GuX948XAe/tso4m+6xso18NGkk6ljCG9uM829qDc+/wG5fP0ZOoXqDpd0qt919XC96SsvDtUSWbuYPtVPY69tI92mqh61UQbA/88Df5OGmknImLM3Wr7GkmrSFrF9vGSPtZPA1XS8isGiOEtku5PWUL6/OoLzb0pHeRXUqpG1ybpnlU8G7DyF+N+KpxeUw3ytasg7EJZmqmvUFg5CeV2Bv9COjFsHw8cP+o45slrKdWv2tfUyZQboDEcP5D0zO4kVUm7A//BLLPEbV9SDcK+jBUDeicBe9mey2DCnNn+wjBfr2luoEql7Q0aCAXgb+3/f7b/IulXc0lkBpD0auA1QIsyY/3+wH9TlvmszXZ7UOkGYPc+Y7iAmW8U3UKZCf9+T1NhZYEYOKmkMvCNn4baaOrnWWr7AgBJf7J9CoDt5ZJW733qgvE32x8fdRDz4EqV5f6sstrDvpTVUiIi5uKd1eTLH9FRHdD2N2Y+Zez1qh5cr4HSbzoPeGv1nrsLcLCktzSYtPfpGs/pnND4bgZPRr4LSe+y/a6aT/8sK1a96XwMJfllobtHR1LVQZLOBr4n6eXUv9aa6C821c9rot/ZVN81JtdC/ByZCJJeC/wrJSGgM3n5HsBQVwbqZPs/JR3LimTDvWy3P89eNqKw5oXntuz3aZI2ab+PD+AHwFG2vw8g6emU1bkOo7w3z1blc2lnInNblYA06/t3g2N0TVUInVRN9AugmT5KEwnEGdPqwfZ0q0eOXJW8PLIE5i7fotxD+yELc+L+sZLeSqnGbGBn4BiVSqm4VEKfye9tH9BADE2soAnw9+rvsV1ldSPmvuLAn6r3im9XbT0PuMtn1AwOAk4DflVtv58ymWJ1SlGiveYY0zA9p+PxbcBvmFuF8vbqDL+XtD3wO8p9q35cCKwL9JM03Pb3jqTjyyStNoc2qJLi30lZgXSVatdtwMF9/g3cLOkJ7c+QatJO9zU/djomvgCsoZULQ/U98cX24VW/oL3K1wtt9zXB0PblKoWh1mPlPNzaq5FVn+FPBh5K+Vkudv0VRdoeBezvjiK5VT/lLzXO3Z9S9fylrFy8bDXg+X3GMbAkM0dERDTnWkl3pyTyHSHpaqCvSsgNeARlJt8lwGqSPgkcSFneZMs5tHcMZWBlkKUQ96AM5nyU0rk8jT4T0SiDjKdL+ma1/XxKJcGYcLZvAT5S/RfD9wbgOEnbd8zMfhvwUuDJdRqo/h8eOn8h9qeaifwWyvvhnRWNbW83sqCGRNIS27c10NSGkjqTUB7UuW37uX20tTdlxZnTq3N/VU2yqUXSd+hdba9OLL2qhC+hfMH/PKXC4ELUxA2bptoZlzZg5WVd39Z1rFZliwXgv1SW5DuOlRMrls98ykTYC/gvyuSJ31J+vn8daUQRMcl2Bx5GSS5ofyc2pcrJpOq1IkhfN36qvvfmwCbAVZTl3Bvh2SsmrjShUdJ+8zTBcdY4OuJZNKt/zETS2rb/CmVitqQXAV+n/k3iJvp6TfXzxqnvGpNrIX6OTIovU5J13s/Ky5BfP0sy1LyrkpcnfYUx4C4JJd3aE8j/3faPZmmnPRF9CbC7pMuY+zLdANvYvnOpddvHSTrI9p41k4f+PsdjQKNjdE1VCJ1UjX2ONtBHaSKBOGNaNUh6AfDj9v+vqujStraPHm1kY2EN228ZdRDzaKfq3z279r+E8jfSa2Whpt4Um1hBE8rEhe8B60k6gjLhod974217UfIODqniuJL6hdG2ZuXf5/W2Xw8g6ZQ5xjNUtuf6e+v2HklrA2+k5C2sRUngnFXHfap7AD+TdAYrj6fXuU/1AEkfn2m7j0Jy+1Oup61t/7qKb0PgU5L2tz3b6hFtrwW+UP1ORFkF4pU1zx2Z+Zj4YvsiSX+i+huXtL7tK+qeL+k/Kb+7S1nxeW7KCrN12+j+m95CErYPr9sG8H3gTEk7uqw+BmVi/RaznVg9/3GStmNF8bKRrRaeZOaIiIgBSbqXy1JQz6PMWNufUkVhbaCJWaD9eA3wUNtTktYHfgk8vs5NwBkss/2G2Z82M9uXA/0kvk3XxkcknciK2e272z5nkDZjtNS7UipzGKCOObB9jMpybcdKej6lIsRjgCdV72uT6Ajgq5Qlr/cCdgP+NNKIBiTpu7Z7JeS2ncEMX0pVLe1Z8yW7Z7V/uOZ507nFZZWAdhxL6G9w/qABXhu483Ool0ur2ckLVgM3bBprZ1zaAN4haQ3bN3XeDKmqZPQzQDTJNqEsnbsdKydWTPrkj4faXqmiWVVZYmTV1yJiom1t+6GjDqJJbmBFEEl7UG40LwOOoiyt3Xcic9cEurvocxLdfCWGbtREI5J2sN1z5ZsF4EDg4XSsOGn7fElPBd5Rt5EG+nqN9fPGqO8ak2vBfY5Miurv7q+U1QNinvRKKJG0KmUC+RHVv73UGffqx+8lvYVS3RNKdc+rq5jqFG1pJ7N1q5vM1tQYXVMVQidWQ5+jTfRRGkkgzphWLe+03S5shO1rq8n4SWaG70p6tu1jRh3IfLD9oAFO72slyB4xDPx9uWrnuGrCwjaUz459PU3F/5ptXQpsUxVUw/YNfZy+xHbn58XLOx7fcy7xDIukg+l9H7mfVaTp+D7+V1ZU4a1r4PtUwJu6tueau/Fy4Gmd15NLpeddKUU9aiUz2z6X0t9Zq9qert+z4El6LuUe6P0oE/QfSFnl8ZG9zuuyE7CR7VknvPWwdcfjZZT3tOX097l+MfAh4ERJr7J9Gn1O9KiSl0eSwNxJK79vRURERL9UKjD/mZIgcRpwqu1fjiiW5ba36Ng+z/ZmA7S3P3AD8F1WnmE4a/WKWb5ktKsyHGH7+rnGF5NL0gN7Ha+RfBgNkvRE4JuU97CdbP9txCHNmaSzbW8p6fx2UrykM21vPdu540rSfW3PumxVnzdDhkLSB4FrKZUCXk+pjvoz2/9e8/x1gHXctayTpEcAf3K1LNcA8X3G9msGaWPcqSwLdVm7ek/H/vWBd3RWKprvdsaljSgkXQI8YsBBtrHT3R+eaV9ERB2SDgM+1N0XWewk3UFZXrX9vW2l7/51k5CryjdXUpZJP52umyy2T+wjpnl5r5d0he31G2jn3bbf2URMC9k49fXSd40m5HMkAiTtafvTfTx/M+CJ1ebJts+bw2v+I2UJ9idQ+imnUgrP/BVY3/Yl/bbZ5+s3MkYn6XbK6p8CVgduah+iFKJZOuhrjLNx+hytkp1+aPumrv0bAS+y/cEabYzNzzPOOsf1O/ZdYHuTEcSyKnCR7YcN+7W74mhXwRewJuUe663MYXWdcSRpa+BK23+otl8BvIjyffNdde5JjxtJP7L91Nn2zdJGz2Jjtmdd8VbSecAz2r/bjv33B44d58JSknbrddx9rMwk6SnA6ygrpkBJVj3E9glziOs+rEg8PWMuE7sHIelC29NOEut1bJrnTnd9/RU4u0p0njh9FIbqPOc8SnGXH9revLpWdrX9qj7a+Drw2iavBZVVCb5i+5l9nLPc9haSHkwp+nUosMck3hdJMnNEREQDJD0EeFzHf+tQZnmfWmcQo6Odp1CSvdoVO/rqTFeJ1V/p2PWSzu1+ZylK2ht4LyUR7c5lMWz3WsqnfW6vLxlLKDPaNrH9tB5tdC5Tp47HS4C72c4qEwtINch8jdNBHZquQbDVKANgt1NzEKw9+CLpQI/J8maSfmp7G0nfBz4O/A44ynYjFdWGoUrgpd9EXUlXATMOYNUZ3KrxGu+y/a4+nr8K8Crg6ZTr6vvA/9T9O5f0FeCTtk/q2v9EyuDAS+vGMkP7W3ruqxfEBFNZPu1twPOBe1PeC68GvgV8wPa1IwxvKCQdDbxm2AOu80XSYyn98P1YuQLFWsALBpngFxGLl6SfUyrz/prBljpfUCQ9udfxuknIVXLA0yhVMzcF/g840vZFNc/vHDNYg5UTfBq5qS/pStvrDdrOYjAu/atxiSMC8jkS0S9J+wKvBr5R7XoB8BnbB/fZzhOB02zf3rFvC9vLGwu29+vP+xhd1LfQ+gYL7efpJulQyj3JT1S79gZatl85oni+Bbze9hWjeP3FQNJy4J9dVh1+EuW+9uuBRwMPt/3ikQbYB0nLKN9Njwe2ZcWE3bWA7/WTGF9NIj4XOJYV/cg72X53jTZ2BfYF3gi0VzzeglJp+OO2v1g3nnFQ3W+6u/uoIixpe+AQyqSm5ZTf4xbAfwCvcx+VziXtRKl8e0LVzhOBN9k+qm4bM7T7GtufqfncGSdy9zPJW9KXga2A71S7dgDOBzYAvtZPfsu4qFsYquucs2xvVSU1b277jn6LBUraivIZfCErFwec88rlkpYCF7qPVX46J9OpVHI/FHjhJObTTFzAERER48ilEvMvgc9XM7GfTfly8HSgVmevqzP9blZ0pg+VVLcz3dQSJW1vBDb2HJa+qTMjUlLPn8ldy9RVHa+9gT0pVWRjQknaBvgAMAX8J/BF4B+BVSS9wvb3RhnfYtH9NzYH95X0OOC5VdJp92DKUG4QdHlPNaD7RuBgyiDR/iOIoy+SRKkY8zrKsoWSdBtwsO0DajazKnB36G/ZoGliWQbsBWwMXAB8zvZt1eG+Plds3wF8tvpvLjbuTmSu2j1Z0qfm2GZnOws+kbmpGxxNtDMubVT+l7Jc1rYdVT/WBXarjj29ZjuT7J7ALySdSUODbCN2N8p74BKg8/PtOmBibnpExNipXQFlMambrFyjnduB7wHfk7QaJan5hKqK8SE1zh/0+0wdtSfbSnpz+4afpB1tf63j2Ptsv30+AhwjA/evGurrNdLPG7O+a0yufI5E9OdVwD/ZvhFA0oHATyhjfP34PnBm9XncnsD7P5T7LcPQyBjdYjdOY0Bj1i9Y6GNarwfeQakqCfADyn3BUbkXcJGkMyiV0oHRjJ9JegHwY9t/rbbvSbkOjh52LA1btaP68s6USSxfB74uadIqxO5JKbRwP0ribNt1lByAfmxO+Y68PeW+zJHAj/opCGX7S5L+DLyHUmTMwEXA/7N9bJ/xjESVdLsXpRDTmcBakv7L9odqNvEm4PleeaWHcyWdRelf1E5mBv4d2Lrdt6gKE/0QqJ3MXN1/27javMRlpdx++gubSZoumVvAsj7aeQCwhe0bqrjeSZlg/iTK9Tb2yczdhaH6TWSuXFvln5wEHFEVDrxxlnO6fQE4kHI/9Y45xICk77Bi/GkV4BHA12Y+467csSpI9f91J5XVHyZOKjNHREQMqErkexzwWGA94DJKVeafAstdc+luSScA+3Z1ppG0KSWZrWflo1naXgY8p/NGWs3zjqN08G+a9cn9tVt7hmH1/HtSvvy9Avgy8FHb1zQZUwxX9SXx7cDawGeAZ9n+qaSHUSpxDbwMX8w/SS+m3GR4AnBW12Hb3m74UU0mlSWdnkWpkvrrat+GwKcoM/Y/2uv86vmNLK0t6auUKt0nVzFdbnvfPtu4gJkTP24BLgXe3/2ZN007F88087jXsa7nfbvX8QlO3KylqlL+Y+AL09zgeKrtuskcA7czLm1U5wx8bU26mapqNpWgNiqSHmj7cklrNN2HjYiI2an/lTxWo9yc3YVSAejbwKG2fzsvAU4fQ2eF55UOAavXrWLT2R/v7ps31VcfZw313ZvoLzbSzxunvmtExGJRjSdtXSX2tO9rnGl7kz7bOYeSDPkh4FW2T+usVjffFsPn/jCM0xjQOPULMqY1XOM0fibpXNuP7to3tPe2+SLpQuDRtm+T9AvKPZKT2sdsP2q0EfZP0uvd56oCs7T3OMp35n8G3mK75z2PhaR93Ut6GWVS0luBs11zpRNJv/AMFbF7HZvh+Rd09klUKkWfV6efImkJ8D5gD+ByynjDesBhwL/bvrVuHE2o/tY2ab9uNTZznu2HjfP7ynSFoYB+C0N1trcm0E4ofxklb+GIfvJQJJ1pe+t+X7urjc7Pmtso92Wvqnnuwaw8pmXgz8Dxtk8ZJK5RSWXmiIiIwZ1CmV35UeCbAyRNrDtdUpft8yXdp9/GVJZrfQbly83TKUlpfSUzU2aenSvpeFau2LdPv/F0h1frSdI/Uqqr7kxZCmPz9ozjmHhLbB8HIOkA2z8FsP2L8j0kJoHL0klHSXqH7f8cZSzTfFlbSQPvW/Pt5cDT3FEJ3/ZlKsuAHUf5jJlNU388j2gPvkj6HHDGHNrYocexJcCjgM9Tqgv0comkZ7trdQJJz6JMHqrjscCVlMoFp7P4quJsYPvAzh3VDZMDJe0x5HbGpQ2AyyW9mXID6Y8AVX/rlZTrZcGzfWL1M7cH2s7wiopVk+x+ko6lVMFaX9JmwJ62/3XEcUVELBa1V76QdDilX3gM8G7bFzYVhKTv2u7VJ72Tm6vwrBkeT7e9EDXRv2qir9dUP2+c+q4REYvFYcDpktqrMj4f+Nwc2rHt70q6GPiqpEPpY7WFBiyGz/1hGKcxoHHqFyzoMa3qfuRd/l5HVThlzCb9rzLNvoWQ83UkcKJKBeGbKfezkbQxMFH3hCVtZ/vHwG8lvbD7uO1vzKHNdSj3UTYBrqJUdB+YpB1sf7eJtubZUklLKX2CQ2zfKqmfz/ReVXb7rcD7vWpiypHV9s7Ur+z8Icpqgg+yfT2ApLWAg6pj+/UZy6COoPS5vlVtPwf4cpXc+7Mhx9KP/YHHUya/rVQYStL+rlEYqpOr1UAqs646PoOTJb2fMjm/M59m1tWLJT3D9ven+6xR14pfPXQXGwNoAR+S9FXbH6vRxlhZCB9sERERo3Y/SmXmxwF7VjPrllOWP/uJ7brJVo10pquZWy8Fnk1JQHs8pWM8lyTro6v/mlb3BuXlwJ8og5g3Aa/qTHS1/ZHmQ4sh6Vxm5eauY1k6ZMLY/k9Jz6UsPwRwwggGQTq/rL2bMjN3kiztTGRus/2naqCmjqc2FMudM8Cragh9N2D78lmecqmkOhVq9gP+T9JOrEiM2YqSoFwrOQVYF3gaZXLPSylLZR1p+6Ka50+6pm5wNNHOuLQBZaDxrZSB8vaksT9QBpx26qOdiVX9XX0IOIFyo/VgSW+qJqpMso9RJvR9G8D2eZKe1PuUiIho0EZ9PHdXynjHvsA+Hf1OUZKQ1hogjlcPcO5cdVfCmenYQtVE/6qJvl5T/bxx6rtGRCwKtj+isoLlE6pdu9s+Zw5NqWrvV5KeSLm/UKuCY0OaGqNb7MZpDGic+gULfUzr3zoeLwNeRKlSOVSSTrH9BN11FZcmvqvM1VmSPgJ8otremz4mk44r2++V9CPgvsBxttu/71WA148usjl5MqUC+3OmOWagdjJzNclhJ8rfwVHATg0XotgamIRk5k8DvwHOA06S9EDguj7O30jTr94pYMN+ArH9JkkvouReAHzG9jd7ndNhB+AhHdc3tq+T9FrgFww5mbm6r3ssK36WvWy377O+bJix9KmJwlC9VuiCFavL/rvtH9Vorl20aZuOfQbqTMI5RtJJwK6+6wplb6NGoULb0yZhS/pv4DTK/ZKJoo6/k4iIiGiApDUoS4TsR0kiXrXmedcCJ013CHiC7XvVaOMq4ArgU8DRtq+X9GvbD6r9AzREpTL0TsD9ge/ZvlDSDsDbKcu0zro8iaR30bvS6rsbCjeGTNLtlJvWAlanJKtTbS+zXTd5M8ZANeP0MZSZvFCSRs+0/fYRxTO2SyDNRD2Wn+x1bJ5iaf99wsp/o40M1Er6jO3X9PH81ShJyO3l5C4CvuxqydE+X3s1yvX5IUr1v0P6bWPSSLoX5QbH84DuGxwH2p4aVjvj0kYUks6jDPxdXW2vA/zQ9majjWwwkk63/U+dnwWSzpv0nysiYlJIusL2+kN6recBD7D9iWr7dGCd6vCbhz1BJ99zBzdOfb30XSMihkdSq8fhW7qq5tVp7wnuWlZb0pNsT3f/JcbUOH2Opl8wWpLOsP2YUccxalXF1HcA/1zt+gHwnn7fI2MySLqDUhysXThmpfvltp879KDGhKQltmtNcqiKwM1oWNXXJf3S9kP6PTaP8XzR9stn2zduJF1o+1H9HuvzNVal3I88oon2Znmtc4BPAv8P2L9zHKuJe92TeL8ckswcERExMElrUypEtqszbw78ilKZ+dS6N8+a6ExL+hhlmZULgS8D3wIusN3XzMKO9h4MvB94BGXmZzuWWduT9HlgPUp16H8CfkeppvlW24ASb3EAACAASURBVPNR7TkiRkTS+cCjbd9Rba8KnGN7mBVPOuMZavJvE7oSiFc6xAJLfJC0pe2hVoyokpi3pyQyb0C5SXDoNDOdYxGR9AxKv+n+1a7fAt+y/b3RRTU8ki6wvUnH9irAeZ37JpGko4CPAIdQ+qD7AlvZfslIA4uIWCQkXWl7vSG91qnAS2xfWW2fS6mEuCZwmO1URRyycelfjUscERFRj6RfU5K02ss0tBMYxIqVpt9q+4juc2do7y5jg5M4XhjNWWh9g4X283TqmtywCrAl8HHbDx1RSABIujcr3ye9YoThLBqSvmu77gqNY6O6H/Eiyr2I9ucYtg/oo40m8gbebPuD1eMdbX+t49j7RlWQqB9VHsY7WbEy7InAAbb/OsQYBq7iK+lo4Bu2D+/avyul6vZQk9O7+0XVfd0LbD9imHH0a5iFoSTtafvTPY7vavtLkt4w3XHXWGG8HbOkh1AKhl0I7G37pkF+HpWV5F8OvND2dJXix9qS2Z8SERERs7iEkrj8E+AASjXSm/ttpImZf7b3k7Q/sC0lYeuDwNoqy4gfY/uGPps8jPIF4aPAU4DdKYMHdWwFbGr7DknLKDPMN7J9TZ8xRMRkuCfQrh6x9igDmUR1q/gvBE0lMtet8CzpcMos6mMo1ZgvbOL1J0lTNziaaGeM2vgY8BDgcOCqavcDKEvcP8v2vnXbmmDfk/R94Mhqe2fK38mk2wv4L8r18VvK8nJ7jzSiiIjFZZjVU+7WTmSunFKNOVxTVS2LIWqqfzVoX6/Jft649F0jIha62VaWrFYSOpEVq8LN9Lx20Zl1uhJL1gIWzdjbQjJOY0Dj0i9YBGNaZ7NicsNtwK+BV40qGEnPBT4M3A+4Gngg8HPgkSOI5Xim+b5le7thxzJErx51AHP0LeCvlOv5lrk00FDF4JdQcgUA3gZ8rePYMymrKY+7QykJnjtV2y+n5C+8cNCGJb3L9rtme57te/Ro484qvqxYXXQ6ewPfkLQH5bqAkkuxOvCCujEPStLbqFbRlnQdKyaS/R34zLDiGMBmVdzdRMeEk0G0J1H0SmSurFH9O+P1UZftX1b92PcA50h6Rd1zZ0i2v5nSd95z0NhGIZWZIyIixsRMX0IrnktFIUlLKV9GXgI8w/Y/9nn+2ba37Kzc195X49zuGX2pfBCxQEnaBfgAcDzlC+OTKNVSvjrEGDq/rK3Byks62/Zaw4olQNK3ex1vYpZ53QrP1XJs7arXnZ+zi+La6HGD4xXAr/q4YTNwO+PSRtXOtEu3SRLwS9sPrtPOJJK0MXAf26dKeiHwhOrQtZSl0y4dXXQRETEJelQlErC67aEUUZF0ie2NZzh2qe2NhhFHFE30rxrqLzbSzxunvmtERICk59j+zizPeTKl0MtewH93HLoe+I7tX81fhNG0cRoDGqd+wWIe0xoFSecB2wE/tL25pKcAu9oeeoK1pM57s8solX9vs/3mYccyX6rJK9j+06hjGYSkC233SmwdtP1aSbiSzrG9effj6bbHlaRzbT96tn1zbHvWvkUfbfWs4tvxvO1YMRniZ72qOc8nSe+3/bZRvPa4k3Rf27+v8bzP237lgK91l79DSdtSkvjX6ZVIv5AlmTkiImJMdH0JbdsGeDNwte2tB2x/9X4rRks6jZLgchTwY8os8Q/UWc5J0k2UqtVQbmhuVG23k8c27SeWiBhvku4LtN+nzrD9h1HGE6Ml6U/AlZSKr6ezYnY30FhVgaihwWSOJm78jEUb1fPPB15l+8yu/Y8BPteexLUQSfou8DbbF3Tt3wR43yQuOwYg6WB6VAO1vc8Qw4mIiCGQdARwgu3Pdu3fE9jW9i6jiWxxaqJ/1VB/sZF+3jj1XSMiFrO5FEmR9EDbl89XTDEc4zQGNE79goU+plUVaXotpWAKwAnAp23fOqJ4zrK9VZXUvLnLarTn2d5sFPF0k3SG7ceMOo5BVH8D7wReR1kduF2V+2DbB4wytrmS9BlK/BfM+uS5tV8rCbfzM3RSi5BJ+gnwJtunVNuPBw6y/dia56/nlVd06jy2g+3vDhjfd23vMEgboyLp/pRq83dOSLd90ugiGo25TqJo4m9I0vNtHz3N/nsBe9r+wCDtT6qhVEiIiIiI2XVWl6wqCLyDMrN2L9vH1mlD0gWsnMRh4M+UaqkHzSGsfSkVTvcB/hN4CrBbzXMfPofXm9UkfymIWMiqWao9q/HGorIu8DRgF+ClwP8BR9q+qJ9GNIQKz4vA3yRt3X2DgzL54G9Dbmdc2gB4JfApSfdgRTWc9SjL/72yj3Ym0X2mG0i3fYGkDYYfTmPOGnUAERExdPsDR0t6KbC82rclsBpl+e4YrlcyeP+qib5eE3E0FUtTfdeIiAVNHSsIde1/PCuWla/Tzsds7wccIukuk10zjjRxxmkMaJz6Ba9kYY9pfQpYCnyy2n55te9fRhTPtZLuDpwEHCHpalasAjhUklodm6tQvvusPYpYGrY/8Hhga9u/BpC0IeU639/2R0caXR867tMvAXaXdBlwC80X+qq7CtFmkq6rXn/16jHV9rKGYplvewGHS2pf63+hfq4CwA8kPdP2bzp3StoD+HdgoGRm4NUDnj8Skj5AWdn7Z8Dt1W5T3usWvOkmUUjqdxLFGpI2p6uYU5vt5dPt73rOXRKZq/1/oayIPJBJmbTQLZWZIyIixoikZwD/Qfli817bx/d5/gOn2d2idOrXtF2rQy1pGXCP7hloku4NXGd71oEVSfIsHY06z5nmnIlY9iYiIgpJq1GSmj8EvNv2IX2cmwrPA5K0BWXAf7obHHt3Tqaa73bGpY2u9tYF7l9t/nYxVJWX9KuZqv1IusT2xsOOaT5IWotyk+D6UccSERHzSysv03qR7R+PMp7FbpD+VZN9vUH7eePYd42IWKiaWkFI0ha2l1fFYu4i40iTZZzGgMaxX7DQxrQkLbF923RVj0dRCVnS+ravkLQmcDMl2e1llOThI2xfM8x4qph+TUk2bFcu/jVwQLti7aSSdA7wNNt/7tq/DnDcJN0TnuE+/Z2aWjVA0hW212+irXEm6dHAxsBFlNWjsX1dz5Pu2sazgY8B29v+VbXvbZQiPM+yfVWv86dpb05VfMeNpIuBTW3fMupYRkHSG4BnAa/pnkQBfK/OJApJ1wNnMn0ys21vN2CMtQr8zTIp8I+2L5n+zPGVZOaIiIiGSHoIpYNzH9uPkrQp8Fzb76l5/pnAOpRkr590H68ze2uW9msnAVfL33zP9je69r8AeLrt19Zo4wTg68C3bF/Rsf9uwBMoCdbH2/587R+inH+o7T36OSciIoavSmLenpLIvAGlcvehtn/bRxursqLC86bMscJzNHeDo4l2xqWNadp8l+13DdrOuJN0JPBj25/t2v8vlBsHO48msmZI2go4jHJzUMC1wB5JFoqIiBi+ufavmu7rDdLPG9e+a0TEQiLpTNtbz3DsAtub1GznONtPrx6/zfb7m4wzRmOcxoDGtV+wEMa02tUjJS0HdrR9abV/Q+CoYVeW7KxmKenrtl80zNdfTCRdaPtR/R4bR5I+BpwGnNrPfZA5vM6Vttebr/bHgaT/B+wKnA38E/D+7vHsPtp6KvBpygpO/wI8hpLc/Jea59+lii9lQkE/VXzHiqRjKe+1N4w6llFoYhLFfBfgk3Rfl1WRZ3teI5MCx0mSmSMiIhoi6UTgTcCn2x2Xfr5kVcm/M30wNzF7q/bMZUln295yhmMX2X7kdMe6nrcM2IMyU/lBlESSZcCqwHHAJ22fUzf+iBg/WnlZs7uwPTWsWGK8SDoceBRwDPAV2xc20OacKzzHypq6wdFEO+PSRtXORC651S9J9wG+CfydMhgMsBVwN+AFk55cI+l8SkWjk6vtJ1D6nU0t4xgRERE1NdG/aqi/2Eg/b5z6rhERC0lTKwh1JpUslu/4i804jQGNU79gIVzv7b/fatWVzwOXVYc2AHZ3nyvZNhVP9+NRkrQUeC3wpGrXCZR70reOLKgG9Lp+J+3alvQ64HHVf1ASm08DTgXOs31HQ6+z4CszS7oI2Nr2TZL+gVKEbdqJTzXbeyJlTPw0YCfXWIW649yBq/iOG0lfBzYDfkRZMRwA2/uMLKghamISRROfDZKeBzzA9ieq7dMpxQ8B3mz7qBptNDIpcJwkmTkiIqIh7Y5C1xfcc20/eogxTPeF7l6UmYs32H59zXZ+bvvh/R7r0d5S4B+Bm21f28+5ETG+tPKyZusDf6