Predict the binding affinity of protein-protein complexes from structural data

Affinity Protein-Protein Transformers—State of the art protein-protein binding affinity in seconds!

The official implementation of DiffAbXL benchmarked in the paper "Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs", formerly titled "Benchmarking Generative Models for Antibody Design".

NeuralMD for Protein-ligand Binding Simulation, Nature Communicaitons 2025 https://www.nature.com/articles/s41467-025-67808-z

Official Implementation of CompassDock

Predict binding affinity of ligand-protein complexes using Graph Neural Networks. The model is implemented using PyTorch Geometric and based on the method in "Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks"

Generate customized voxel representations of protein-ligand complexes using GPU.

Predicting the effect of mutations on protein stability and protein-protein interaction affinity.

CNN-based protein-ligand scoring function.

TAG-DTA: Binding Region-Guided Strategy to Predict Drug-Target Affinity Using Transformers

Molecule oracle for predicting clinical viability from binding affinity and historical clinical trial data.

MolPro is a comprehensive python package for small molecule generation using protein active site or/and similar molecules using 3D information of molecules with in-silico validation of molecules by docking , pharmacophore hypothesis. Also off target prediction based on the binding site similarities.

🤗 Hugging Face space for gnina-torch 🔥

PyMOL wizard integrating brianhie/efficient-evolution and haddocking/prodigy for guided antibody evolution.

mulecule protein binding affinity prediction

A weighted average ensemble method implementation to predict drug–target interactions

Protein-Ligand binding affinity prediction with Protein LLMs and Graph Attention Networks.

Uncertainty in Binding Affinity: A Deep Learning Benchmark