Popular GitHub Repositories for Ligand Receptor

PocketGen (Nature Machine Intelligence 24): Generating Full-Atom Ligand-Binding Protein Pockets

Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery

DrugHIVE: Structure-based drug design with a deep hierarchical generative model

An accurate and trainable end-to-end protein-ligand docking framework

Code for our Nature Scientific Reports paper "A universal framework for accurate and efficient geometric deep learning of molecular systems"

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data

Implementation of ICLR23 paper "Molecule Generation for Target Protein Binding with Structural Motifs"

Large language modeling applied to T-cell receptor (TCR) sequences.

Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning

Pytorch implementation of Pocket2Drug: a generative deep learning model to predict binding drugs for ligand-binding sites.

SAnDReS (Statistical Analysis of Docking Results and Scoring functions) is a free and open-source computational environment for the development of machine-learning models for the prediction of ligand-binding affinity. We developed SAnDReS using Python programming language, and SciPy, NumPy, scikit-learn, and Matplotlib libraries as a computational

《ICLR 2025论文：CheapNet：基于层次化表征的跨注意力高效蛋白质-配体结合亲和力预测》

Benchmarking modern neural networks for scRNA sequencing analysis