ke3xO4wvaDRhhejJCkO4Abq83OL52iTNBZq4+2Bq7wHCtrMJmjiRs/Y9FG1c5Y3JgYFklPoUw6ALjI9o9HGU9Tpvv/OGk3PiIiIhaKhm7uNdFfbKSfN05914iIhaSpFYS6Kqnm/XYBGqcxoHHqFyyEMS1JVwEfqTZXpxRGAridcm/xI9OeOH/xjN37iaT/AZYCX6h2vRy43fa/jC6qwUm6nRX3ElY6BCyzvXTIITVC0v1Ykdj8XODefd4XuZ7pi6AJWN32kkYCHVPdf3e9CrHN0k779yhgNeBWyvtK7XtVTVTxHTeSdptuv+0vTLd/oWliEoWkp9s+bsA4TgVeYvvKavtc4KnAmsBhtp9ao41GJgWOkwX95hYRETFkf5a0EdUXC0kvBmZd+qFN0pttf7B6vKPtr3Uce5/tt9do5sNd2wauoczO/UzdWICrJT3G9hldMW4N/KmPdkoQZVZw7d9FREyGdrKypM8C37R9TLX9LMpyTbFI2V6liXa6Kjy/u4kKzwGUgbpxaWdc2gDoezB0klUVbYZa1WZITpT0aeBISl94Z+CE9qQ/28tHGVxERMQi00T/qom+XlP9vHHqu0ZELCT7Ad+U9DKmWUGoj3Y2lPRtyntt+/GdbD+3iWBjpMZpDGic+gULYUxrVeDu3PV3sgS4x/DDYTNJ11XxrF49hjkU6xiUpCW2b6NUqe1cgffHks4bVhzzxfaqsz9rckgSsAklifnxwCOAS4Av9tOO7VFc9+Ok83NcwEadn+t1P9Mb+j0u7U5krtr+U1XQbeLY/oKkuwEPqXZdPOlV3vu0Wcf7eidRVhqfVa9E5j5WXrhbO5G5corta4BrJK1ZJw7gLEmvnmFS4NkznDPWUpk5IiKiIdVyIp+hfDn5C/Br4GW2L695/oyzfIc961fSY4D/pSzl1Dl4+ArK7LDThxVLRIy/6ZapmdSla2K8NFnhOVaQtEoTS9o10c6o2pC0BHgV5abo/ardvwW+BXxukQ3cLSiSeiVo2/Z2QwsmIiJiEZmv/lW/fb357OeNS/83ImKhGnQFIUlP7nXc9olzjS3GwziNAWVMq1njUv14HLV/N5KWAzvavrTavyFwVH5v40PSD4C1gHOBnwI/tf3z0UY1mcbpM72JKr7jRtK2lCrvv6Hcb1sP2M32SSMMa8GQ9Bzb36nxvBkrJ0u61PZGNdq4D/BN4O9MMynQ9h/qRz4ekswcERHREEkPsv3rapbUKravb++ref6dy0B1LwnVzxJRkjYH3kiZ6QlwFvBB25d0zN6t0869gb3pGDwEDrF9dZ3zmyBpK+BDlIGYtwGHAo8Bfgm8xvY5w4olImYm6fvAycCXql0vA55k+xmjiyoioLkbHE20My5tVO0cCVxLGbC7qtr9AGA3oFV3CduIiIiIKJroXzXUX2yknzdOfdeIiIjFaJzGgMapX7BQx7T6uQ+62LR/N5K2oxShuqw6tAGwe7X6WoyBarW4TYGbKcnMPwF+Ml1V35gckm5nRdGdlQ4By2xPXHVmSWcDL7V9cbX9EOBI2wuh0v/ISdrP9sdqPO8I4IRpqirvCWxre5c+XnOgSYHjJMnMERERDZlu5p2ks+t2+pqozCzpRcCBwPsoScxQZl7tA7wWeI/tp9b6gRpSJXffbPuOqiP8MODYmoM7ZwDvBO4JfBDY3/ZRkp5K+VkeO5+xR0Q9klqUv9UnVbtOAt5te2p0UUUENJrM0cSNn7Foo2rnl7Yf0u+xGH+S7klZTWQDylKkANjeZ1QxRURELAZN9K8a6i820s8bp75rREQMro/lvmNMjNMY0Dj1CxbqmJakVu4nTE/SVcBHqs3VgVWrx7dT7r9+ZNoTY2QkrQVsQ1nNeRtgHeBC27uNNLAFIp/pg5N0vu1NZ9sXcyPpCtvr13jevYGjgVuA5dXuLYHVgOfb/uP8RTm+lsz+lIiIiOhF0sOARwJrS3phx6G1gGV9NLWZpOsos/hWrx5Tbddt553AP9v+Tce+8yX9GPgFK77sztkcviCcBDxR0r2A44AzgZ0plVtns9T2sdXrHmj7KADbP5J0UH+RR8R8qQYZ9x11HBExrS2nuYlxFfBTSb8ccjvj0gbAlKQdga+3l/OUtAqwI/CXPtqJ8XMMpfLJBUCWcI+IiBieJvpXTfT1murnjVPfNSIiBnf27E+JMTNOY0Dj1C9YkGNaSWTuaVXg7pT7xZ2WAPcYfjhRwy3ATZQKzbdQJi7cbaQRTRhJy4C9gI0p47yf61h9Op/pgztL0v+w8oq7Z/V4fvSn+/16WtWK6I+rKu8/str9f5NcVbkJSWaOiIgY3EOBHSjVg5/Tsf964NV1G7G96uzPmtWSrkTmdtu/kXS57bc38Br9fkGQ7ZskvQr4pO0PSjq35rl/k/R0YG3Akp5v+2hJT6bMOI6IMVBVXf837loFc7tRxRQRd2rqBkcT7YxLGwAvoaxm8QlJ11b77gkcXx2LybXM9htGHURERMQi1ET/qom+XlP9vHHqu0ZExOA2GnUA0bdxGgMap35BxrQWn9/bPmDUQcTsJH2UUo35wcA5wE+A/wZ2s31tr3PjLr4A3AqcDDwLeARVUSXb3xlhXAvFa4G9Kat7Q/k9f3J04Sw47uvJJXl5UScwd5Ld1+8vIiIiZiDpsbZ/MuIYzgOeY/uKrv0PBL4ziqVBJJ0D/CvwUeBVti+SdIHtTWqcuxnwQUpVvf0pHevdgN8Cr7Z92vxFHhF1Ve89/02Z7HDnRAPbmR0dMWKSNqDc4HgKZSlLWHGD4622fz2sdsaljY62Hg48D7h/teu3wLds/7xuGzF+JO0P3AB8l1L5BEiFn4iIiGEYtH/VYN914H7euPVdIyJiMHWX+47xMU5jQOPWL8iY1uIi6Rzbm486jpidpH2AU4Fzbaco1wA6cwkkLQHOsL3FiMOKuJOk65k+aVnA6rZTYHiOkswcERHREEmHMU2HxfYeQ4zh+ZTk3/exooLyVsBbgbfYPrpmO48ENrL97Wr7o5TqyACH2F7eR0xPBt4InGr7QEkbAvvZ3meWUyNiQkg62/aWo44jIqbX1A2OhpJCxqWNt1Cq1XylOh/Kcn8vAb5i+wN124rxImlv4L2UG4Ptvrltbzi6qCIiIha+pvpXDSQbNdbPG5e+a0REDE7SlbbXG3Uc0Z9xGgMal35BxrQWH0mtTNKPxUbS8s7k5e7tmBtJ5/c6PorCeBHdkswcERHREEkv6thcBrwA+N2wk3arasZvBB5Z7boI+LDt8/po4zvA+9uVjyX9DHgHsAbwItvPbzbqGeMQZYktA0cB21EGen4OfLq9FFdEjJakdwFXA98kVTAjxkqDN2wGbmdc2qja+SXwSNu3du2/G3CR7QfXaSfGj6TLgMfY/vOoY4mIiFhMmuhfNdRfbKSfN05914iIGFwqM0+ecRoDGqd+Qca0IiZTKmz3R9LtwI3tTWB14KbqsW2vNarYJpmkcyl5F18GvgPc3Hnc9uWjiCuiU5KZIyIi5omkVYBTbD9u1LH0S9JZtrfq2P6p7W2qx6fYfkKNNr7D9EtrAGD7uTXa+CRwb+BuwHXAasC3ge2BP9red7Y2ImL+SZpuCbxUwYwYAw0mczRx42cs2qie/wvgGd2Dc5IeCBxn+6F12onxI+k44Pm2bxp1LBEREYtJE/2rhvqLjfTzxqnvGhER9WS574VlnMaAxqlfkDGtiIgYhKSHAbsAzwF+RklsPs72bSMNLKKSDntERMT8eTAlEXcS3aNzo53IXKn7Mx1U/ftCYF3gS9X2LsAfa7bxRNubSFoK/AG4r+2/SzoSWF6zjYiYZ7YfNOoYImJGdwD3A7pn1N+3OjbMdsalDYD9gB9J+hVwZbVvfWBj4HV9tBPj50bgXEnHs/JqAUNdLSUiImIRaqJ/1URfr6l+3jj1XSMiogbb95j9WTFBxmkMaJz6BRnTihhTPSbVQBmnvBT4d9s/Gl5UESuz/QvgncA7Je0MHA4cCHxopIFFVJLMHBER0ZCOLyiq/v0D8JaRBjV3v5P0T7ZP79wpaRvgd3UasH1idc6HO6s8A9+RdFbNOG6r2rpV0pm2/15t3yYpN30iRkzSm21/sHq8o+2vdRx7n+23jy66iKg0dYOjiXbGpQ1sf0/SQ4DHAPevdv8WONP27XXbibF0dPVfREREDFFD/auB+3oN9vPGpu8aERGxSI3TGNDY9AsyphUxvnpNqpG0KvAo4Ijq34iRkHR/4CXAC4C/APsD3xxpUBEdZM+4+ntEREQsUpIeA3wV+DwrKiBvCewG7Gz7jD7a+jmwve3Lqu0HAcfYfniNc48FdrR9Q9f+dYFv235M3TgionmSltveovvxdNsRMTqSVqGBGxxNtDMubcTCVi3R+pBq8+LuJVwjIiJifI1TXy9914iIiNEap8/R9AsiogmS9rT96VHHEYuTpBMpK3T/L/B14JrO47anRhFXRKckM0dERDSomsn2QDpWP7B90pBjeArweuCh1a6fA4fYPqHPdu5NmQ3+yGrXRcAnbP+xz3aeAXwWuIxStfqBwGtsH9dPO11trgmsafvqubYREYOTdI7tzbsfT7cdERExDJK2Bb4A/IbS91wP2G3YffKIiIiIiIiIiIiIiHEh6TeUFcbp+Beqlcdtbzj0oCK6LJn9KREREVGHpAOBnYGfAe1Z1AaGljghaXvgEOAA4N2UjucWwKGSXmf7mLptVYnC/2/AeFYB1gYeDDys2v0L27cM0q7tG4EbB2kjIhrhGR5Ptx0RETEMHwaebvtigGrp1SMpq4xERERERERERERERCw6tjcYdQwRs0ll5oiIiIZIuhjYdNBE3QFjOAHY1/Z5Xfs3BQ62/eQRxHSW7a2G/boRMf8k3U6ZWCBgdeCm9iFgme2lo4otIiIWJ0nn2950tn0REREREREREREREYuFpCW2bxt1HBG9pDJzREREcy4DlgIjS2YG1u1OZAawfb6k+4wiIOCHkv4N+Cod1ZRtT40onohoiO1VRx1DREREl7Mk/Q/wpWr7ZcBZI4wnIiIiIiIiIiIiImLUzqCs6n0Xks6xvfmQ44m4iyQzR0RENOcm4FxJP6Ijodn2PkOM4cY5HpuWpE1sXzBAPAA7V//u3bHPwIZzbVDSPYG9bb93kMAiIiIiYsF5LaXf2e6Dnwx8cnThRERERERERERERESMnGY6kETmGBeyPeoYIiIiFgRJu0233/YXhhjDtcBJ0x0CnmD7Xn22dzKwGvB54Ajbfx04yP5efz3gHcD9gKOBI4EDgJcDR9red5jxRERERMR4k7Qm8Dfbt1fbqwKr2b5ptJFFRERERERERERERIyGpKuAj8x03PaMxyKGJZWZIyIiGjLMpOUentfj2EH9Nmb7iZIeDOwBnC3pDOAw2z/opx1JjwIeASzraPvwGqceDpwIfB14JmWJ8HOBTW3/oZ8YIiIiImJR+BHwz8AN1fbqwHHA40YWUURERERERERERETEaK0K3J0eFZojRi2VmSMiIgYk6X9t7yTpAuAuH6y2Nx1BWI2qKto9H/g4cB2lg/t229+oce47gW0pyczHAM8CTrH94hrnnmd7s47tq4D1bd8xl58jIiIiIhY2SefafvRs+yIiIiIiIiIiIiIiFgtJy21vMeo4InpJZeaIiIjB7Vv9u8NIowAkHc80CdUV235qn+1tCuwObA/8AHiO7eWS7gf8BJg1mRl4MbAZcI7t3SXdB/hSHzHcixWzA68B1pYkANtTtX+YiIiIiFgMbpS0he3lAJK2BG4ecUwREREREREREREREaOUiswx9pLMHBERMSDbv6/+vXzUsQD/Ns2+bYA3A1fPob2Dgc9RqjDfmQRi+3eS/qNmGzfbvkPSbZLWquJYr+a5awNns3LHenk7DGDDmu1ERERExOKwH/A1Sb+j9CHXBXYebUgRERERERERERERESPVV+G7iFGQPVPxxoiIiKhD0vWsXA1Z1bYo1ZDXGlFcTwbeASwD3mv72Dm2czfgYZSf6WLbf+/z/E8CbwdeArwRuAE41/buc4knIiIiIqIXSUuBh1abF9u+dZTxRERERERERERERERERG9JZo6IiBiQpKMpFd++AXzF9hUjjucZwH8At1CSmI8foK1nA58GLqUkZz8I2HOAxOgNgLVsnz9ATBsBLwVeYvuRc20nIiIiIhYmSY8DNqBjRTLbh48soIiIiIiIiIiIiIiIiOgpycwRERENkLQ28EJK9eFlwFcpic1TQ47jTGAd4EPAT7qP217eZ3u/AHawfUm1vRHwf7Yf1kcbXwcOBY61fUc/r9/Rxv0oy4O/FNgEeD/wDdsXzKW9iIiIiFiYJH0R2Ag4F7i92m3b+4wuqoiIiIiIiIiIiIiIiOglycwRERENkrQKJaH548D7bH9kyK9/AjDTh7ttb9dne2fa3rpjW8AZnftqtPHPwO7ANsDXgMNsX1zz3NcAuwD3B/63+u9bth9U/6eIiIiIiMVC0s+BRzgDXhERERERERERERERERNjyexPiYiIiNlUS1nvAjwROAV4ge2Thx2H7W2baEfSC6uHZ0k6hpJEbGBH4Mw+Y/oh8MOqevUu1eMrgc8CX7J9a4/TD6FUmH6p7bOq2JKYEhEREREzuRBYF/j9qAOJiIiIiIiIiIiIiIiIelKZOSIiYkCSfgNcC3wF+DFwW+dx28uHGMubbX+weryj0NNeDQAAIABJREFU7a91HHuf7bfXbOewHodte48+4/oHYFfg5cDvgCOAJwCb9ErArs7bkZIEvS4lqfqVttfr5/UjIiIiYnGQdDzwaOAM4Jb2ftvPHVlQERERERERERERERER0VOSmSMiIgYk6QRK1WKqf9Vx2La3G2Isy21v0f14uu0hxvRN4KHAF4HP2/59x7GzbG9Vs50HADtTEpvXBL5ZNzk7IiIiIhYHSU+ebr/tE4cdS0RERERERERERERERNSTZOaIiIgFRNI5tjfvfjzd9hBjeort4xtu88HALrYPaLLdiIiIiIiIiIiIiIiIiIiIiIgYrlVGHUBERMRCJOkzI3ppz/B4uu2hGCSRWdKukl4+zaFtgEvmHlVERERELCSSrpd03TT/XS/pulHHFxERERERERERERERETNLZeaIiIh5IGm57S1G8Lq3AzcCAlYHbmofApbZXjrsmAYh6XTgqbZv6Nq/JnCS7S1HE1lERERERERERERERERERERERDRhyagDiIiIWKCuHsWL2l61yfYkrQa8CNiAjn6D7QOafJ0elnYnMlevf6OkiUrMjoiIiIiIiIiIiIiIiIiIiIiIu0oyc0RExDyw/cxRx9CQbwF/Bc4Gbun3ZEnrAtj+g6R1gCcCF9u+qGYTq0ta0/aNXe3eA7hbv/FERERERERERERERERERERERMR4STJzREREQyQ9ADgYeAJg4GRgX9tXjTSwwTxgronZkvYE3loe6kDglcCFwPslfdD252o08zngKEl72b68ancD4BPVsYiIiIiIiIiIiIiIiIiIiIiImGBJZo6IiGjOYcCXgR2r7V2rfU8bWUSDO03SJrYvmMO5rwMeCawOXA5sXFVovhdwPDWSkW0fJOkG4CRJd6923wB8wPan5hBTRERERERERERERERERERERESMEdkedQwRERELgqRzbT96tn2TQNIFlOrSS4AHA5cBtwACbHvTGm0st71F9fg825t1HDvH9uZ9xnQPyotf3895ERERERERERERERERERERERExvlKZOSIiojnXSNoVOLLa3gW4ZoTxDGKHBtqwpKW2bwW2b++UtAxYpe/GksQcEREREREREREREREREREREbHgpDJzREREQyQ9EDgYeCylqvFpwD62rxhpYAOQtA1wUTuRWNJawMNtn17j3PWB31fJzJ3771+18cP5iDkiIiIiIiIiIiIiIiIiIiIiIiZHkpkjIiIaImmZ7b+NOo4mSToH2MJVh0HSKsBZtrcYchyr2b5ltn0RERERERERERERERERERERETFZ+l7ifdK1Wi23Wq27d+37c6vV2qDGuce0Wq2N5i24AbVarW1brdZZI3jdu7darcNbrdYFrVbrF61W69+6jr+j1WpdWv33jo79r261WudX553farV2HXbsHbHkumj+dZ/ZarXOa7Va57ZarYtardZ7W62WOo5v12q1zqqOXdRqtTYbdoyzyXUxL6+70K+LCyWdKukDkraXtHbX8ybxupA7Zj7ZvgNYUqc9SetJ+oqkkyW9XdLSjmNHd7zuo1ut1qmtVuumVqt1VFdMH2i1WueutdZaf6mum7+1Wq19qsM/6fEZM+O11mq11mi1Wke0Wq0Lq2NfbbVa96iOvbLVal1bnXduq9X6Zs3fXd4vmn/dnu8XkyDXxby8bq/3izn9/Q5brot5ed0Zv1e0yveUczv+u6PVaj23OrZ79fxzq3P3mflV5v1nyHXR/Ov2/L7ZarV2qo5dWP17n2r/O6rPnfNbrdbZrVbrGcOOvSPGXBfNv276F7kuer3+Oq1W64/dfYxJkOtiXl9/Iq+LXBPz8rozfhepjvc9Bt5qtZ7eKuNgt7RarYOG8DPkumj+dWe8NzJbv6M1JuOguS7m9fUn8jMEcl3M8+vnuhhD49i/6NWHqI7PNK7xrlardXVrxVjYJ+b5Z8h10fzr9rouet37mnF8vNVjjHSefoZcF/P3+vkcGUMjfL/odc97Iu6n5bqYl9ft9Vkxa55eq9V6aHXdHNSx7xOt8p33vOq62mqef4ZcF82/7pzGp1qt1qrV//9LW63WJa1W6186jq3barW+VbX38+mup7molYwUxdTU1LNHHcOYejvwd2BTYA3gtFardcrU1NRPW63Wk4AdgUdVzz291WqdODU1dRLwK2DbqampqVar9QDg3Oq834zgZ5izXBczOgXYYmpq6vZWq7UUOBU4Hfh2q9W6P3Ao8IypqamLW63W6sDSHm1NnFwXM1rQ14XtjSWtDzwR2B74hKRrbT8aJva6uEzSPsCnqu1/BS6ree6hwNeBnwKvAk6U9Bzb1wAP7Hje1cAbgEcDT+ts4C9/+cvHgK8BX1q6dOn+a6655jE33njjzyVtu2TJkn9g5s+YGa814DXA3YBNqvO+BrwW+GC1/cOpqakX1/wZBzah18Uw9Pp/uODlupjRjO8XlaH+/Q5brosZzfi9Ympq6hXtJ7VKcsCPge9Xu74OfH5qasrVAN+FrVbrhKmpqfOH/hMMINfFjGa8LqqBtncB201NTf2h1WqtDbRXezgD+PDU1NRN1TVzYqvVuu/U1NTNI/kp5ijXxYzSv4hePgkcA9xj1IEMU66LWS266yLXxIxm/C4ywBj4ZcC/AC8Glg3nx5ibXBczmvHeCAt8HBRyXdSw6D5DINdFDbkuolOvsc65jmsAHD41NfVvjLlcFzPqdV3MNq4x7fj4LGOkYyXXxazyORKdet3zXvD303JdzKjXZ0XPPL1Wq7Uq8Gng6K42jwX2m5qaurXVau0AfBUYy4ThXBczmuv41MuAjYEHA/8AnNNqtX5YnfcR4KypqanntVqtdYCzq/GwKwcJNMnMXVqt1m+Awylv5vcFDpqamjqk49gOU1NTF7ZarUcAhwF3By4ANgDeMzU19d3O501z3kOBjwH/SPlQ+djU1NRhXTGsAVwBPGxqaurP1b6DgOunpqbe3Wq1jgAeCqwGXALsMTU19ZeuNratYt9qhu3dKAlpS4C/Aq+tBs0eBxxCqdq9tPqZjpzl17YZVRIAcGOr1TqRcjH/FNiZ8oXp5up1D6/2nfT/2TvvOLuqao9/z0w6abMoitJLgNBCACkiBkERBBQERGkBpAlio4mCCg9FivoQUKRXaQIKD56gSC/SQhUeIBipElYKhJRJ5rw/1r6ZM2dOuffOnb6+n898knvKPvuc8zt7rb12U9W7Kwmo6usi8hawAvBayfV6HNdF7bpQ1Q8SP0eE+2oLv7+B6eLFcOw8oF91DgDXheuiM1EUrTB+/PjHFy5c+K/m5ua1mpqamltbW5+q7O+PugB2BM4eMmTIT0aOHDn6/fffvx44uBpdjB07dq3m5uaLVXUa8M0oivYB7o2iaBdgyWzPqvom8KaIrJPxWLcHpgIrNDc3/27RokVzW1tbTwDeHzly5PPAX3NsTJHWYqyBqdJItBTwesa1G4aXFw0vLwYErou6dFFUXgwIXBd16eLuxP+L6hUHAVep6oJw7JzEvopdiOmDuC4arovvhOu+HfbPThybbMh5GoiwAE23+gr14Lpw/yIL10VdcS1EZG/gHeAxYKey4/sbrgvXRRrXRMPrInXFwFX15XD8l4qu3VO4LhrbNlLid/SbOKjrwm1IFq4L10UWrovG+hf1xjX6Gq6LhuuiEXGNDjHS3sB14XYkC9dFXbrIbfMuqcP2G1wXjY2Bl/gXAMcDt4bnODpx7K2JNB8CVhCRJlXtldi666KxbWZaHJ/6CnBBeNfvisjN2ID+M7CYyC9Dmu+KyDRgT+CsoryU0dSVkwcwo1R1C2AKcJqkpi4PXAGcp6rrYgLetCxRERkCXA18R1U3BbYCjheRtZPHqeqH2CiHryXO+xpwWTjkW6q6iaquDzwHHFfLzYnIpzDxbK2qG2MCuzjsPg44Q1UnYTNJ3B7O2UVELsxJ8nFgdxEZKiLLYB3PKjNurgT8K3HsdGDFjDxNAcaHtPoqrovadIGIbCIiT2Ojvu4C/ifsmgiMEZG7RORJEfmFiAyvJb99CNeF6yLJ9DiOxzY3N/97zpw5Y5qamiYOHz58236uizXjON5rzJgxuw4ZMuSpOI6/NnLkyN2pQhdz5sz558KFC+8KedilpaVlCvAtbLT38mX3CxDH8WVxHG8DTB05cuT8oUOHfj2O423iON5lyJAhiyiwMQVaOx94H6vwvwPMVtWrE+l8WmzZlXtF5AvV5LNKvLxoXHkxkHBd1KiLErrr++1pXBd16iKvXiEiw8I9XJzavouIPIfZkzNU9Zla7qWHcV00ThcTgdVCWfGEiPxQEkt+J9gPeEVV+1xH5gSuC/cvsnBd1KALEfkYNlPN8bXkox/iunBdpHFNNK4u4jHwEgawLoraRgZSHNR14TYkC9eF6yIL10VjY52V606htrjGXmLLfd8hIlt05doNwnXRQF2UxDUK4+N5MdJewnXhdiQL10Vt5UVZm3cR/ak9zXXRDTHwtH8hNnP/9oTOqQUcCfyP9lJH5gSuiwa3peZQFPN6HPM7IxFZFdiSjiu014XPzNxOcgauawDUlmmZifVEf6GyU0TGYmK4Ihz3sIhU0+g9AVgHuEZEKtuGh20vpI69FDg7/O0AvKBhWndgP7FRV8OwkTX/V9UdtrMz1jv+kZCPCGgJ+/4G/FBEVgfuVNVHAFT1T+Qvu3oa9tE8BrwL3A0sW21mxEZCXA58Vfve0r2uC6MeXaCqjwEbhEDuH4BPAfcCzcAnsVEy8zBDcBxwco157i1cF4brIhDHMVEUxcBGURTdNW/evKWiKHoIeGn8+PELoyjqz7r4NTA5uSGKoh8BiyjRxbBhw+6YO3fu2sA7SV1EUbQHtrxN1bS0tPw7juMVZs2atXYURScBjB07ds3m5ubccwq0tl04pNKh+moROVpVz8RGGl6rqvNEZCPgdhHZRlX/UUt+E3h5YTS6vOjvuC6MunRRQKO/357GdWHUrYuSesWXgOlqqwUsoZKuiKwE3Cwit2mYHa2P4LowGq2LZmwp8M+G/P4vFoC5PHHep4FTyF6Cr7dxXRjuX3TEdWHUo4sLgGNV9YPEfQ0UXBeG66Id14TR6LpIKR4DH7C6KGwb6edxUNeF4TakI64Lw3XREdeF0S3+RR1xjd8Cp6otA/9Z4I8iso6qvldvHurEdWE0XBcF/kU18fHMGGkP4row3I50xHVh1KOLojbvIvpDe5rrwuiWGHjavxCRocDvgANUdXFeOSMie2Eddreu8R4bhevC6K621Fr5Htb5fRrmi/4V60fUJQZjZ+Z3sSViP4AlPePHhe0V5if+v5jan9MiOs56PSL8GwEz1HrGF6Kq94vIGBFZH5iKTXte6Xl/OLCl2hTdXwMOqSEPlXxcrKonZVz3VyJyC2b0fi0id6jqD0vy+iFwROW3iJwHPB9+Tqdjr/uVgH8njl0TuA04VFXvL7pON+O6aLAuUufPEJHbsanm78V08ZiGZY9E5Dps1rO+huvCdZFFli6WBt6N4/hfLS0tHzQ3N/9h0aJFKwH7xHE8NoqifqeL+fPnrzVs2LCPAnEURd8dPnz4WiNGjPhIFEU/HjVqVBNV6GKppZbaZamllpoL3JM8Jo7jJ6mxY1Bra+slcRy/idmbC4HdFy9e3Nbc3JxrYxJ5SmvtMGwpz/nhmVyLae1MDUuAhPOeFJEHgE8AZZU3Ly96trzoL7guulEXGenV+/32NK6LbtBFFfWKAymYcURVp4vI37Hl+XqjM