All Algorithms implemented in Python

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Open source code for AlphaFold 2.

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

Python package for graph neural networks in chemistry and biology

AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

Conversion tool for molecular simulations

MSA(Multiple Sequence Alignment) visualization python package for sequence analysis

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

DeepMol: A Machine and Deep Learning Framework for Computational Chemistry

This is a code for compound-protein interaction (CPI) prediction based on a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.

Implementation of PocketXMol, the pocket-interacting molecular generative foundation model.

Protein-compound affinity prediction through unified RNN-CNN

A snakemake-based workflow for FEP and MM(PB/GB)SA calculations with GROMACS

Predicting Drug Protein Interaction using Quasi-Visual Question Answering System

A multiple-layer inter-molecular contact features based deep neural network for protein-ligand binding affinity prediction

Protein contacts prediction using deep learning models

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

Official implementation for LigUnity: Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.

DLSCORE: A deep learning based scoring function for predicting protein-ligand binding affinity

FragPELE, a new tool for in silico hit-to-lead drug design, capable of growing a fragment into a core while exploring the protein-ligand conformational space

A specialized LLM for study search, study screening, and data extraction from medical literature.

Learning protein representation for rigid-body docking

The code corresponding to Transfer Learning for a Foundational Chemistry Model