7Promd1MR24QW05zQUi8kesvLg8nLcFcCXwRe3dzu2uC/cvsnBdNF4XWwAXhaDwaGCk2OCWHcvusw/hunBdpHFN9GBdBI+BJxlUuihpG0ke15fjoK4LtyFZuC5cF1m4LnrWv6grrqGqbyfydKeI/BvryNOhnaaBuC56WBeJtDv4F1XGxwtjpA3EdeF2JAvXReN1kdvmXXKPfak9zXXRwzHwHP9ieWB14LZQzowHIhEZq6qHhPN2BU4FtlXVd6rNQ524LnqnLTWLSszr0fB7yUzNqvousE8i/dvIiInUSlP5IQOOO4FDE78PAR4OQaeqUNU5wDO0TxX+CWD9xCEvE6YmF5FtgY+E7S8CH4rIvpUDRWRtsdEAWVyG9WLfGhspAVZgzAbeE1t27MCcc/+JLS/TIraszFcT+27BRgCsEPLQLCIbh/9PUNVXVPV84L8xg1WIiIwVkZHh/xsAuwLnhd3Xh2uNDMfsB1wXjl0N+DNwlKreXnadbsZ10XhdTBCRpvD/pbBRKJVRLlcDnxGRYSEf2wNPlaXZC7guXBdZdNLF4sWL56rqh1EUPRbH8fKLFy/eFjPSWzc1NXValrw/6GLRokXvRlG0dBRFQ4Exw4YN2wzzG+ZEUfRtGqiLMkRk5JAhQ9YeNmzYHsDMOI5/AmyxcOHCReTbmCKtvQpsL7bcRRPweeDZcOzHE9ddGdgceLqKbHp50bPlRX/BddFgXRTRhe+3p3FdNL68KKxXhOt8CrgqtX2dxP+XAbah98oZ10XP6uJq4HPBFxgKbEvwO0VkU+BaYHdVfaLsOt2M68L9iyxcFw3WhaqKqq6iqqsARwO397PGQHBdgOsijWuiB+sieAx80OpCCtpGSvyOvhQHdV24DcnCdeG6yMJ10bOxznrjGskY6SRgFbp34L7romd1ketfSEl8XHJipN2E68LtSBaui8aXF7lt3kWUlRc9jOuiB2Pgef6Fqk5X1WUS5cyvgAsSHZl3An4BbK/tMw93J66LHm5LLeB64GARaRKRZbFVHm4IaS4t1tEcEfkM9nyvriHtTAZjZ+ZvA6uIyNMiMg37aPctOSeL/YBvisizwHdo74EOcCLwvZD+F7Be6qjqImwa8L3C9Z/DAlvDcq5xecjbHxMf5P8Cr2DTj98DZDauquqbwFnA48CDwFuJffcCP8CWU34KM2ZfDLuPEpHnRORJ4JvhOERkFxG5MCefqwFPicjz2BTqe4fro6p3AzcCz4W/G1W1MvLz59hIipNFZFr42z7nGt2N66Lxuvgi8ExI6xFslM+F4VoPArdjU80/jY2Q+WlOOr2J68J1kUUnXcydO/f9KIq+i3V8+SCKon9hzs6X29ra8pyaPq2LhQsX3hhF0U/HjRu3sKWl5fNDhgz536amprfiOP7FBx98cDUN1IWIrCIir2MO8I4i8rqIHJQ4ZLfFixd/qKrPAx9GUfQxoLW1tXUU+TYmV2vAT7AlOJ7FHNjh2ChCgCNC/p/CluA4QVWfzHm+Sby86MHyoh/humiwLkrKi3q/357GddH48qKsXrE/cIuqzkydd0i41jRs6aNzVPWOnGt0N66LntXFNcB/sMFn0zA/4qKw7zxgJHB+4rz16R1cF+5fZOG6aLwuBgKuC9dFGtdED9ZF6o2Bi8hWIc3vAoeGNLszPu666MG2EfpPHNR14TYkC9eF6yIL10XPxjrrjWv8VESeDfm7ANhXE7M1dwOui57VRVFcoyw+nhcj7Q5cF25HsnBdNF4XuW3eJWVJX2pPc130bAy83n56l2DP5YbEeUtXcV69uC56sM1MiuNTV2Cdrl8CHgZOVtVXw75PAP8QkReAk4Gda+lwnkcUx3FX03AAEbkbW57+1t7Oi9N3cF04WbguBh5RFL0F/AZb6qETYSbhQvqqLqIoWhY4FliXxLIWcRx/psrzm4Dd4zi+rov5OBH4NTbDwLlADFwQx3GnZTUGEn1VF07v4rpwsnBdOFm4LpwsXBdOFq4LJwvXhZPGNeFk4bpwsnBdOFm4LpwsXBdOFq4LJwvXhZOF68LJwnXhZOG66B8M6e0MOI7jOM4A4K04jk/u7Ux0E1dhM07vBByGjdx+t9qT4zhui6LoWMLyqvUSx/Ep4b9/iKLoVmBEHMezu5Km4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4zi9j8/M7DiO4zhdJIqiJ+M43qi389EdRFH0eBzHG0dR9HQcxxuEbY/GcbxpDWmcBszAOkXPrWyP41gbnmHHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcfoVPjOz4ziO43SdbXs7A91Ia/j3rSiKvgC8CUiNaXwl/HtEYlsMrNbFvDmO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO08/xmZkdx3Ecx8kliqKdgPuAFYFfA2OBn8Rx/KdezZjjOI7jOI7jOI7jOI7jOI7jOI7jOI7jOI7jOAMC78zsOI7jOE63EkXRUOBwYOuw6W7g/DiOW3NPaj93ctH+OI6f6HIGHcdxHMdxHMdxHMdxHMdxHMdxHMdxHMdxHMfpNbwzs+M4juM4uURRdBnwrTiOZ4XfLcBZcRwfWEMaFwJDgcvCpn2BxXEcf72Kc/8W/jsC2AR4CoiADYDH4jjeotp8OI7jOI7jOI7jOI7jOI7jOI7jOI7jOI7jOI7T9xjS2xlwHMdxHKdPs0GlIzNAHMczoyjaqMY0No3jeMPE77uiKHqqmhPjON4GIIqiG4HJcRw/E36vB/y4xnw4juM4juM4juM4juM4juM4juM4juM4juM4jtPHaOrtDDiO4ziO06dpCrMxAxBFkVD7YKjFURStnkhjNWBxjWmsVenIDBDH8bPAOjWm4TiO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4zhOH8NnZnYcx3Ecp4izgIeiKLo+/N4DOLXGNI4B/hZF0T+BCFgZOKDGNJ6OouhC4Mrwe2/g6RrTcBzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRynjxHFcdzbeXAcx3Ecpw8TRdFE4DPh511xHD9f5Xl7xHF8fRRFqwJvAmuFXS/GcbygxjyMAA4Htg6b7gV+E8fx/FrScRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRynb+GdmR3HcRzH6RaiKHoijuPJlX8bkN5IYKU4jl9sQPYcx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx+kDeGdmx3Ecx3G6hSiK7gRiYFPgvvT+OI53qSGtXYAzgGFxHK8aRdEk4ORa0nAcx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecp+/hnZkdx3Ecx+kWoigaBkwGrgC+nt4fx/E9NaT1OPAZ4O44jjcK256J43j9BmXXcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcZxeYEhvZ8BxHMdxnL5JFEXNwF/iON6mnvPjOF4IPBxF0ZZxHL/bxey0xnE8O4qiDpfoYpqO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4ziO4/Qyg6ozs4g8AgwHhgETgGfDridV9YAupn0u8CmgDWgFjlfVv3YlzUTaqwCPqeoyXUjjKmAbYHlgjKp+UOW+GHgGuy+AfVX1mbDvBGAf7H7fBw5V1edEZHngFkxfzcALwCGqOjMnb0cBw1T1zPB7EnA2ULnf76nq7Rnn3QysGvL2AfBNVZ0mIkOB68O+V4A9VXWRiCwD3Ahsp6oLQxqHAycA7+G6SL97Ac4FNsbyfq2qnhz2HQh8B1gMLAK+o6r3icgE4PyQ3iLgUeAbqjpPRIYDfwQ2ASjLd1IXItIMnAVsBwzF9HWMqsapc5bGZoBdHVgIvITp8l0RacHe/zLAfar6jXDOWsBZqrpTIp2fA1/GdOW6qF4XmftEpAl4ABgVknkLOExVXyval5O3XYDPqeqR4ff3gb2x8uYRrKxZkHHe0cDBwJrALqp6a2Lf+cCWwLvArqo6O+j1TuBLqqrhuB2Ay4A3GSS6iON4cRRFbc3NzePHjRt3OyXvSUQuBg4goRsR2bmlpeUMERkCPA4coKofVvYBZ2Dvr8O+NCNHjoxHjx59FdA8YsSIrYYPH37N+PHjlxWRx1R1k4J7/wJwClZ2KDBVVV+tw1a0qOpPc67h/kV2ebE5ZhNGAq8B+6jqf4r2iciWwHmJSywHvK2qk8N5uX5JRt6SdmQKcBvwf2H3AlXdLOe8zGuIyKrANcBo4KqKHkLae6vqwYk0rgHWx96Z66K9PCgs8+uxI+G8AzC/pBn4J7B/ouyuVzNHAQcmdq8GXKiq300cPwIru+ZllUM5+f4IEAEjMF0sAOYCt6jqAQV+yVOqenVWvhPH9VlddOXbDtc+D/PvFmPP56KyfRl56FDnCNsK32E4Jk/PtfqWue/Q7Uht9dSSOseWwJnA+JDM/wDHqmpcZAtE5GDgm9j3GQOnq+qVOXluAu4F9lLV18O2bwJHYM94sapOCtv3AY4FJgLfVtVzctL8InASpoMIuBjYM/wWYEVgPuaH3hvKix8DL1fyGfzXB4BtVXV23jMPx/ZpXSTS6uBbhrRfpv0bAbvf9xLndPquReTzwM+xdzsUuBn4YdDFaKwc2SjsuzBZTmTk6Q/Aaar6qIicCOyFlT+twAmq+ufU8VOAvwLfynr/IjIKuADYEHv3zwJfV9X3RWRH4PRw6PdU9c+hvFg5XPMj4fgI+Diwetm7L6Iv66LMjiSOy6qP5NYZ8+ojXY1rhW2ZNiboZmr4eamqnpKTZl7cpZP9CboYC6yA+deVb6QJ02uhD1FEX9ZFSCPPd98d+GHi0BWw8nO3xLkRVu+fVMlDVnmsqmeFfVOBX2H1F4BXVXXXnHx1sBUi8gPgK9j7jICfqeq14dhqbcUkzD40YeXVA1gsdIGIbAJciPkSPwe+Ee5hWeCj4RkBPIn5tVNV9dWs65TRlzVR5COE/TXHsot8i5w8/AJ4UFVvCO/sXMzG3KaquxfkvVH10LNV9cGca7jPWYNfGfYXxh2zypGwPbN9o+g7zsl3Uk+nAZ9P7F4b0+LZ4dhq7Uu63j0OmMHgq6N2Jc5QFO+q67yM/C2JgZfUcT4OXImtjPdSXr02pFl1e1ri2ayGlRuV8uJD4FbNiZFWQx/XRVFdM9e3kIK2r7I6TF55kZO/mmyMiCwbrntfng0qsI2d7E+wI0uHvzGJexqL6TnT/lRDP9ZFYbtnXn0jka+aY1o1fvfcZXf0AAAgAElEQVRnYm2dqwDrq+qzYbu3jfRi34vEMZntadShmTKfJXXdqvplFNWNUullfSPrYppYClgDi2nNBP4c/Isp1OjLJo7rs7ooil3UG9fozvhEYn9ROef1kcaUF3ltX2XtJqVlSeIa6T4UVbfBh+P3By4FdlbVWxtpK4oY5Lqotw9fnp4K67cZ10/bg4OB47Ay/3bgKFVtyzhvBPBLrO/WfOAhVT3E/YseqacW1UVz+3FlXKfm8iLk7XpgPey9/yfk7ZXuKC+aSp/WAEJVN1NrVNwRmKWqk8Jfl8QeOEFVNwjpfx24NgSz+goXAZPq2AewZeJZVToVTAIOBTZV1Q2B6zBxgwW/tg7Hrw+8DpyYlbBYw923sI8KEVkKE/OxqjoR2AD4e06+9lfVDVV1IyyYc3HYvj2gIV+zaA/ynQ78oPKRBC7EOr1ujesizaXAI6o6QVXXBX4HINZh+FdYgTMJOBkrMMGe5XdVdW3s3Y0Cjg77FmPvabuyTKV1ARwErIMFZtYNf1/JOLXS8WCtoL1XgNPCvr2Bv4Xta4vIemH7L4Fvp9I5M+R3Mq6LNJeSoYuifcHJ+Xz4XjfEnJ9flO1LE57RqYR3KiKfA74KbIbpYyEWLM7iHuxd3ptKcz1gzaCLu4F9w67jgd+pWrA55PV24N9Y8Gcw6eKDtra2p2fNmvXSzJkz75k5c+Y98+fPH0nqPQUnJz3AYDTWOWNnVV0DC4geXbYvjYiMGj58+Ec+/PDD14EFCxcuPHv+/Pl/jeM4qxxInteCdUDfK7zjC4DfhN212oqDRGRs1nXcv+hcXgSH9krgCFWdgH17p5XtU9UHE89vEuYDpBvHOvklGddP2xGA5xPnZXZkLrnGESG9DYD9RWSMiAzD7OBxqfNPA2a4LjpSRZl/KTXaERFZB/gvrCFoXSxYm64A1awZVT07ocNNsYpZWounAg/nPYScfL8a0rwaCx6OBJ7GBm1Bvl/yo/DtFNFndVHvtx3yfxNmk9fCfMBbyvalySkToOQdBvL8olp9y9x36Hak5npqUZ1jDlZPnIjVHbbAGmYr5NmCl4Ap4X3uCPwqBJ6y2AN4Tts7Mu8Wtm0azt8+cew0rLNrWUfCtzGfaD1skN3hwNHhvc3CGnzWAmaqNfpMADbRRIdrtUaqK4Hvdkq9M31dF5m+ZSD5jUzSREfmQNZ3fT8wOdzvJOCzwM5h3wmYpjbAAn37hqBgVp42A0ar6qNh09+x974BNgDmWhEZmTh+DNapMLMzQuAQLCi7PhYIbMbeP5iPsWP4OzVs2xd4DPhk4llsiNnCat59EX1WF9XYkZz6SG6dsaQ+UndcK0EnLYrI1lh5sV742yNsS6dZFHfpZH9CefYqsBOJbwTYlup8iCL6rC4SdPIhVPWGlGb+Teey+EjgX6ltncpjEflUYv9fEulmdmQOdLAVwDnhOW6EfdMXhPoqVG8rXgQ2D/ezPtaR6NCw73jgKMxv/VHQxLaYdmemfItfAj8uuVYRfVkTuT5CF2LZZb7FEkRkBczG/CFs+g9WNufFqtI0oh76s7zE3eesza+sMu7YqRwpad8o+o5JpdNBT6p6fKJM+yzW8HpdOLYq+xK4lI717k0GaR21rjhDSbyrrvPSpGPgFJdDH2Adzb5WxW1X3Z4W7MijWAf5pG/xaQpipFXSZ3VBQZlQ4luUtX1l1mFqaQ+t08achw3ozaTENnayP1injzeBKXTUxW4U2J8q6Ze6oODdl7SNdCWmVct3fzPW/p32eb1tpDHU3feijva0ohhp1W2lNfbLKKsbVcj6Rm4J7+1vmGZHh/s5ql5fNkGf1UVR7KLeuAbdFJ9IUVTOeX2kMVxKdttXmS7K+nEBmX0oqm6DD8evgPkESZ00zFYUMch1Ua8duZTs/jpF33IHMuzBqsCPsLrGmuEvM/aBaWA+MCGUS5Uyyf2LLlJSTy2rU15Kfj+uJXSxvLgMWCfk7Y+JazS8vBhUMzPnISL7AcdgzsEr2MjT/4jNuLE3MA8bNfY2NivCG+k0tOPsN+PIbnRDRF4CdlfVp8LvI4GN1Rokz8SCAcMwx+RAVU0Hw0ZhAlkX603/oqruWXaPqnpXOL+mfQVUZjQahY3UH4c5T6hqa8gbYjPqjgbyZgf6MjZ6uTIa5GvA/ar6cEhrETZrcla+08+8MiqklfaRCqOAhSLyaaBNVe9LpdEqIndgHWPvTO4bzLoQkTUxA/fFxLFvh/9G4W8M8A42I0Dl3b9GmKFGVdtE5O9YxanyLv9S0CkgSVoXG2INRhVd3Ym9g2tS96NYh9QKD9PeENwKjAqF/HBMF/tjI4VeTqXzroj8E2v4eSn1bFwXGboo0Uz6nsfS/r0W7kuxFdY5sNIYuCE2q8DckIfbgZ+QEQjW0OEgo5xrBYYHXSwFvB06h2yqqj/JyMN1WEeFC5IbB7gubgRujOP27DQ3N388mb/Q2P4j7JtJzmS6Azb6rPId/TZc++SSfWm+HEXRPW1tbR0qxmIjf4tYA3hHVSszMN4GXCE2CqxeW9Hh3RcxwHVRyV+eD7ExMF9V7w+/f4vZhwNL9iXztBzwOXIa9kpI25FGUNHMUGxAYBvwfeCCYH+WoDazzXKhbGxN7hvkusgt87tgR9YDpqnqu+H3bdgAlsPK8pmiSDM7A2+p6mOVDSFwuyZWidwwL9GCfC8CooRfsmOwY0OBM0QkrYvxwKsi8gr9VBeJ82v5trcD3lfVm8M9xliDXdm+NJ3ebw3vME/P9fqWd6YTysPLi+x9JXWOZxPHLRCRJ7FZbMvycXfi/6+LyFvYTFuvZRx+CB39le8BJ6rq++H8dxJpVWY9yvNvK8c9kvj/bBH5R8j3fYTyAvNVFwZdnAO8KSI30VEXuwKfEJE96Me6KPAty87L/K6146wlI0KeK+9kQ2z2whiYKyL3YN9XViPPISQaFLTjLMxPY+9paUL9OOTjDKyDaR4x7f4F2HuunF/xPSIsGA3WwfVbGeksBk4QkV0ZoOVF4vxOdqRAM0V1xtz6iHYtrlVkY74CXK7tM35eHrZ1GHRLQdyFAvtD504KOwAfA14WkacYwLooQkQmY2X6nxLb1sQ6EE+lo/9ZVB7XQgdbkXqOo7Hn2BT2VWsrkn7qUGwWlnQsdARWHoA1GJxFYpanhG+xjpiBPWggaaLIR6DOWHaNvsUBwA3BpqCqb2L2ep2c46uhrnpoomwrZTD7nCWaKYw75pUjFLRvlHzHaTroKcW+WKy8Umeuyr6U1LsHXR21zjhDUUyr3vPSdIiBF5VD4R7uqyJGWld7GtahOclXsXf/vIg8ygArL0rKhGTaHXwLra3tK0nV7aHUaGNEZG/Mj3yM/PpIrm2kwP7Q2S/eANhMRF4A/sEg0kXJuy9q/6g7plXjd39/uO/0Lm8b6Xh+j/a9KKi/1quZqttKqaFfRrV1o5KysxLTqpQlX8E6UC0ALsrwLyaLyMvAdPqpLhLn58bAa4lrdGN8Ygkl79DrIx3Pr1kXJT548rhOuigqS1Kk+1DU0gYP1iHxO9ikDBW63VYUMdB1EfJXsx0p0lO1vmwgXV7sDtxcqcuIyAWY/3l56vqjgf2AFRJ+aaU9xP2Ljuc3up6aW6estpwJ1FVeqHW0/lNi00O0D3xu+LsfVDMzZyE2g9Zp2BTaG2DTk/86cchWwDFqo9HuAf67IK2TQ/DmZmAPzQ40XQbsn/h9AHBJ+P9pqloZAft7OhqLCtsDY1V1Yjgu6eRME5GPFd9xXdwd0v6Z2JI5hA/2F8BrIvIGFrj7fvIkEZkGvIs5SHmGcQo2Qr3CRKBVRG4L17xI2mcq6YSIXCgi0zHHt/Jc7wTeF2uomY0F7U7BlmzM4iHMMUymO9h1MRELWlwoIk+E97EugKrOCOk/EZ79T7HlKzsgNiPVgXQs0KplCh118Tiwi4gsJTZK9EuUdEoIgdfDE9e/EjNcTwJ/wSpjB5H9PMF1kUWuLkr2Va55m4i8jRmmo6rdl2AKnXXxWRFZRkSGYEvglXZWSaKqL2Ijg5/Als27CptxJG9Gg0GniziOL6v8tbS0fKWlpeXnQ4cOXYuO7+lcbPan2alzV29tbZUoio6JomjEnDlztojjeEIURafHcbwGHRvbp2NLqGcxZd68eZtHUVRZTpEoilo++OCD03OOr/B/wEdFZNPwe+/w70o0wFYUMdB1UQUrkXi/wXY0idW6ivYl2Q+4I1EJqtDJL8lgCh3LC4AJoXx6RKzDRxFZ1zgb6yT2EDbjxseBT6jqFTlpDLryolpyyvx67chTwKYisqrYaNKvAaNTeqpXMxUOpP25EXyRX9E+YKqe+70Jm33zyXAP3wHewL6PLF1cg72ngaCLWr7ticB7InK9iDwZ/l2xin1pppB4v7W+wxy67FsW4eVFdRTVOcQCwF/GlmGuUGoLxBoEx2O+ZnrfUGw2muRsWROBzUXkQRF5TGw5troRkbWBzYG7wqafYQ0Vl2Id0v4b+I3aDAtpXXwCC57tRv/WRaZvGRgbnvPjInJMKPtLv2sR2UREnsYa++6iXRePA7uLyFCxAW/bk1+nmEK+rdgPeEXbZ+zeARinqjfkHF/hfGy2g3fC32xtX7L9WOwdXAocLRasfUhTDUKhvPg+Nkvangz88iLLjuRppqjO2MEnJaM+InXEtUq0WHpNKI27VGV/EnbkN9ig3IGsizJf70BsOdzKcoZN2CwgR5AaeJgkozwG+HS41r0i8oWc87JsBSJymFgnnyexZYHzOivlIiIfC7qcgZUdlRlQTsZmmr8DOEaswSBO5iHpW2D13/cZuJro5CM0Ipad41skmUK+naiGbqmHFuE+Z4f8p/3KXBtSUo4Utm8UfMdpppCvpwNon1UXqrQvFNe7B2UdtY44Q1FMq97z0kwh591XUQ4VIl1oT0uUFz/GVj4Z0OVFUV2TlG9RBZl1GGprD51ClTYm3Nt3sZUbcimxjVXZn4QubsJ84MGsizRFZXPdMa0G4W0jHenpmFZe/bVezdTSVjqFOvpl5NSNOpHxjVyCdfR6CBscfirwjKrm+Rd/xsqcgaCLvBh43r6iWFh3xCcyyXiHXh/pSD26KG37ChRppowpdPy2q60fICKHYytLpW1Nt9qKIgaJLuqlKj1V4bNMoT7NrI7FIn8U/Nu7RWSrsM/9i440up5aVKestpyBLpQXKY6kXV8Nf/eDvjMzsA1wm6q+FX6fT8flYO5X6+wGFqD6TF5CqnqSqq6OjVo4XWyJhTSXA18VkSEisj7WSFrpib6DiDwsIs9i03ZnTRv/FDZ7xrlisy0tSFx/ktpo3Eaykqpugi1DM5EwRbyIrIz16l9DVT+ONa5dljxRbVr2j2AjcfNmx1sBa7Cr0IyJ9iBgMhbQOyvjvMo1vh4c3hMIyx+papuqHqw29fqh2AdyIbBycO6vF5HkyLO3Qz6SDHZdNGOVkktVdXK4xz8BiE33fiQ2c+1KWFDkpkQAhlBZuga4S1Xr6cyc1sWlmMF5AJtK/+/YaM4ifo0tXXMOgKrOVdXdgy5+hM1QcyKwtYj8QUSuDrqu4LroTK4uSvZVrrkjNivU74EfVrsvQQddqI1IOxdrrLsXm0W7TBedUNUfhuexBxYMfgSrvF8dtJF8j4NGF1EUXRf+fSaKoqejKHp65syZK8ycOfM/8+fPX4bwnkRkT2ChqnYKoi9YsGAvbJaZVYH/ieN4EjarXLRgwYIv5z2HDFZoa2sbHsfxrMqGOI5nxnGcO9gl3M9szMn7pYg8BiyHLW2xqEG2oogBqYseJt04Bzl+SQZpO/IEsGIon/YCThKRvKUfM6+hqm+p6vaqupGqnk9YalVEvhrKiktSgb5BU17USk6ZX5cdUZt5/SjgWmwWzcpsABV7UK9mABCR5bH3cGVi8xnAuZoxoreG+52HzaaxIeYrz8GWZdsaG137pYRfcj/wXMjjQNBFLd92c7jfE9WWwn2I9jpH0b406fdb0zvMokG+ZRFeXpRQVOcQWwL3T8BZqvpk2FxqC0RkIuFZaPZM7ctgfk9yXzMW3NkKW/bsWMlf1rvsnpbHluf6RuKZPYp1cN0MazCYH/L+U2xGlZ0TSdyPLXfcb8uLIt8SeAub9WETbMaAL2OxAyj5rlX1sRDkXBErZytLo56GdSZ6DNPT3eTXKfJsxaex4NxXw+/xId0jc9JJUtHg8uFvmIhUlrK+T21Jvc2AF7Bl704P7/7XtM92sA02898bIY8DvbzoYEeKNNPVOmOdca0u25iiuEuR/cE6Lo8O9qeii5cZ2Loo9PXEOoR+jY6+x9HAPao6Le+ec8rjW8P1JmEzpF8k2bMhZtkKVPW3agNRNgd+IDbzVk2o6pvh+h/FOgXsFrb/Q1W3VtWNsfd+CuZbHgiMEZFzsPdf8S3exjosDkRNZPoIXY1l5/gWaTLtRJV0Zz20CPc5ydZMiQ0pKkcK2zfyvuMM8vyOT2DxrVtru8slecurdw/KOmqdcYa8tOo6L4O8d19NOVSI1tiehn0DY0KMtOJbvMjA9i3K6ppZvkURRXWYWtpDa7ExFwDHascVajpRZBuL7A/tPmcL7bp4lUGsizroSkyryxR99+JtI90a0yqJeRSRq5ka670198vIqRtl3VvWN/IuMCfkeQb2bA8PZcnGwBcTvuz92ARF/d6/CGTFwDP3VaOLnohP5PjEXh/pSD26KG37ChRppoy6bIWIrIrFG09K7+sBW1HEYNBFvZTqqUqfpV7/ohmbIPDJ4N8eB9woImPdv+hEQ+upJVRbzkADfEsRORab9btSh274u/fOzN1AcBrHAutn7JuOBXp2wJYgu1RV41Bp/CXWaLoeFmwekXH+P7FpyO/EPsynRKTTcY1CVf8d/p2Die2TYdce2Mi5SkFxOVZ4pM9vxZzpfXMuMY+O9zkdK1TfUpum/GpsdqmyfF4BbJNuCBCbTn1zVb0cGzl2DPbhnJ04bETIR7fSz3QxHZiuYcp3Vb0RWF5spqrPAbMqhkBVr8NG4CwDILbEyVXATPJn2C2jgy5C4VfpcLo1NpvW83kni03rvybwlaCj9P6tQ7r3YI3AU7EAT3Iko+uiM0W6KNqXzFMbcBEZZULRvkC6vEBV/1tVJ6vqlsAzFOiiDBERzGE/DWv4+x323JMjugaTLirLV++EdZJZ8tfW1rYT7e9pCvAZEXlNRF4L254TkYmLFy8eN3To0DewmWrWHTt27G+iKHoNOHbx4sXL0nF0+EpY55ss5gFEUbRSZUMURSuTs+xH6n7/oqpbBYf6HKxz9SvJY/qSrSiij+iiWqaTeL+hLGhTVS3ZV9m2OSBYMDyZzzy/JE3ajszRMKJdVV/FRlpmnlvNNURkX2zgwxtYI3Nl+dZvJw4bTOVFPflOl/l12xFVvUZVP6HW2esvwBvh/dWtmQT7Y5XjGYltW2EdCV/DAgLri832Wcv9Jlk9HFPxS76DdVas2y/pq7qo49ueDjyuqi+E31fSXjco2pcm/X5rfodF9CXfsoi+qot6KKpziC3fdSs2k0Wy40ihLQj+wG3Y0mT3k01WWTEd+H2os/wHewalddiMe1oOK8NOV9Xrcw77IjbjzlZYMOsb2Oj7ZF28v5cXU8jxLVV1QXjGhH+vov0dVvVdh/L8diymgap+qKpHhEDbdsBi8usUnd6/iGyBlT9f0vZg6XpYx+S/h/zsDvxERDo1DGANUDeq6nxVnY91fukUW6G9w2rl3X+TLr77IvqgLoBcOzKFHM2E6+XVGTv4pOTUR+qIaxVpsaprUhJ3qZBhf47GAuPdYn/6oi6q8PV2Bf6pqsnyYGtganhH9wMtQT9jIb88VtUZ2r6895PYgPus8j7Pr6yk8ww2k/qUsvsrSGMuVl7snbH7OOxZjAc+i3VIeA/YJHHMCMzf7BJ9URMFPkLdsew83yKDwndfRH+rhxbRF3VRRJFfWWBDisqRqto3Sr5jyNfTgcAVasvAV6jWvlRV72YQ1VHD+bXEGQpjWvWelyLL56y2HKr2nqttT/sZMJduipH2RV1U0b6V5VsU3WNRHaaW9tBabMwW2KCr17BZNHcQkdsyjqvKNmbYnyOxWem7xf70U11kUVQ2dyWm1VC8baTHY1pTyK+/1q2ZAp8lTU39MqqMVVX7jWyKdW6ulCWHY52u6y5L+qou8mLgBfumUBDXSOSpkfGJdL5K36HXR+rWRakPXqSZKsn6tqupH2yBTZ7xj6CTzTEf4sDkQf3FVhTRB3VRL4V6qsFnqVcz07Gy+/fh+o9gg1UmJA/qL5rpy7rIqKeW9b2opq4P9b/7ynW/iQ3u3FFVP8zY35B3752Z4W/AjiLy0fD7YExMFT4ZHjbYaJhOy2eISCS2vEbl9ybY6Ph/5lzzUqzD3FdpH2k5Fput8m2xZcoyR1SJyArAYlW9GQsiLYsZtoYjIi1i089XRm/sDlRmG3gV2EpseQqwGaieDceuKCKjw/+bsJHGz+Rc5hlgrcTv64DNxEaZA3weG7GQzttoSSy5IyI7Axr+kvySdgdqKazjWxu2TG+FdTKuMdh18TgwV8LU86GBTLHGj1eByaECQ2g8nQPMCHm8FGsAPkizp+Ovhg66EJERIjIu/H8lrNH+F1knis1StTHWmLwgY/8w4L9on9J+FKYJ10U5RbrI3Sciy6YM5R6EMqFoXwbp8oLKuxAb9Xk8Fpyrl9OxUc0L6VheLJU4ZtDoIo7jt8K//2ppafmwpaVlbhzH/4rj+F+jRo3agvCeVPUbqrqCqq6iqquE09dV1edbW1vfBzZtaWlZA7gtiqLDgOviOI5bW1vnYEsvVp7NYZgNyOKZ4cOHPwDcH0XRFVEUXQncO3To0Atyjk/eb0UjTdjyzL8NDUZJ6rUVRQxIXdTA48BIaV9a5jDg+ir2VejUOFfil6RJ25HlpX0ZesE6iHQ6t5prhPMPxgY+DAWG4P5FVZSU+XXZkXBs5TsfAfyEYAu6opkEnUbDq+oGiTJvL6zhZ4Ma77dyzDAsqDg23McorAP1Y7Tr6ZNYEOkp+r8uav22bwdWFJsBBDrWDYr2penwfqt9h9XQRd+yiEFdXhRRVOcI5cAtwMOqelLqvFxbICKrYUtZHqWqt+ddW1VnAe+IyCqJzVdj+iPUjz9Fbe8asU4EdwLnqOpFOcd8ChuxPhkLMseYLt6kY3mxBlY375e6KPItRWQ5ERka0h4F7EJ4h0XftYhMCPmsvKMdaK+PjE2UQRtgnRPOy8le2r/YFOuEtLuqPpG4h/tVdblEfm7Algo9OZ0gVr/ePryXJkxLz6ae46eASK1DUcVXrTCaUF5gHY+eZWCXF53sSJFmwvXy6oz/S059RLoQ1yqxMdcD+4nIyKC7/ciuA+XGXSoHFNifmI66mIyVSQNOF1X6egfS2ZfbSVVXCu9oK2BmeGdzispjEfl44v8rY/5Z1qCJTrZCEo3QYjMfbUSNg7FFZDWx2SAr7/+LdPYt1wS2CA0GyfKiDWucqPgW64S/gaaJorhkXbHsIt8ig7w6RSE9UA8tYlD7nCWaybUhReUIBe0b1XzHCbLioCMx25Kesa1a+1IU261cY1DUUaXOOAMlMa16z0vRqW2E6suhTKRr7WnQ0bfYlIHrW1TTvtXJtyhJM7cOQ5XtoYGqbYyqSsIfPRq4XW12tzS5tjGR/zz7Ax11sSGDWxdZ5NY36EJMqxvwtpEejGmV1F/r1kxBvTdN1f0yiupGSar5RkJZMhEb+P1xrCw5CCtzkv7FxvRj/yJB1uCz3H0lsbDuik8k77Xad+j1kfp0UeqDU6yZakh/20XlyRJU9WpV/WhCJw9jGkj7Ot1hK4oYDLqol6K21Fp8lrRm/oCtxrNsSOdgsjUzA3s/nw3Xn4A915dTh7p/0fh6alnfi7JypkJd5UVI91DgEOCzqpkDc6FB735I0c7BgKo+KyLHA3eKSIyJ9NDEIQ8AZ4YX9zbZI50i4HfBiC/CepDvqaozcy57I7bcx6NqvflR1WdE5HoskD0DG3Wzdca56wOn2aVoBn6mYfpxEZmG9X7PWqr1RtpH0r0oIs+q6vYl+9YGzg/PZSjwIO3LNd4IbAY8LiILgFlYgQAm/LPEGoqbsIp53qiPG7GGwh+H5zBdRH4OPCQibVhl+pCQz02Ak0Oleyng+lDJXox9iDunGrL3Cc/4/8Kmk2gfzXRMIg/bAz9IZmqw60JtRMkBwCViAdYPgd3C831cRE4H7hGRhdisP7uHc3YE9sEqH4+H/DygqkeE6z2KTRffIiKvA/+rql/PeRZLdAGMA+4OmgA4rtJYnNRFKJy/jy1D82C4/ququmsi7WOBi1S1UnD/FxaMXUhY3ito9zNYx8fmyomui0JdkLcvOBSXigXtIuy73iekX7Qvza3YUqxN2j7j9p3BSRiKVahvDnn5GDaT5qTw+xhspuFlw/XmAxNDI0Olg0BT6CAAVhm7AFvu8ZREHrbHnLklDHRdRFG0eXNz84WjRo1aZ9y4cREQtbW1LRo6dGhpAC2O47+3tbXd2dTUdGtLS0sztoztt6IoWh2YjS09cavYKMEnaZ8NOp3OTU1NTddgDfGbA03jx4//TBRFxwLjQnlyoar+OGUrAP5LRD6Jvcs7sEDOErpiK4oY6LqoUFBetImN0j5frNHlNcK3XbQvpDkSG9G9WepyRX5J1rNI2pEvY8uXtWL+72Wq+sdwvV2AXYI9quYaPwdOUhv4sFBErsYc/w9CvisdpdbFKkrLVU4c7LqgoMwvsjEldoRwzsrYd34N7aM7u6IZQtkxGuvgWBUpPVVj447FZrqKsUp3Mzbb5nRsVr31MF3sjM2m9zr9Vxc1f9uqOldslO/twT97DxsBXbgv5347vN88Uu+wsB4VqNW3rAovLwr37UB+neMgbDaTpUWk8p6uV9VTKbAFWNm+NHCyiFQ6nB6nqlnf/02YX3B++P1L7Dk/F35frqp3hnv4KgNM3SYAACAASURBVLakYwu2dObxwOdCY8TJwJuq+lvMP5kAHCoWEAL4b1W9JHHdU7GOtl/AdFfpjPAQFnDaG5spYOWwrd/qooCtsHe0GCsvbsVW3ijji9jsiYtCPm7CZr0EWxbvurBvPrB3QZ5uxN793eH3ediqH+eHewTYV23W1VxE5ELgT2rL/P0EWxGm0mngKexdV44dRvu7B3vXX8caMiOsTt0qIpdgcZf7GLjlRZ4dKSOzzqiq74vIIWTXR+qOaxWhqneHsi1ZXtwT7m+J/VHV3LhLIrks+3ML5rv8LGFHLsCe2YsMPF0U+npinbY+CeyZk98sisrjI0Tki7Qv2XyC2gzNWaRtxY/FYlatWBzzKFX9R8hntbZiS+A4sdhYM3APHeMVkGgwUNWnRWQ6Zku3wcqRF7AybBUGpiaKfIR6Y9lFvkXW/e6JNXYh1qH9fqwz6Aix2MWPVPWiHqiHVoX7nIWagRwbUoQWtG9Q3XecfE5L9BTYDXhBw4CdxDWrtS9F9e5KcoOljlpXnKEsptWF85KkY+C55VDwYf4FDKc4Rlpve9olWGP9iYny4jysPHiWgVdeFJYJRb6F5Ld95dZhSsqLrGdRlY3JOb+Sz8OAj6l1jC+yjRWy7M+fsdnUzlHVV8VWoDkPi4G+wuDTRea7L6pvdCWmVcN3j4icjdmOjwJ/EZH3VHXdSsI53723jXRvTCuXLmqmWp+l6n4ZFNSNUr5smT8FVpYcjc32eCPW7v9trPPbOcCnsVjWFljd6C36ry5yYxdF+wrolvhEne/Q6yN16KLIBw/nFWmm2rKkg/9YVJ5Iqg9FGd1lK4oYDLoI+2q2IyV1usK+Whn3m7QH/xSRU7AO7WB9Kq4MeUn3vTgMuFhEzsLiXPuqDeyv3Jf7F0ZD66lFdcqyciZFXeWF2MCn32B+6J3hvhaorUpEOL5h7z6K43onTh34iMhUYCdV3b238zKQEZE/A8drfgNAd157beB8Vf10DedMxXXR7fSyLrYH9lHVvGVass6Ziuui2xGR87Fg0E29cO2lsQrYpqGyVs05U+nnuoii6DFsxO71WCPFfsCEOI6/38V0o7gGJ2Ts2LGzhwwZMqW/2IqS9KbSz3XRH+hlO3IosIKq5nWczTpnKq6LXqU3NZNH0MVBwGu1+CVOZ/qbb1mS3lS8vOg1xGbU/D0282WfCagEXZwMHKCqf+3l7AxIxJaOvx/YTFX71LKJoRHpovS79/KiZ+iLPgTk2x/XRffTV20FgIj8DHg52dnJNdEYQqPPo8AXVPWtXrh+zfXQkvSm4rroNXpbT3l4HbVn6M0YeBF5MVIvL7qfvlomQL79cV00jv7Wjl6S3lRcF71KX6y/Bl18E+sw1RBfdrDS39rFStKbipcXVdPLfSgaaitKrjUV10VDcP9i8NIfyoumHsqP4xRxJLasSG+wInB4L13bKaY3dTEWm2nC6Xv8EJv9rDdYDTi82o7MA4k4jl8GmuM4XhzH8SWEpdSrIYqisWEm5jTr15KHefPmXTNv3rwjoihaMYoiqfzVkkYXcFvRP+lNO7IYm+Hd6V/0pmaKGIL7JY3AfUunIajqq8BZ9L3yYggwwzsydx9qq7p8D1i1t/OSJMy2cK+/+16lr/oQbn96iT5sKwDewGbadBqMqi7GZgXqLTvh9dABRB/QUxFeR+1+ejMGXoTHSHuJPl4muP3pfrwd3WkkfbX+GuNlSSPwdrHBS2/6j24r+ifuXwxe+nx54TMzO47jOI6TSxRF9wLbYUtxv40t7zQ1juMNqzh3T+BXwH+w5a2mxnH8aNj3RBzHk2vIx6sZm+M4jlerNg3HcRzHcRzHcRzHcRzHcRzHcRzHcRzHcRzHcfoe3pnZcRzHcZxcoihaGXgHGAZ8BxgHnBdmay47dxqwQxzHb0VR9AngcuD7cRzfFEXRk3Ecb9SdeXccx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecx3Ecp+8zpLcz4DiO4zhO3yOKor/Gcbwt8I04jo8D5gM/qTGZ5jiO3wKI4/jvURRtA9waRdGK2JJRteRnKLbkxNZh093A+XEct9aYJ8dxHMdxHMdxHMdxHMdxHMdxHMdxHMdxHMdx+hCDrjOziLyGdciaD4wA7gO+oaqFnaFEZCrwoKr+X87+c4FPAW1AK3C8qv61QXleBXhMVZep8/wtgfMSm5YD3lbVySLSBDwAjAr73gIOU9XXctLaBficqh4Zfu8MnIFp6XHgAFX9MOO8jwBXAKsA84BDVPWRsO98YEvgXWBXVZ0tIsOBO4EvqaqG43YI+w+p5zkUMUh1MQE4H1geWAQ8it3zvLD/+8De2Lt9BHtnC8K+E4B9sHt6HzhUVZ8TkeWBW8I5zcAL4byZOXk4ChimqmeKyMeBK4HJwEuquklB3kcAvwS2w97ZQ6p6iIgMBa4HVgVeAfZU1UUisgxwI7Cdqi4MaRwOtKjqTwuu8xqui6p0Ea77MvBsIrltVfW9RNojsHJiXsn7/QNwmqo+KiKfA34KrA/8WlWPLjhvFaysWx1YDJylqheJyKrANcBo4KrKOxeRKcDeqnpwIo1rgLNV9cGC67zG4NDF8lEUbQnsEkXRtePHj78Uu1/iOJ7R1NT0tTJbAbwfRdHqLS0tE4EzWlpahsRx/NSsWbN2BdbNOC/XVowePfrppqamj0ZR9NoHH3xw0uLFi3cDzheRNeghW1HEINJFXloXAwcAY1T1g7At145knL/EHoTfBwPHARFwO3CUqrZlnDcBuAxYGngP2E9VX2q0PaiXwaoLEbkK2AazJUs0UcW+A7EZ4BdjNug7qnpfKu1OWsu4fr16cv8i/9qr0DX/wuscJbguANdFJwapLrpST82rq+TWU7uzDisik4Czgcqz+J6q3p6R5s2YjWkDPgC+qarTCuzP9JDmS8BwTBfTgHEl9mcq/VQXIY1cHyJxTJZPugrZ9cQtgTOB8eH0/wGOVdU4kV49ddjlgEuAFYGhwN8w32NRxnmZ5Vg9dVjgagZZeZFKK9NHFJEIK9MnVa4TnuNtQOWeF6jqZmHf54GfY4NvhwI3Az+s6EJETgSmhvMuVdVTCvKU1MWJwF6YBluBE1T1z6njpwB/Bb6lqudkpJebNxHZETg9HPo9Vf1zsCGjgbmY/bsP+DZwDxavmZ2T76n0U024b1HOIPUtXBcluC6AHF2k7UuZ/5Dn/5X5jRn5+wX2bG8IaZ4LbATcpqq7J44bBVwAbIjFPJ4Fvq6q76fSK2ojbMFiG8sA96nqN8I5r4f7fJl2XcwCnlTVqwvyPpX+q4vcuogUtH+Ed3Qx0ITZ6Acwn35Bid9xMPBN7N3FwOmqemVO3pqAe4G9VPV1EdkHOBaYCHw76TuIyA+Ar2B+RwT8TFWvzUnzl8Bnw7FvAAeq6psisglwIbZa489V9bJwzoyQ1zdo18VawP6q+mrBs51K/9WF25ES3I4ArotODFJd1NvGvjvww0RSKwD3qupu4byq9BKO9b4XfU8XXl6U4LoAXBedcF0Ag1gXTd2RaD9gd1WdhHWkWhfYrYpzpgITCvafoKobhHS/DlwrFjDvdVT1QVWdVPkD/o41cqDWkePzqrqhqm6IdfD4RVY64X5OBU4Lv0djgZKdVXUNrFNrXkfDn2FO1wTgCOBKEYlEZD1gTVVdH5tlc99w/PHA7yofQsjr7cDGIrJ6vc+ihEGlC2Ah8F1VXRvYACsQjwYQ60D6VWAzYJ1w7HfCvknAocCmQTPXYQUiwAxg66C19YHXgROzLh4Cbd/CAnFgjbYnAV+rIu+nY0ZrQrhO5RrbAxryNQv4fOL4H1Sc6cCFwEEiMrbkWq6LKnQRmJUsazTRkTlwKvBw0cVFZDNgtKo+Gjb9E3tGZ+SftaR8ugkrN9bC3tUtYfcRmM42APYXkTEiMgw4GevcluQ0rLwqYzDo4iTs21oBOHP27NmzZs2a9casWbPeWLBgwTJUZysOHzJkyFIkbEUURTPHjx9/D3Bgxum5tiKKohVmz57d0tTUdNPYsWNXjuP4gJEjR+5Iz9uKIgaDLjoRHOM4ta2svEge28EeiHXe+BGwBbBm+Nsn5/K/Bc4NmjkXCxRB99iDehmMurgImFTLPhFZGvgVFvyahJXR56eO6aS1jHS6oif3L7oJr3NUjevCdZHFoNIF9ddTi3yPonpqt9RhRWQprFHnWFWdGO7l7zn3vH/4DjbCOsdcHLbn2Z8WLIi5Ie26mEm5/ZlK/9UFFPsXeT5pUT1xDvbsJ2Kdg7ags49QTx32BOAfqroB9t43JuO7LSnH6q3DDrbyAij1EY8E/pWx/flE7GKzxPb7gcnhfidhnXt2DtfZGtgDWC/87RG2ZeUprYu/Y3G0DbC68LUiMjJx/Biso3KnAQ/V5A3Txo7h79SwbQjwnKquTLsmdsIas79bcJ2p9FNNuG9RNYOqrHBdVI3rIqWLHPuS6z+U+H9Vt32IyApYGf+HsOk/WLmdFVc7BOtsuj5mm5qxle3S95vbRoh1qvpb0OjaQbMAAuyR0sWLwI9CI3seU+mnuqCgLhLIa/94Edg83NP62KQLhybOy/M7XgKmhGe/I/Cr0AEiiz0wu/56+D0NGyiV1bH8nPCMNwrpXiDWaT3NLlj9aYOQh+dp70x3PHAUsCkWV6vE7kYDn03p4hHgxzn5rjCVfqoLtyNV43bEdZHFoNIFdca0VPWGlJ3+N6F8r0Uv3veib+rCy4uqcV24LrJwXQxSXQzWzswVRoS/mWAvV0QuEZFnw9+xYfsBwCbA2SIyTUS2SyekHWezGEdOEF1EXhKRDRO/jxSRS8L/zxSRR0XkKRH5q4isnHH+KBG5XkSeD8ddV8sNi80Q8zmsl31W3sdiIxCy2AqYkags74CNKngp/P4tNto3iz3DflT1fmAB9kxbgeEh+LEUsFBs1Nqmmj0C+TqyO8A1kkGhC1V9TVWfDP9vwwJYlbQ3xEbhz1WbVeB2LKgF7TPAVEaDjMMafFHVVg0jO0SkGQts5Onpy1gBOS+cO1tt9sW5RfkOhfB+wIkhb6jqO2F3ayJfozA9fRpo09TMjmojdu4gX7NpXBfFuihERD6FdSC7ouTQQ0gE4FT1ZVWdho1gLWI74H1VvTmcF6vqf8K+ii6GYnavDfg+cEHS4IbzpgHLicia1dwXA1gXcRzfEMfxDsDpcRx/pq2tbes4jreJ43ibkSNHXkEVtiKO46fGjBmzFilbEUXRHnEcX5VxbpGtiKIoWoNgK0aNGvWZ5ubmkb1oK4oYsLrIOG9pLKCdbgyvpbzoYA+A3YGbVfXdUA5dQEZZHXyaycDvw6bfA5NFZFm61x7Uy6DRharelSiDq90Xhb8x4fd4gn8R8pKntTT16sn9C7pdF17nqB7XRWdcF4NEF12oj+TuK6qndmMd9mvA/ar6cDh2kXYe7Fm55/T7qFw/z/4APBH+rehiBmZ/9huIugj5y/UvCvyE3Hqiqj5bKUPUZvd+knat1V2HxZ7bmFC2DMc6GL2RcV5ROVZzHZaOq+8NivIinJfrI4rV6/ciBPWrQVU/UNXF4ecI7P1VvsmvAJer6rxQDlxOvu1Jxzb+rO0zoTyN+b1LJ47/BTaIe0adeatoZimsURysE9rPE8dXNHEN8PWBqgn3LWpi0JQVrouacF2Qb19K/Idc/6/ato/AAcANidjEm2qzZGWtdhbT7jMMxTT2esZxS5DObYStwKiE77JQRPYP1/t3OKaii+nY5CNfGIi6KKmLFJ03T9s7cw0FRpJfziTPu7vi24Vy5y1sYpEs0r7Fs6r6fNZ1Us94NPaMs/oDxNg7HxHe/xja9VPxLUZgszaDdVybSXtbTUUXdwE7iMhHB6IuMvLndqQYtyOdcV0MEl10IaaVvO5kzBb8KWyqRS/e98LoU7rIyJ+XF8W4LjrjunBdZDGgdTFYOzPfICLTgLeBV1X1jrD9ROyZrI9Nm72/iOygqpcAj2HLQk5S1b9kJSoiJ4vIK9gyf3toYmnKBJcB+yd+H4AtPwm29GBlttvf0x5wTrI9MFZVJ4bjlozuDR/jx0rufT/gjoQDUjn3NhF5GxPzUTnnTsFG2FZYiY6zm0zHltHsgFjgJ1LVGeljVfVFbMnNJ4DVgKuwJSwyZ28EHgK2zdnXVQatLsRmgzmQdsf4ceCzIrKMiAzBCrOVAVT1KayR5TUReQNrGPp+Kr1p2LTza2Kzw2QxhY56qpbVgfewGQAeE5G7RWSrsO9O4H0ReQqYjS27dQq23FYW1ejJdVGFLgJjwzt5XESOkTCCSWxWil+RMStEBlOoTxcTgfeCQ/Bk+LdSHp0N7Iq97zOBjwOfUNW8RmnXRYI4jk9J7O9NW3Hz2LFjn29tbT1g5syZ+zY3N9+6cOHCI3Ly0J22oohBo4sE5wI/SjnWUF5eJJlCHZoJ297Q0KAf/n0zbO8Oe1Avg1EXNRO+/UOBJ0RkOvBT4BuJQ/K0lmYK9enJ/QujW3XhdY5SXBeuiywGpS7CMbXUR0p9j6J6ajfUYScCrUHf00TkIsmeDa1y/QuD/TuV9meeZ39mkq2Lh7CZFAa0LnLI8xOK6olLEOvU82Vsqfiu1mFPwWb/eAt7P39W1Qcyzisqx+qpw45kcJYXme9eLOh+IfZNZC0/OUFEnhCRR8Q6bCXP3UREnsZmwryLoAuq9yuhuLzYD3hFQ2OD2LKM41T1hpzjq8nbsdg7uBQ4WkT2wxoifpHWhKq+jTVIjGdgasJ9i3IGY1nhuijHddFRF6UxiLT/QI3+XwFTqN7nPB+bceud8DdbVbNm6k2SbiO8ElgD65j9Fyw2chDmf+b5nD9gAOsiHJOui0BO+0c4/mPhWc3A3snvEufl+h2J86dgtvnxjH1Dseect9JLVnqHicgL2Hs9RLMHVt6Czbj2dvhbC/M9wepEJ2Cd0o4R67gWY/5FWhe3A89iPuyA1YXbkVLcjrgushiUugjH1NrGXuFA4CptHyTTqHpoEd42Ynh5YXh54brIwnXhusii23QxWDszV6YiXxYbcfrtsH07bJaTWFXnYKLr1FM/D1U9SVVXx2aAO11sCcg0l/8/e/ceJtlV1ov/W7kIJJiQhaDclFFB0UFRIl64HETtqKOOrUwQjwiCKI76C3qOgpcDiJcD+hiNyiiUwAAKiEgMOkLgiIqcowgIiIAHkEEFufxkJQESIbc6f+xdk917qm/T3VO7uz6f58mTqapdu1Z1v73W2mu/+91JHl5KOauUcu80B6fTq5a+qZTyt6WUf0xT7nvW7TPfmuRepZRnlFIOpXM1dvvH+O/rNPP7csstS7tt/+Ykd07znX+2/3rrrmkWRLZVrfVn27YfSrP48/o0iz4vLKX8USnlIZ3NP5TVr0zeqoWMi3bC/OIkr6m1vrx9z2vSLNi9Ks2k9N1pr7YuzVUlB5N8fq31LmlOlDyv953vk+Qzk7wzyeNW+ehTjacz03Scb661XpjmFqsvK6WcV2u9udb62NqU3f/BNBPp303yOaU5afmHpXP1TDYWT+JiA3GRtmpA+zv5pjR/y49pX/uVJM+otc6qRtW3lbh4SJqrRr8szcD5vLbdH6y1XlRr/bJa6zPTDLiPL6U8vO1jnttbWBYXq7fvtI8Vn/zkJ786ST7+8Y//j4997GOf/olPfOLrb3vb277yrLPO+pVzzz33dXMYK9ayUHFRSrk4yfW11mP919bpL/q2fX6xQ+PBqVqouDhVpbn12I+kubrzs9NUP7q8NLe1WTXWZjC/GHBcOOZYl7gQF7MsZFxs9nhkI3OPtY5Td+AY9sw0C3mPSXMniY8n+dXVNq61fn87/v10muOnteYzZ6VZmHx3mmOgaVx8KM0C5Z6Ni1nWmSesepzYef+npzm5+Ku1raCUrR3DHkpTefdOaZKQH1RKeegmvtKpHsOemQXrL9b53f/3JH9Vm8rVfX+fZoH+y9NcoP+k0qnUUmt9Y631S9L8Pd03yQNn7GM9M/uL0iQC/Xya2wunlHK7NJWjf2QjO12tbbXWv661fmVtbl3/T2lukzk90fx/0/SP3Zg4K83PZ0/FROd1c4u1LVRf0XldXKxNXLRxsZE1iFXmD5ua/61hM3E4/V3cqf3v00opq91KeGrFOcLaVIh8aDvnfHKa6rv/I03ls39L8o40Y053znmP7OG4mHUskrXPf0wraN8nyWeluXvC9BbYa8472s/7oun3rbfcaazrM9LE5KzXZqq1/k6t9QuTfFWSnylN0kLflye5V5o562el+Zld2r7/nbXWB9Va75vkz9LMX56QpjrcvyR5XZq/nW5c3C97OC6MI+syjoiLWRYyLja7ptV5363S3OnhpFyeDXJuZMBxob9Yl7gQF7OIiwWNi0VNZk6S1Fo/meRPk3zDNu/3NWkO6O4947V/TfL2NAe7j0pytNY6KU2C6K+lOVjdn+aqq1vPeP97k3xxmqugvj7JW0spJ203Synlq5KUNAees9p9c5JnJ3nEKrv4z16b/jUrrxj77Nxy26nufj/afv5nrLVtKaWkWWx/WpoD42el+Rn9ZmezW7ft2DGLFBeluY3u76ep7LTiio5a62W11i+vtX5NkrelWbRKmhNzb6u1frB9/PwkXzujTTekOUm40XjaqH9NM7l/Ufs5r09ztfs9uxuV5naiX1VrfX6aK8J/Is0E+zc6m204nsTFiX3OjIta66fqLbfr/Uj7/vu3b3tAmkW696U5eLt3aSoJzbKVuHhTrfWf2se/l2bxrP/dHpFmwP1AmkXZh6U5aHx8ZzNxsXbbTutYccMNN3xr++8/mkwmn7rgggvef/bZZ180Go1+MXMcK9ayQHHx4CQPKaW8r/37TpK3twvwa40jfacUM+1zd2n7rGnfdef+tts9HpyqBYqLU7WU5OraXPWZWutL0lQE+IysE2s9pxpP5hd7dBzpbzuUY461iIuZxMUCxcUpHqduaO6x1nHqNh/D/muak1YfbOP7hZlxfDKjDS9I8rX9ZIPe+FPSJMb8ZJrKadO4uHU2cDvrjRhiXKzhwVl9nrDmcWIp5Zw0P79X1Vq7yUZbOYb90TRVlG6uTTXHKzJjzSQb7Mc2cQx7ooLIAvUXD87qv/sHJXlU+/zrklzQbnderfVj7e8mtdbjaaqw3L+/89pUKHlFmnWwZOPzymRGf1FK+eo0Mfjt0zlvkv1pks/+rm3rQ5P8XCnlSWt98Rlt6+omod2x1vqw9jPelVti4ox0TqKcqgHGRPf95hbrWKC+ovt+cbEOcZFknTWINeYPpzT/m2Ezc87HJXlZrfWT7e/uDzJ73pG27WueIyylPChJaq1/1W73M0nG7f8XYs652rHIOuc/up97bZrfw39tH68572jn+X+W5Adrc4vnWU71nElqrW9Lcye7B894+VFpYvaaNmZ/L7Pj5wlpEtdul+ZuIP8tTQXPh2ZlXMyqaHcqbR5cXHTebxxZh3FkJnGxQHFxqmtareUk7621dtcftnQcukHOjegv9BfiQlyc/Jq4mGNcLHQyc2luOfhf0izmJs0tlB5Tmgpwn57mKtlXt699LMn5q+xnVEr5ws7jC5PcMcl7V/noo2l+6Q/PLRVhzktyfZIPte2aWQmolHLXJDfVWv84TSnvO6RZVNiIRyd5Qa31xFVepZQ79IL0UJrJ0yxvS3OboalXJvmKduKSts0vWeW9f9i+ntLcluI2Ofl2Sb+cplrO9WludzhJsyhybmebe6W5gmHHLEpctPs7muSmJI+pvdL5pZTPav9/QZIn5pbbSx1P8oDS3HY1Sb45zS2kUkq5Wynltp39f2c2Hk8b0p6w+Yu0A1Up5Z5pfq7v6W36a7nl5F43nm7b2WbD8SQuTrw+My5KKXcsze3Opgu635bkLUlSa/2SWuvda613T/NzelttKgnNckpxkeYk3t1KKXdqH39jer/bdsB9bJoB9+w0lYjExcbi4pTGitFo9Larr776yTfffPNFZ5555j+NRqN/uP7661/1yU9+8vaj0egNo9HoxaPRqHvF7kljxY033vhvo9HoVUn2jUajl19//fVv/sQnPnHmVVdd9dIbbrjh6zOnsWItixIXtdbDtda7dv6+k+SLa63vaPe52jjS1/+7/6Mk397G3Rlp/m5Pml+0Jw7e0n7ftP9/c631/+9tuq3jwalalLjYguNJvrw0t2lNKeVr0/wc/mO9WOs51XgyvxjgONLak8ccaxEXM4mLxYmLUz1OXetYZdXj1B08hn1Jkq9sfzfJjOOT9jNvW0q5W+fxtyap7X9d3fFnlJXjzzQu7pWmIuuei4u1rDNPWPU4sTSLx3+S5G9rrU/q7XMrx7DH289Jaap6fH3aNZOedfuxTR7DXt9530L0F2v97mut31Jr/ez2+Qckuard7mOllDuV9rbw7c94Ke36RSnlnm07U5p1r2/KLX3CHyb53lLKbUpzy+Dvzepjz4q4KKV8RZr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k926t3Sb9BFF1KfIXk9y6dSsHn9Rae2uWZ078SFb+/P8hyQ/09eUlSYYd+JsmuUu6jtqfJ3lD/9pfSnLq1r31o1tr7aPpOjM3TPKQdEGj35fu+/vQqjqptfbTObCy8IJ0F0dP7x/3s1me+eyWSe6WbtDuCdVlUr17kk+01m7bupWJrx4V46/S1ZuJeyd5SVV9d//zD/fnmquSrHRRpU7NuT6Y4z5JTk432PZ9/a9W+uz+d5K7tdZum+4zSrqO2YlJTu7PMS86MqVnNdWt5vuJdNspreYzfdDps9JdpF+a5SsRlwUbVNXtknyodQswFtItlrpjurp0+6q6U1Y/z0y1QhvFFjlEPflyksW+Xtw5yR/0g7TPT993rC6LxH3Sna+X6T+3K6rbHidJHpwuCGHiiv78/swkf9jfd6i2jBkb1ZWV2vJfTfLIvo/wo+mD1ZO8pr/vtkne2w8CPj7JXfs69q4sDyj5bGvtdq01g3mz9+Yk31VVP1VV76iq91TV/+kHcFJVP1Zdhpn39r+7flWdUFVvqgNZdH60f+xCVb2tqt5dXcbn4/r7L62qp1bVu9MtqFmz1q2S//Uk315Vt93ct75zbOb3O10mk/cOg9L7n9+b5NYrlaEPTP1Euow3d+4nhpbSLWq6bQ7Ujd9J8p6+3/lbSV44OMwhryX666BnJLlX3899fo5wMNV2tsn1ZCXruq6tqsnjbtuX7Q5J0lr7y3Rty/36/uSX+mMs6/Me9h9jhzpCdeAZSV4wuLb8o/7+P0ryf/vzwSSgKEn+V/99vkOSX6yqb26tPTYHxkvG4xU/k+6a5bZJ7prkaVV1Qv+7702XIf5W6SYofnjtfx3Wq7qF0T+e7trvUB6eLujw5HSf82X9/TdLt+D/1ukmeCaJGg41vn2TJD/UWhPIPDtvS3LjZP9uG3fof75BP7Gfqrp1Vf1j35d8f3ULY5M+gL26zIavrarr9o+/aVW9uqourqo3V9Ut+/v3VNWzq+od6QIF1qUfb31tunMYG7CO73vSnaPvneQ2Se5dVd92iMdqM3aATag/t07yvkMEHK40/pUcPAd35uG/EzZqSl1Yqc0/6BywxnmtC9IFLE+cnuSCQ4x9p7///NbarVtrApm3xrDvsFKbf6Oqenl1mY/fV12ylFTV/Qd9iuf0dWh/5uSqOqeq9mdsrW6Xhl/tf/61qnpn3xf5nf6+E6vbKeyFST6QboHMmrXWPpVuPu1Rg/MMq1jLd/8w5pRul25saBJAtpb+x92T/E3/80pjF9Ouh1e1Sn+EwzSrumPuY9v469ba9/X9hX9OF5OTrHwtkaq6RboYmjNaa+8cHe9xSf6+tXbHdH3Kp/XBrMnB9Yb5NPM6UVV3TBdMveNjI2Rm3jne0Fr7ryT/VVVXJPnb/v5L0mVUOS5dJouXVe3fXenrj3wxWYePtQPZCv88yW8n2dta+3/9fZPVqs9MNyDzJ1X1yiSvHB9olc//Juku2E9Id+LcO3jq37Uuu9Ul6YJvJ4Fql6QLbmTjFtJ9R+/V3z4+3QDJ3tHj7prkVoPP7xsmASJJLmqtfSXJV6pbyXOjdJ/RH1TVU5O8chyo2Fr7dFV9tKp+IF2m91umy1zzyHSrSN/Zv9Z102X0XUad2jZ+NN1K0SuTpLqM39fJyp/dW5LsqaqXpsvwnnR179mtz3DTWmtHqOysbLKCO+mC0P4k3Wd6KJPP8+J0kzUr+aWqenCSm6fL5Jx056mFJO/pbx+X7jz15hziPMPMTasnQ8ck+b1+cP7qdIPGN2qtXVpVn62q703XnryntfbZFV7jeUkeXFW/nG5A546D37148P/T+5+ntmWttS8c3ltkk0yrK7fI9Lb8LUnOrS6b71+31i6rqncmeX4fRPg3rbX3Vpel4lZJ3tJ/3l+XbnJi4oItf1esahC09uocCFrbV1U/ny5o7VdyYJD+LX3/78vpJmZe01p7cj9ofOxoEPeLVfUb6QZxn9i/3Gf7wd11a10mpfel66++77Df8M60Fd/vzSrbDyR5U2ttb7Ksj/kj6QPWWmt/X1XfXFXf0P9utWuJW6Rb6P26vpzXTJdJmkPb9HpyiNda73XtDye5sLX25SRfrqq/PeiIy621z8tyR7IO/GAOfDZ/lgMBiHdJv6CuXyBxRX//L1bVZCH2t6W7Dlmpb5p055AX98f4ZFX933SLej+f5B9ba5clSf9+T0zX/rG5JvXpxukmh163yuPfluRxVXWTdHXqQ/05Yu+gLl2c5MQ1jG+/rK1tBwi2wCCYYHztOTYJYH9RdbtwXDPdOf9mSe7bWntoPy71s+nGxJ+b5OF93fj+dMFsd+mPNQlgP9zP/d3p+pgcnvV+35Pk9a21K5Kkqv4pyXek23lhGm3G0W2z6s8y1e1SuDvJDVtr35oVxr/6h4/n4H4x3c6oHFkH1YVV2vyDzgFV9YCsMq/VWntPVd2wqr41XTa8z7XWPlZVZ2X62Pe/J/m31trbt+A9k6l9h5Xa/EmQ0WL/nONqeQD7V6vq/HQB7MMF0RekS7Lxx/3t05PcrZYHsB+T5BX9OeLf+/sfdLife2vto30Zb5jkk4dzjB1kvd/99XjdaC7zUP2PN1T3Yl9IF4uRrDx2Me16+DCKxwbNuu58Y8x9bAe7qupJ6T6v49LttpBM70d8U7q+wYVJfqa19k9TjreQ5KerXxSTLt5ikr19XG+YTzOtE/0Y65+l62estvPDUU8w887xlcHPVw9uX52uHlwjyX/2K8XYHsbblv1nkm8eP6i19rXqVnD8eJJ7JXlUDgzoThzq839GknNba6+oA1uoTXylf42rq+qr7cBWapN6xWGobiuCq9INphyTbpXgaw79rFwjXYDJl0fHSpZ//69Kcq3W2v+rLrPqPZI8qape31p7YpZ7SbqL939JF/C6r7rVwi9orf3mGsqjTm1PK352rbWH94NEpya5uLrMzsyfL40/v6r6WpbvyHGd0XMm54mrcujv2tNbt7XeT6dbJHPTdOepp7TWnjN+8ArnmWFZxuXgyDmonozcL92F2O37Ad9Lc+Dzel6SM5L8j3RZLVfyV+m2zPv7JBePgp73Tfl5alvGzE07p0xty1tr51TVRem+92+pqru11t7UD/ifmm5BzLlJPpfuYv2+K7zmF7fovbA22zGAXUabw7MV3++3Jjm5qq4xGXSrLpP/yUn+KV3bcag+yUasdi1xTLrtOn9wE19zJ9j0etJae2GmW9d17WG8l7X2eVnuSNaBNetf965JfrB12z6+MRs7p2xGHWN1X2qtnVxVx6abGHpkugCUqdeJrbW/qC6z7qlJXlVVv5Dkozn487puVh/f1secje0cwK6PuTHr+r73tuRcrM3Yljar/vxTkttOrk9aa09O8uSqmiycP9T413gObnybI2NaXdiT9c1pr3Ve62Xp5lH/Rw6MU0wd+66qE6NvsVUEsJNs/Lt/qPZi/N09VLt+53QxGC9Kt1vYL2fleYyDrodH5ZhWFjbfrOvOMTH3sR3sSXLP1tr7quqMJKes8vgr0rUDP5Kufzl2TLodBv91eGcfV+Ez3x72ZEZ1orpkLRcleZx+Rucaqz+EbeS/klz/cJ7YWvt8kr1VtTtJquqYsk3vvPv2qppMwv5cui0qTqyq7+rve0CS/9tf4B3fWntVkl9Kty1aMqgvq3z+xyf5eP/zg7byDZFUt53ys5M8s598f02SR/TBIKmqm9eB7QeGXpvk0YPjHLIz3l+cX9la+/MkT0u3lcHYy5OcluS+6QKbk+T1Se5VVTfsj1NVdVCGA3Vq23hTkntW1XWr6vrpMu1emRU+u6q6aWvtHa21/53k0+mymLwuyS9Ul70xVZYZz6l/S7dS/Our6hvTLXBZzYr9itbaK9K1Ow9Kd576X317k6q68WAQcNp55tJ0A4nJga2A1/zaHDHHJ/lUP5Fz5yzPZvPydNuqfV8OrEw9SD+Y95p0W7n/6ejX9x78PwloXFdbxkxNbcv7duKS1tpTk7wzyS37fsInW2tL6QLhb5fk7Ul+eNJvrarrVdXNj+g74FAm2xqe3Fp7dGvtv9MtRntma+02SX4h/UBua+2cJD+fblLoLVV1y9bam5LcKV0d2VNVD8yBQdzJcW/VWnvI4DUPe0Cvuow2t0k3wcXGbej73Vr7cLoJv8cPjvn4JO/uf3eoPsmw/X97kjtV1Un960/6mG9Ovx1wH5Tymf7aYy3+Ncm3TK6jq+raVXXrNT6X5TbaDqxkvX2BtyT5qaq6Tt8X/cnB7/Qnt9ZW1YG3JrlP//P90n3nk24s4hH9a1yzqo7vy/C5Pijtlukyuk98dTKOMvLmdFu/XrMff7lTkn9c87tm07Ruh6hfTPIr/XjCpTlwnTjZnWyy4P+jrbU/Spfx5nsOcUzj2/NpsiDiO9L1CSfbua8YwJ7kp9Nty/2qqpok5ZgWKLA/gH3w77sHj9vopPH3Rh9zw9b6fT8M2owdYKP1p78GeVe6RAvXTJKquk4OLFY41PjXeA5O9u0ZGtaFHGIOI9PPAWua10oXwHyfdHXrZf19U8e+t+I9st+0vsNqbf7YJIB98thbtNbOnvK4SQD7vXNwAPvkud/VWptkh95Q36KWJ7NiDdbx3R+PAVyazelvpHU70z4myQP78ampYxfTroez9rm5lfojHKYZ1h1zH9vD9ZNc3n/v7je4f1o/Ikn+O8liuvPAz0053muSPLq6xICpbndbtpeZ1InqdqR6eZIXttb+clPeyVFAMPNRpHXZ7t5SVR9IFzS0XvdL8pDqtuj9YLogRubXvyZ5ZFX9c5JvSrdF+4PTrUq9JF32qWenO+m+sqren26w5Zf7578kya9V1Xuqy6y50ud/dn/Mi5N85oi8s53nulX13qr6YJL/k+4i6Hf63z0v3Uqed/ff7edkeqaHX0xyh6p6f3XbmDx8lde8TZJ/rG518xOSPGn8gNba59IN2H9Ha+0f+/v+KV0wwmv7OvW6JCes8Brq1Jxrrb073QDN+5L8XbqL62Tlz+5pVXVJXxff2j/veelWnb2/f/y0zhoz1lr7WJKXJvlA//97Dv2MJMnfJlnsz08/OuX3T0zXpvyfJH+R5G19+/OX6dqelc4zv5PkvKp6V7qBu2nGbRRH3ovStSuXpMts8S+TX/SBjW9I8tI1ZLl6Ubo+yWtH939T346clW6xVbL+tozZOTvT2/LHVNUH+s/2q+nallOSvK+q3pNuYuC81tqn02X3fnH/2LfF9s3zbi4D2PuBpaek2/73/Rs9Hkk2+P3uH/uQJDevqo9U1UeS3Ly/b7U+yXOTvLqq3tCfJx6W5K/7PuZkUvHsJLfvy3FO1rE4sm+/7pXkqf0x35susxPrd3Y2Xk+S7hrisv7fuVlnX6C19s4kr0jy/v61LsmBreT3JHl235e97obeLdOcna2pA49O8uD++Q9I11dM//+d+77pxemy/b86ybX6cbFz0rU1E8/tj/2iUblfnq6+vC/d7iG/3lr7jw38HdiA1tp70n0e903y++kW878nyQ0GDzs9yQf668pdWb41+DTGt+fUdgtgr6qfTZeV8cWbcbydbo3f99VoM3aoTag/P59uV9MP9+ORr0vy6/3vVhz/ysFzcM/a6HthY0Z1YaU2/6BzwFrntVprH0w3rv3x1trl/X2vzfSxb7bY0RbAXgcns2KN1vjdH88pHW5/Y6UyXJ6uX/jIrDx2cdD18Drm5lbqj7ABs6g75j62jd9O8o50iRKG/b9p1xJJktbaF9MlUvil6nYwHvrdJNdO9z3+YH97qqp6c7o258f7a5u7bcL7YeNmVSdOT7dw9ox+HPu9JdlXjtm3T18Jtpvqti96ZWtt16zLAgDsLFV1jSTvTrK7tfahVR77q+l2iPjtwX2XJrlDa82CFphDVfWF1tpxo/tOS7d48nPpJvK/r7V2SlU9I912i1enGwQ+I90k0K+lG7T/QpIHttb2VpdV76k5sA3o41trr1jLOaGq9qS7/vnL6raEPiFddr6vT7eo5nGttf/chLcPbDNVdVxr7QvVbR/6piQP6xdtAjAHxn3LqvrbdIEc7+z/vyrddqr3b62dWFWPTReY+tUk/5Fu0fw3ZDAW3l9nHtdaO7u6XRyela5/eO0kL2mtPXHYfzxE2c5I1w99VFWdneSh6XYiu166gJPH9QFwwA5jDg5m5xB9h3/I9Db/RukCQSdZjx/RWntbVd07yW+mS+731SSPbK29fTwO1Qcnfaa1dufBa56VbiFE0o1t3b8/9qrnheHxq+qqdItur51uV4o/S3Jua+3qw/4DAQAc5QQzwzZkIAUAmIWqulWSVyZ5eWvtV1Z57MuT3DTJXYZBioKZAYDNUlV/kS4jxnXSbSP8lBkXCQCAbc4cHAAAwGwIZgYAAAAAAAAAAAAAZuJasy4AAAAAQFX9cZIfHt19XmvtT2dRHgAAtr+qenCSs0Z3v6W19shZlAcA2P6q6h1Jvn509wNaa5fMojwAAEcLmZkBAAAAAAAAAAAAgJm4xqwLAAAAAAAAAAAAAADsTIKZAQAAAAAAAAAAAICZEMwMAAAAAAAAAAAAAMyEYGYAAAAAAAAAAAAAYCb+f9miVhGVIr+UAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8h3lAStbhih-" + }, + "source": [ + "# **Save Plot as a PDF file**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tI0YcNB5hkNx", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 594 + }, + "outputId": "5644af41-0eb3-45d3-cd34-89b2bd59dc56" + }, + "source": [ + "plot = klib.corr_plot(df_cleaned, annot=False, figsize=(12,10))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HMxWGD5NihFS" + }, + "source": [ + "plot.figure.savefig('figure.pdf')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "M4ZeJoCMRQVH" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 4caaa03433e4a167fb4249343fde95119551beb6 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 9 Jan 2021 09:05:12 +0700 Subject: [PATCH 64/73] Add files via upload Progress bar in Jupyter notebook for tracking the progress as your machine learning model is training --- python/model_is_training_progress_bar.ipynb | 208 ++++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 python/model_is_training_progress_bar.ipynb diff --git a/python/model_is_training_progress_bar.ipynb b/python/model_is_training_progress_bar.ipynb new file mode 100644 index 0000000..6c6e46f --- /dev/null +++ b/python/model_is_training_progress_bar.ipynb @@ -0,0 +1,208 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "zzD4-HxqXBmt" + }, + "source": [ + "# **Progress Bar in Jupyter Notebook**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "**Data Professor YouTube channel**, http://youtube.com/dataprofessor" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "An7XU557Y5ci" + }, + "source": [ + "# **Progress Bar with the tqdm library**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3yc04janmetd" + }, + "outputs": [], + "source": [ + "# ! pip install tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "gxa8jup1DNjt" + }, + "outputs": [], + "source": [ + "from tqdm.notebook import tqdm\n", + "from time import sleep" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "009bdoXCE74q" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "93cc2d7933af4faf96fda14e55f24e23", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00 Date: Thu, 14 Jan 2021 21:50:45 +0700 Subject: [PATCH 65/73] Update CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb --- ...Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb b/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb index edd3a3d..4023c21 100644 --- a/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb +++ b/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb @@ -33,7 +33,7 @@ "colab_type": "text" }, "source": [ - "# **Bioinformatics Project - Computational Drug Discovery [Part 4] Comparing Classifiers for Building Classification Models**\n", + "# **Bioinformatics Project - Computational Drug Discovery [Part 4] Regression Models with Random Forest**\n", "\n", "Chanin Nantasenamat\n", "\n", @@ -1614,4 +1614,4 @@ ] } ] -} \ No newline at end of file +} From d9bf90748c8aebd6dc89b99c2d37d345dcb0bb1f Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 16 Jan 2021 09:12:11 +0700 Subject: [PATCH 66/73] Update crypto-price-app.py --- streamlit/part12/crypto-price-app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/streamlit/part12/crypto-price-app.py b/streamlit/part12/crypto-price-app.py index 1a10102..3b2f474 100644 --- a/streamlit/part12/crypto-price-app.py +++ b/streamlit/part12/crypto-price-app.py @@ -15,7 +15,7 @@ #---------------------------------# # Page layout ## Page expands to full width -st.beta_set_page_config(layout="wide") +st.set_page_config(layout="wide") #---------------------------------# # Title From 027e657f961692e685aff038f73122e68dc9ad1c Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Fri, 22 Jan 2021 22:02:57 +0700 Subject: [PATCH 67/73] Add files via upload --- ...tylcholinesterase_Compare_Regressors.ipynb | 1139 +++++++++++++++++ 1 file changed, 1139 insertions(+) create mode 100644 python/CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb diff --git a/python/CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb b/python/CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb new file mode 100644 index 0000000..773945b --- /dev/null +++ b/python/CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb @@ -0,0 +1,1139 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "xNq-XQWWYNXh" + }, + "source": [ + "# **Bioinformatics Project - Computational Drug Discovery [Part 5] Comparing Regressors**\n", + "\n", + "Chanin Nantasenamat\n", + "\n", + "['Data Professor' YouTube channel](http://youtube.com/dataprofessor)\n", + "\n", + "In this Jupyter notebook, we will be building a real-life data science project that you can include in your data science portfolio. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n", + "\n", + "In Part 5, we will be comparing several ML algorithms for build regression models of acetylcholinesterase inhibitors.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OnVZXG7QY1D2" + }, + "source": [ + "## **1. Import libraries**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B4i2iuTlZYvQ", + "outputId": "1172a063-ee94-4abe-a8a8-fa0b0ba1073e" + }, + "source": [ + "! pip install lazypredict" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting lazypredict\n", + " Downloading https://files.pythonhosted.org/packages/5f/db/1566dca1050ea74e9474dca0f1e7bbffcb0c3e694cf92e7e6e7ef9fca3af/lazypredict-0.2.7-py2.py3-none-any.whl\n", + "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.6/dist-packages (from lazypredict) (7.1.2)\n", + "Installing collected packages: lazypredict\n", + "Successfully installed lazypredict-0.2.7\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vf73M_f9XwZ7", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4256ee85-6e74-4322-fa05-9e79a1779dfd" + }, + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "import lazypredict\n", + "from lazypredict.Supervised import LazyRegressor" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n", + " warnings.warn(message, FutureWarning)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lUGwBVH7Y8Rq" + }, + "source": [ + "## **2. Load the data set**\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oiess4VaZChk", + "outputId": "b9956d3a-6631-420c-b7d5-fc69ca407cff" + }, + "source": [ + "! wget https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2021-01-22 14:57:46-- https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv\n", + "Resolving github.com (github.com)... 192.30.255.112\n", + "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv [following]\n", + "--2021-01-22 14:57:46-- https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 8363909 (8.0M) [text/plain]\n", + "Saving to: ‘acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv’\n", + "\n", + "acetylcholinesteras 100%[===================>] 7.98M 46.7MB/s in 0.2s \n", + "\n", + "2021-01-22 14:57:47 (46.7 MB/s) - ‘acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv’ saved [8363909/8363909]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WVojHjyWZC9D" + }, + "source": [ + "df = pd.read_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv')" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "KbTq4zzKZpVf" + }, + "source": [ + "X = df.drop('pIC50', axis=1)\n", + "Y = df.pIC50" + ], + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7Qa6WSGGZ39n" + }, + "source": [ + "## **3. Data pre-processing**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CtC4SWpBaCVQ", + "outputId": "1008b65f-5b93-423a-b2bf-0695dc2014ec" + }, + "source": [ + "# Examine X dimension\n", + "X.shape" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4695, 881)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mVHzT2f5Z8sF", + "outputId": "66fc3cdb-2f97-4694-b915-58c95a49b3d2" + }, + "source": [ + "# Remove low variance features\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "selection = VarianceThreshold(threshold=(.8 * (1 - .8))) \n", + "X = selection.fit_transform(X)\n", + "X.shape" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4695, 137)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MYck-_XeaHNZ" + }, + "source": [ + "# Perform data splitting using 80/20 ratio\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zkW9nKohamEj" + }, + "source": [ + "## **4. Compare ML algorithms**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nnFbUjA1aUj1", + "outputId": "84de30a4-1459-4506-fefd-d4495c30fbee" + }, + "source": [ + "# Defines and builds the lazyclassifier\n", + "clf = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None)\n", + "models_train,predictions_train = clf.fit(X_train, X_train, Y_train, Y_train)\n", + "models_test,predictions_test = clf.fit(X_train, X_test, Y_train, Y_test)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + " 90%|████████▉ | 35/39 [00:57<00:11, 2.82s/it]" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "[14:58:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:59<00:00, 1.51s/it]\n", + " 90%|████████▉ | 35/39 [00:48<00:08, 2.18s/it]" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "[14:59:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "100%|██████████| 39/39 [00:50<00:00, 1.29s/it]\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cHOFkp-ka5tF", + "outputId": "7abc08e9-e5b4-44e2-955d-dec0c20b91a1" + }, + "source": [ + "# Performance table of the training set (80% subset)\n", + "predictions_train" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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R-SquaredRMSETime Taken
Model
DecisionTreeRegressor0.860.570.16
ExtraTreeRegressor0.860.570.16
ExtraTreesRegressor0.860.575.93
GaussianProcessRegressor0.860.575.10
RandomForestRegressor0.830.644.67
BaggingRegressor0.810.670.59
MLPRegressor0.770.756.83
HistGradientBoostingRegressor0.690.872.55
LGBMRegressor0.690.870.50
KNeighborsRegressor0.650.922.98
SVR0.581.016.45
NuSVR0.571.024.82
GradientBoostingRegressor0.461.141.72
XGBRegressor0.461.141.28
Ridge0.331.270.05
LassoCV0.331.273.96
ElasticNetCV0.331.273.83
RidgeCV0.331.270.15
TransformedTargetRegressor0.331.270.07
LinearRegression0.331.270.09
SGDRegressor0.321.280.16
BayesianRidge0.321.280.21
HuberRegressor0.311.290.61
LassoLarsCV0.311.290.45
LassoLarsIC0.311.290.11
LinearSVR0.301.300.74
AdaBoostRegressor0.231.361.15
Lars0.231.360.12
OrthogonalMatchingPursuitCV0.231.370.15
OrthogonalMatchingPursuit0.231.370.05
LarsCV0.151.430.47
Lasso0.001.550.08
ElasticNet0.001.550.05
DummyRegressor0.001.550.03
LassoLars0.001.550.06
PassiveAggressiveRegressor-0.131.650.10
KernelRidge-13.705.951.46
RANSACRegressor-177394519750505689776128.00653870642365.761.11
\n", + "
" + ], + "text/plain": [ + " R-Squared ... Time Taken\n", + "Model ... \n", + "DecisionTreeRegressor 0.86 ... 0.16\n", + "ExtraTreeRegressor 0.86 ... 0.16\n", + "ExtraTreesRegressor 0.86 ... 5.93\n", + "GaussianProcessRegressor 0.86 ... 5.10\n", + "RandomForestRegressor 0.83 ... 4.67\n", + "BaggingRegressor 0.81 ... 0.59\n", + "MLPRegressor 0.77 ... 6.83\n", + "HistGradientBoostingRegressor 0.69 ... 2.55\n", + "LGBMRegressor 0.69 ... 0.50\n", + "KNeighborsRegressor 0.65 ... 2.98\n", + "SVR 0.58 ... 6.45\n", + "NuSVR 0.57 ... 4.82\n", + "GradientBoostingRegressor 0.46 ... 1.72\n", + "XGBRegressor 0.46 ... 1.28\n", + "Ridge 0.33 ... 0.05\n", + "LassoCV 0.33 ... 3.96\n", + "ElasticNetCV 0.33 ... 3.83\n", + "RidgeCV 0.33 ... 0.15\n", + "TransformedTargetRegressor 0.33 ... 0.07\n", + "LinearRegression 0.33 ... 0.09\n", + "SGDRegressor 0.32 ... 0.16\n", + "BayesianRidge 0.32 ... 0.21\n", + "HuberRegressor 0.31 ... 0.61\n", + "LassoLarsCV 0.31 ... 0.45\n", + "LassoLarsIC 0.31 ... 0.11\n", + "LinearSVR 0.30 ... 0.74\n", + "AdaBoostRegressor 0.23 ... 1.15\n", + "Lars 0.23 ... 0.12\n", + "OrthogonalMatchingPursuitCV 0.23 ... 0.15\n", + "OrthogonalMatchingPursuit 0.23 ... 0.05\n", + "LarsCV 0.15 ... 0.47\n", + "Lasso 0.00 ... 0.08\n", + "ElasticNet 0.00 ... 0.05\n", + "DummyRegressor 0.00 ... 0.03\n", + "LassoLars 0.00 ... 0.06\n", + "PassiveAggressiveRegressor -0.13 ... 0.10\n", + "KernelRidge -13.70 ... 1.46\n", + "RANSACRegressor -177394519750505689776128.00 ... 1.11\n", + "\n", + "[38 rows x 3 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "KP6qT55ebcfd", + "outputId": "b25cc626-135a-4c86-8a4b-90f44a63dabe" + }, + "source": [ + "# Performance table of the test set (20% subset)\n", + "predictions_test" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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R-SquaredRMSETime Taken
Model
HistGradientBoostingRegressor0.541.062.13
LGBMRegressor0.541.060.46
RandomForestRegressor0.521.084.56
BaggingRegressor0.481.120.54
NuSVR0.481.123.47
SVR0.481.124.29
KNeighborsRegressor0.461.140.97
MLPRegressor0.451.156.73
GradientBoostingRegressor0.421.181.70
XGBRegressor0.421.181.17
ExtraTreesRegressor0.341.265.89
Ridge0.321.280.04
RidgeCV0.321.280.13
ElasticNetCV0.321.283.75
LassoCV0.321.283.95
ExtraTreeRegressor0.311.290.12
SGDRegressor0.311.290.18
TransformedTargetRegressor0.311.290.07
LinearRegression0.311.290.08
BayesianRidge0.311.290.16
LassoLarsCV0.311.290.45
LassoLarsIC0.311.290.09
HuberRegressor0.311.290.61
LinearSVR0.291.310.73
DecisionTreeRegressor0.281.310.16
OrthogonalMatchingPursuit0.241.360.04
OrthogonalMatchingPursuitCV0.241.360.12
Lars0.211.380.10
AdaBoostRegressor0.211.380.77
LarsCV0.151.430.46
Lasso-0.001.550.05
ElasticNet-0.001.550.03
DummyRegressor-0.001.550.03
LassoLars-0.001.550.05
PassiveAggressiveRegressor-0.221.710.09
GaussianProcessRegressor-4.993.803.88
KernelRidge-13.825.981.28
RANSACRegressor-225543784142202044678144.00737597884681.011.13
\n", + "
" + ], + "text/plain": [ + " R-Squared ... Time Taken\n", + "Model ... \n", + "HistGradientBoostingRegressor 0.54 ... 2.13\n", + "LGBMRegressor 0.54 ... 0.46\n", + "RandomForestRegressor 0.52 ... 4.56\n", + "BaggingRegressor 0.48 ... 0.54\n", + "NuSVR 0.48 ... 3.47\n", + "SVR 0.48 ... 4.29\n", + "KNeighborsRegressor 0.46 ... 0.97\n", + "MLPRegressor 0.45 ... 6.73\n", + "GradientBoostingRegressor 0.42 ... 1.70\n", + "XGBRegressor 0.42 ... 1.17\n", + "ExtraTreesRegressor 0.34 ... 5.89\n", + "Ridge 0.32 ... 0.04\n", + "RidgeCV 0.32 ... 0.13\n", + "ElasticNetCV 0.32 ... 3.75\n", + "LassoCV 0.32 ... 3.95\n", + "ExtraTreeRegressor 0.31 ... 0.12\n", + "SGDRegressor 0.31 ... 0.18\n", + "TransformedTargetRegressor 0.31 ... 0.07\n", + "LinearRegression 0.31 ... 0.08\n", + "BayesianRidge 0.31 ... 0.16\n", + "LassoLarsCV 0.31 ... 0.45\n", + "LassoLarsIC 0.31 ... 0.09\n", + "HuberRegressor 0.31 ... 0.61\n", + "LinearSVR 0.29 ... 0.73\n", + "DecisionTreeRegressor 0.28 ... 0.16\n", + "OrthogonalMatchingPursuit 0.24 ... 0.04\n", + "OrthogonalMatchingPursuitCV 0.24 ... 0.12\n", + "Lars 0.21 ... 0.10\n", + "AdaBoostRegressor 0.21 ... 0.77\n", + "LarsCV 0.15 ... 0.46\n", + "Lasso -0.00 ... 0.05\n", + "ElasticNet -0.00 ... 0.03\n", + "DummyRegressor -0.00 ... 0.03\n", + "LassoLars -0.00 ... 0.05\n", + "PassiveAggressiveRegressor -0.22 ... 0.09\n", + "GaussianProcessRegressor -4.99 ... 3.88\n", + "KernelRidge -13.82 ... 1.28\n", + "RANSACRegressor -225543784142202044678144.00 ... 1.13\n", + "\n", + "[38 rows x 3 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CM-lcHz6byNz" + }, + "source": [ + "## **5. Data visualization of model performance**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 628 + }, + "id": "MLsi5uSvbpC7", + "outputId": "2cca0f31-9f81-4d37-8ccd-e88d35268272" + }, + "source": [ + "# Bar plot of R-squared values\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "#train[\"R-Squared\"] = [0 if i < 0 else i for i in train.iloc[:,0] ]\n", + "\n", + "plt.figure(figsize=(5, 10))\n", + "sns.set_theme(style=\"whitegrid\")\n", + "ax = sns.barplot(y=predictions_train.index, x=\"R-Squared\", data=predictions_train)\n", + "ax.set(xlim=(0, 1))" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[(0.0, 1.0)]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 628 + }, + "id": "OLkiiTTwb-Cb", + "outputId": "6efb0857-c5fe-4bbc-dead-1bfd98a6f5d3" + }, + "source": [ + "# Bar plot of RMSE values\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.figure(figsize=(5, 10))\n", + "sns.set_theme(style=\"whitegrid\")\n", + "ax = sns.barplot(y=predictions_train.index, x=\"RMSE\", data=predictions_train)\n", + "ax.set(xlim=(0, 10))" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[(0.0, 10.0)]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 628 + }, + "id": "pCdcxpzScqXX", + "outputId": "62dbbf7b-d569-4280-ce60-0c191bbd8e2a" + }, + "source": [ + "# Bar plot of calculation time\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.figure(figsize=(5, 10))\n", + "sns.set_theme(style=\"whitegrid\")\n", + "ax = sns.barplot(y=predictions_train.index, x=\"Time Taken\", data=predictions_train)\n", + "ax.set(xlim=(0, 10))" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[(0.0, 10.0)]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pip install yt-dlp" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VegGCtSaspMK", + "outputId": "06c0b539-dc12-4eda-bb42-8231ebb8ec75" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting yt-dlp\n", + " Downloading yt_dlp-2024.11.4-py3-none-any.whl.metadata (172 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/172.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.1/172.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading yt_dlp-2024.11.4-py3-none-any.whl (3.2 MB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m2.6/3.2 MB\u001b[0m \u001b[31m69.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m65.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: yt-dlp\n", + "Successfully installed yt-dlp-2024.11.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import yt_dlp\n", + "\n", + "def download_audio(url):\n", + " ydl_opts = {\n", + " 'format': 'bestaudio/best',\n", + " 'postprocessors': [{\n", + " 'key': 'FFmpegExtractAudio',\n", + " 'preferredcodec': 'mp3',\n", + " 'preferredquality': '192',\n", + " }],\n", + " 'outtmpl': '%(title)s.%(ext)s',\n", + " 'verbose': True, # Add this line\n", + " }\n", + "\n", + " with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n", + " ydl.download([url])" + ], + "metadata": { + "id": "NYJ03kwms7TY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "URL = \"https://www.youtube.com/watch?v=UF8uR6Z6KLc\"\n", + "download_audio(URL)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wMM6oBSks8FZ", + "outputId": "d17918c9-7196-4aa4-83f8-1780a0739df7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[debug] Encodings: locale UTF-8, fs utf-8, pref UTF-8, out UTF-8 (No ANSI), error UTF-8 (No ANSI), screen UTF-8 (No ANSI)\n", + "[debug] yt-dlp version stable@2024.11.04 from yt-dlp/yt-dlp [197d0b03b] (pip) API\n", + "[debug] params: {'format': 'bestaudio/best', 'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192'}], 'outtmpl': '%(title)s.%(ext)s', 'verbose': True, 'compat_opts': set(), 'http_headers': {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'en-us,en;q=0.5', 'Sec-Fetch-Mode': 'navigate'}}\n", + "[debug] Python 3.10.12 (CPython x86_64 64bit) - Linux-6.1.85+-x86_64-with-glibc2.35 (OpenSSL 3.0.2 15 Mar 2022, glibc 2.35)\n", + "[debug] exe versions: ffmpeg 4.4.2 (setts), ffprobe 4.4.2\n", + "[debug] Optional libraries: certifi-2024.08.30, requests-2.32.3, secretstorage-3.3.1, sqlite3-3.37.2, urllib3-2.2.3\n", + "[debug] Proxy map: {'colab_language_server': '/usr/colab/bin/language_service'}\n", + "[debug] Request Handlers: urllib, requests\n", + "[debug] Loaded 1838 extractors\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[youtube] Extracting URL: https://www.youtube.com/watch?v=UF8uR6Z6KLc\n", + "[youtube] UF8uR6Z6KLc: Downloading webpage\n", + "[youtube] UF8uR6Z6KLc: Downloading ios player API JSON\n", + "[youtube] UF8uR6Z6KLc: Downloading mweb player API JSON\n", + "[youtube] UF8uR6Z6KLc: Downloading player 4e23410d\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[debug] Saving youtube-nsig.4e23410d to cache\n", + "[debug] [youtube] Decrypted nsig TODInL2VkpwBeZYW7 => wBKo3X3nM8BmSA\n", + "[debug] Loading youtube-nsig.4e23410d from cache\n", + "[debug] [youtube] Decrypted nsig vAeQv-NfW6cUtSuNm => 9nN6xXoPuOaU-A\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[youtube] UF8uR6Z6KLc: Downloading m3u8 information\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[debug] Sort order given by extractor: quality, res, fps, hdr:12, source, vcodec, channels, acodec, lang, proto\n", + "[debug] Formats sorted by: hasvid, ie_pref, quality, res, fps, hdr:12(7), source, vcodec, channels, acodec, lang, proto, size, br, asr, vext, aext, hasaud, id\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[info] UF8uR6Z6KLc: Downloading 1 format(s): 251\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[debug] Invoking http downloader on \"https://rr1---sn-qxo7rn7r.googlevideo.com/videoplayback?expire=1730771008&ei=4CMpZ4XhJpPBsfIP7deLmAo&ip=35.199.188.39&id=o-APTN0lKEb0Qfe0Jq0HNmcfAnPmHCwQ_vTDUQ-grBegFk&itag=251&source=youtube&requiressl=yes&xpc=EgVo2aDSNQ%3D%3D&met=1730749408%2C&mh=-6&mm=31%2C26&mn=sn-qxo7rn7r%2Csn-a5mekndl&ms=au%2Conr&mv=m&mvi=1&pl=20&rms=au%2Cau&vprv=1&svpuc=1&mime=audio%2Fwebm&rqh=1&gir=yes&clen=11436231&dur=904.241&lmt=1727658751589050&mt=1730749051&fvip=3&keepalive=yes&fexp=51312688%2C51326932&c=IOS&txp=4532434&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cxpc%2Cvprv%2Csvpuc%2Cmime%2Crqh%2Cgir%2Cclen%2Cdur%2Clmt&sig=AJfQdSswRAIgdyRaqQwBtuq6sm2qQrZRZtYbW8LoccP0YWy2R9hHK70CIE3HXpRHAOJFUukFaZHGV2uQ-Sk6VmPSgZ74DwmAQICb&lsparams=met%2Cmh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Crms&lsig=ACJ0pHgwRQIhAI2cNPYa2PUX70RTEbQQjQVfq6JLIv8EL0Te68Yfpqn-AiAOWu5_w37ZFpinBi-6Xt70X5Mdhlv6iviwRj7LeGV7Cg%3D%3D\"\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[download] Destination: Steve Jobs' 2005 Stanford Commencement Address.webm\n", + "[download] 100% of 10.91MiB in 00:00:00 at 22.43MiB/s \n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[debug] ffmpeg command line: ffprobe -show_streams 'file:Steve Jobs'\"'\"' 2005 Stanford Commencement Address.webm'\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[ExtractAudio] Destination: Steve Jobs' 2005 Stanford Commencement Address.mp3\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[debug] ffmpeg command line: ffmpeg -y -loglevel repeat+info -i 'file:Steve Jobs'\"'\"' 2005 Stanford Commencement Address.webm' -vn -acodec libmp3lame -b:a 192.0k -movflags +faststart 'file:Steve Jobs'\"'\"' 2005 Stanford Commencement Address.mp3'\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Deleting original file Steve Jobs' 2005 Stanford Commencement Address.webm (pass -k to keep)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## scikit-learn" + ], + "metadata": { + "id": "gHB4HINduElj" + } + }, + { + "cell_type": "code", + "source": [ + "pip install scikit-learn -U" + ], + "metadata": { + "id": "7A1yCVNlutQ_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.datasets import make_hastie_10_2\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "X, y = make_hastie_10_2(random_state=0)\n", + "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n", + " max_depth=1, random_state=0).fit(X, y)\n", + "clf.feature_importances_" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KVIBZ62euF87", + "outputId": "1bc8f2b9-3eae-48c3-b44a-2b5d89d25e7e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.10684213, 0.10461707, 0.11265447, 0.09863589, 0.09469133,\n", + " 0.10729306, 0.09163753, 0.09718194, 0.09581415, 0.09063242])" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## altair" + ], + "metadata": { + "id": "YFoTWdpdu_fV" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Heatmap\n", + "\n", + "Here's how to create a Heatmap." + ], + "metadata": { + "id": "cAno9XZkvOor" + } + }, + { + "cell_type": "code", + "source": [ + "import altair as alt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Compute x^2 + y^2 across a 2D grid\n", + "x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n", + "z = x ** 2 + y ** 2\n", + "\n", + "# Convert this grid to columnar data expected by Altair\n", + "source = pd.DataFrame({'x': x.ravel(),\n", + " 'y': y.ravel(),\n", + " 'z': z.ravel()})\n", + "\n", + "alt.Chart(source).mark_rect().encode(\n", + " x='x:O',\n", + " y='y:O',\n", + " color='z:Q'\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 262 + }, + "id": "7NSe5rKiuG82", + "outputId": "c56c460c-5cde-4100-f91e-e1e44ece081f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Stacked Area Chart" + ], + "metadata": { + "id": "1sGwP2h9vaKw" + } + }, + { + "cell_type": "code", + "source": [ + "import altair as alt\n", + "from vega_datasets import data\n", + "\n", + "source = data.iowa_electricity()\n", + "\n", + "alt.Chart(source).mark_area().encode(\n", + " x=\"year:T\",\n", + " y=\"net_generation:Q\",\n", + " color=\"source:N\"\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "vDQc_4kLvH8l", + "outputId": "0c72c68a-d72e-4564-afe6-253793faf7a8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n", + " col = df[col_name].apply(to_list_if_array, convert_dtype=False)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "xZ2fN6SLvMYe" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From d494f8093073990fcf77061bd740a0e4c2d40020 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Tue, 12 Nov 2024 20:19:44 -0800 Subject: [PATCH 69/73] Delete python/google_colab_first_notebook.ipynb --- python/google_colab_first_notebook.ipynb | 983 ----------------------- 1 file changed, 983 deletions(-) delete mode 100644 python/google_colab_first_notebook.ipynb diff --git a/python/google_colab_first_notebook.ipynb b/python/google_colab_first_notebook.ipynb deleted file mode 100644 index 13386f1..0000000 --- a/python/google_colab_first_notebook.ipynb +++ /dev/null @@ -1,983 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# My First Notebook\n" - ], - "metadata": { - "id": "yWsgD4dxrhSX" - } - }, - { - "cell_type": "markdown", - "source": [ - "# H1 heading" - ], - "metadata": { - "id": "zRVndNmir2M4" - } - }, - { - "cell_type": "markdown", - "source": [ - "## H2 heading" - ], - "metadata": { - "id": "ByuA1cDmrqhb" - } - }, - { - "cell_type": "markdown", - "source": [ - "### H3 heading" - ], - "metadata": { - "id": "HM0mR4v4sAXP" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Load API key" - ], - "metadata": { - "id": "qC1RLhLKwswk" - } - }, - { - "cell_type": "code", - "source": [ - "from google.colab import userdata\n", - "dummy_api_key = userdata.get('DUMMY_KEY')" - ], - "metadata": { - "id": "8idhWq95wgKl" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Examples" - ], - "metadata": { - "id": "ECEbeZCFsIh5" - } - }, - { - "cell_type": "markdown", - "source": [ - "## pandas" - ], - "metadata": { - "id": "RmJPQguLsKip" - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd" - ], - "metadata": { - "id": "dw1W26YmroA8" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df = pd.read_csv('sample_data/california_housing_test.csv')\n", - "df.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "wGnYqBtFsPnf", - "outputId": "d4142036-ab13-4905-856a-aa8f4ee3d17b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", - "0 -122.05 37.37 27.0 3885.0 661.0 \n", - "1 -118.30 34.26 43.0 1510.0 310.0 \n", - "2 -117.81 33.78 27.0 3589.0 507.0 \n", - "3 -118.36 33.82 28.0 67.0 15.0 \n", - "4 -119.67 36.33 19.0 1241.0 244.0 \n", - "\n", - " population households median_income median_house_value \n", - "0 1537.0 606.0 6.6085 344700.0 \n", - "1 809.0 277.0 3.5990 176500.0 \n", - "2 1484.0 495.0 5.7934 270500.0 \n", - "3 49.0 11.0 6.1359 330000.0 \n", - "4 850.0 237.0 2.9375 81700.0 " - ], - "text/html": [ - "\n", - "
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\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df", - "summary": "{\n \"name\": \"df\",\n \"rows\": 3000,\n \"fields\": [\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.9949362939550161,\n \"min\": -124.18,\n \"max\": -114.49,\n \"num_unique_values\": 607,\n \"samples\": [\n -121.15,\n -121.46,\n -121.02\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.1296695233438325,\n \"min\": 32.56,\n \"max\": 41.92,\n \"num_unique_values\": 587,\n \"samples\": [\n 40.17,\n 33.69,\n 39.61\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"housing_median_age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.555395554955755,\n \"min\": 1.0,\n \"max\": 52.0,\n \"num_unique_values\": 52,\n \"samples\": [\n 14.0,\n 49.0,\n 7.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_rooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2155.59333162558,\n \"min\": 6.0,\n \"max\": 30450.0,\n \"num_unique_values\": 2215,\n \"samples\": [\n 1961.0,\n 1807.0,\n 680.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_bedrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 415.6543681363232,\n \"min\": 2.0,\n \"max\": 5419.0,\n \"num_unique_values\": 1055,\n \"samples\": [\n 532.0,\n 764.0,\n 2162.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"population\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1030.5430124122422,\n \"min\": 5.0,\n \"max\": 11935.0,\n \"num_unique_values\": 1802,\n \"samples\": [\n 947.0,\n 1140.0,\n 2019.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"households\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 365.42270980552604,\n \"min\": 2.0,\n \"max\": 4930.0,\n \"num_unique_values\": 1026,\n \"samples\": [\n 646.0,\n 629.0,\n 504.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"median_income\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.854511729691481,\n \"min\": 0.4999,\n \"max\": 15.0001,\n \"num_unique_values\": 2578,\n \"samples\": [\n 1.725,\n 0.7403,\n 2.6964\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"median_house_value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 113119.68746964433,\n \"min\": 22500.0,\n \"max\": 500001.0,\n \"num_unique_values\": 1784,\n \"samples\": [\n 71900.0,\n 63000.0,\n 115800.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 5 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8YBEQ5ZPtyRu", - "outputId": "b23eb485-b7f6-477f-b515-d46fedc41dca" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(3000, 9)" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## yt-dlp" - ], - "metadata": { - "id": "dthivj-_sn_w" - } - }, - { - "cell_type": "code", - "source": [ - "! apt-get install ffmpeg" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BKdcB7l-vz_h", - "outputId": "16893d68-2951-49d0-96d2-c3653d6a44fb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Reading package lists... Done\n", - "Building dependency tree... Done\n", - "Reading state information... Done\n", - "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", - "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "pip install yt-dlp" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VegGCtSaspMK", - "outputId": "06c0b539-dc12-4eda-bb42-8231ebb8ec75" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting yt-dlp\n", - " Downloading yt_dlp-2024.11.4-py3-none-any.whl.metadata (172 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/172.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.1/172.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading yt_dlp-2024.11.4-py3-none-any.whl (3.2 MB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m2.6/3.2 MB\u001b[0m \u001b[31m69.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m65.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: yt-dlp\n", - "Successfully installed yt-dlp-2024.11.4\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import yt_dlp\n", - "\n", - "def download_audio(url):\n", - " ydl_opts = {\n", - " 'format': 'bestaudio/best',\n", - " 'postprocessors': [{\n", - " 'key': 'FFmpegExtractAudio',\n", - " 'preferredcodec': 'mp3',\n", - " 'preferredquality': '192',\n", - " }],\n", - " 'outtmpl': '%(title)s.%(ext)s',\n", - " 'verbose': True, # Add this line\n", - " }\n", - "\n", - " with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n", - " ydl.download([url])" - ], - "metadata": { - "id": "NYJ03kwms7TY" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "URL = \"https://www.youtube.com/watch?v=UF8uR6Z6KLc\"\n", - "download_audio(URL)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wMM6oBSks8FZ", - "outputId": "d17918c9-7196-4aa4-83f8-1780a0739df7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[debug] Encodings: locale UTF-8, fs utf-8, pref UTF-8, out UTF-8 (No ANSI), error UTF-8 (No ANSI), screen UTF-8 (No ANSI)\n", - "[debug] yt-dlp version stable@2024.11.04 from yt-dlp/yt-dlp [197d0b03b] (pip) API\n", - "[debug] params: {'format': 'bestaudio/best', 'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192'}], 'outtmpl': '%(title)s.%(ext)s', 'verbose': True, 'compat_opts': set(), 'http_headers': {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'en-us,en;q=0.5', 'Sec-Fetch-Mode': 'navigate'}}\n", - "[debug] Python 3.10.12 (CPython x86_64 64bit) - Linux-6.1.85+-x86_64-with-glibc2.35 (OpenSSL 3.0.2 15 Mar 2022, glibc 2.35)\n", - "[debug] exe versions: ffmpeg 4.4.2 (setts), ffprobe 4.4.2\n", - "[debug] Optional libraries: certifi-2024.08.30, requests-2.32.3, secretstorage-3.3.1, sqlite3-3.37.2, urllib3-2.2.3\n", - "[debug] Proxy map: {'colab_language_server': '/usr/colab/bin/language_service'}\n", - "[debug] Request Handlers: urllib, requests\n", - "[debug] Loaded 1838 extractors\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[youtube] Extracting URL: https://www.youtube.com/watch?v=UF8uR6Z6KLc\n", - "[youtube] UF8uR6Z6KLc: Downloading webpage\n", - "[youtube] UF8uR6Z6KLc: Downloading ios player API JSON\n", - "[youtube] UF8uR6Z6KLc: Downloading mweb player API JSON\n", - "[youtube] UF8uR6Z6KLc: Downloading player 4e23410d\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[debug] Saving youtube-nsig.4e23410d to cache\n", - "[debug] [youtube] Decrypted nsig TODInL2VkpwBeZYW7 => wBKo3X3nM8BmSA\n", - "[debug] Loading youtube-nsig.4e23410d from cache\n", - "[debug] [youtube] Decrypted nsig vAeQv-NfW6cUtSuNm => 9nN6xXoPuOaU-A\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[youtube] UF8uR6Z6KLc: Downloading m3u8 information\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[debug] Sort order given by extractor: quality, res, fps, hdr:12, source, vcodec, channels, acodec, lang, proto\n", - "[debug] Formats sorted by: hasvid, ie_pref, quality, res, fps, hdr:12(7), source, vcodec, channels, acodec, lang, proto, size, br, asr, vext, aext, hasaud, id\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[info] UF8uR6Z6KLc: Downloading 1 format(s): 251\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[debug] Invoking http downloader on \"https://rr1---sn-qxo7rn7r.googlevideo.com/videoplayback?expire=1730771008&ei=4CMpZ4XhJpPBsfIP7deLmAo&ip=35.199.188.39&id=o-APTN0lKEb0Qfe0Jq0HNmcfAnPmHCwQ_vTDUQ-grBegFk&itag=251&source=youtube&requiressl=yes&xpc=EgVo2aDSNQ%3D%3D&met=1730749408%2C&mh=-6&mm=31%2C26&mn=sn-qxo7rn7r%2Csn-a5mekndl&ms=au%2Conr&mv=m&mvi=1&pl=20&rms=au%2Cau&vprv=1&svpuc=1&mime=audio%2Fwebm&rqh=1&gir=yes&clen=11436231&dur=904.241&lmt=1727658751589050&mt=1730749051&fvip=3&keepalive=yes&fexp=51312688%2C51326932&c=IOS&txp=4532434&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cxpc%2Cvprv%2Csvpuc%2Cmime%2Crqh%2Cgir%2Cclen%2Cdur%2Clmt&sig=AJfQdSswRAIgdyRaqQwBtuq6sm2qQrZRZtYbW8LoccP0YWy2R9hHK70CIE3HXpRHAOJFUukFaZHGV2uQ-Sk6VmPSgZ74DwmAQICb&lsparams=met%2Cmh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Crms&lsig=ACJ0pHgwRQIhAI2cNPYa2PUX70RTEbQQjQVfq6JLIv8EL0Te68Yfpqn-AiAOWu5_w37ZFpinBi-6Xt70X5Mdhlv6iviwRj7LeGV7Cg%3D%3D\"\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[download] Destination: Steve Jobs' 2005 Stanford Commencement Address.webm\n", - "[download] 100% of 10.91MiB in 00:00:00 at 22.43MiB/s \n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[debug] ffmpeg command line: ffprobe -show_streams 'file:Steve Jobs'\"'\"' 2005 Stanford Commencement Address.webm'\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[ExtractAudio] Destination: Steve Jobs' 2005 Stanford Commencement Address.mp3\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[debug] ffmpeg command line: ffmpeg -y -loglevel repeat+info -i 'file:Steve Jobs'\"'\"' 2005 Stanford Commencement Address.webm' -vn -acodec libmp3lame -b:a 192.0k -movflags +faststart 'file:Steve Jobs'\"'\"' 2005 Stanford Commencement Address.mp3'\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Deleting original file Steve Jobs' 2005 Stanford Commencement Address.webm (pass -k to keep)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## scikit-learn" - ], - "metadata": { - "id": "gHB4HINduElj" - } - }, - { - "cell_type": "code", - "source": [ - "pip install scikit-learn -U" - ], - "metadata": { - "id": "7A1yCVNlutQ_" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.datasets import make_hastie_10_2\n", - "from sklearn.ensemble import GradientBoostingClassifier\n", - "\n", - "X, y = make_hastie_10_2(random_state=0)\n", - "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n", - " max_depth=1, random_state=0).fit(X, y)\n", - "clf.feature_importances_" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KVIBZ62euF87", - "outputId": "1bc8f2b9-3eae-48c3-b44a-2b5d89d25e7e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([0.10684213, 0.10461707, 0.11265447, 0.09863589, 0.09469133,\n", - " 0.10729306, 0.09163753, 0.09718194, 0.09581415, 0.09063242])" - ] - }, - "metadata": {}, - "execution_count": 11 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## altair" - ], - "metadata": { - "id": "YFoTWdpdu_fV" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Heatmap\n", - "\n", - "Here's how to create a Heatmap." - ], - "metadata": { - "id": "cAno9XZkvOor" - } - }, - { - "cell_type": "code", - "source": [ - "import altair as alt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Compute x^2 + y^2 across a 2D grid\n", - "x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n", - "z = x ** 2 + y ** 2\n", - "\n", - "# Convert this grid to columnar data expected by Altair\n", - "source = pd.DataFrame({'x': x.ravel(),\n", - " 'y': y.ravel(),\n", - " 'z': z.ravel()})\n", - "\n", - "alt.Chart(source).mark_rect().encode(\n", - " x='x:O',\n", - " y='y:O',\n", - " color='z:Q'\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 262 - }, - "id": "7NSe5rKiuG82", - "outputId": "c56c460c-5cde-4100-f91e-e1e44ece081f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Stacked Area Chart" - ], - "metadata": { - "id": "1sGwP2h9vaKw" - } - }, - { - "cell_type": "code", - "source": [ - "import altair as alt\n", - "from vega_datasets import data\n", - "\n", - "source = data.iowa_electricity()\n", - "\n", - "alt.Chart(source).mark_area().encode(\n", - " x=\"year:T\",\n", - " y=\"net_generation:Q\",\n", - " color=\"source:N\"\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "vDQc_4kLvH8l", - "outputId": "0c72c68a-d72e-4564-afe6-253793faf7a8" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``.\n", - " col = df[col_name].apply(to_list_if_array, convert_dtype=False)\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "xZ2fN6SLvMYe" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From b99bda9287a8f3c69d00f6fdd9f8f2bd2f68542c Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 10 Jan 2026 00:23:20 -0800 Subject: [PATCH 70/73] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index dd32105..520d3f4 100644 --- a/README.md +++ b/README.md @@ -9,3 +9,5 @@ Folder | Description [shiny](https://github.com/dataprofessor/code/tree/master/shiny) | Codes for building *web applications* in R with *shiny* package. > Note: More to come. Please stay tuned! + + From 1abd57fef5503f6d2693913e23cb268a5ad97e5a Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 10 Jan 2026 00:24:48 -0800 Subject: [PATCH 71/73] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 520d3f4..be0cfe5 100644 --- a/README.md +++ b/README.md @@ -10,4 +10,4 @@ Folder | Description > Note: More to come. Please stay tuned! - +** Subscribe to my Newsletter:** https://dataprofessor.beehiiv.com/ From 820a84022d66523efce6260ab2fd2e1b68f0a599 Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 10 Jan 2026 00:25:10 -0800 Subject: [PATCH 72/73] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index be0cfe5..2139429 100644 --- a/README.md +++ b/README.md @@ -10,4 +10,4 @@ Folder | Description > Note: More to come. Please stay tuned! -** Subscribe to my Newsletter:** https://dataprofessor.beehiiv.com/ +**Subscribe to my Newsletter:** https://dataprofessor.beehiiv.com/ From d05a1b139d03880269634926a2ca7d45538b1cbb Mon Sep 17 00:00:00 2001 From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com> Date: Sat, 10 Jan 2026 00:25:58 -0800 Subject: [PATCH 73/73] Add sparkle emoji to newsletter subscription line --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2139429..957e9e5 100644 --- a/README.md +++ b/README.md @@ -10,4 +10,4 @@ Folder | Description > Note: More to come. Please stay tuned! -**Subscribe to my Newsletter:** https://dataprofessor.beehiiv.com/ +✨ **Subscribe to my Newsletter:** https://dataprofessor.beehiiv.com/