RDKit

AlphaFold3google-deepmind/alphafold3

AlphaFold 3 is an AI model that predicts the 3D structures of proteins and their interactions with other biomolecules like DNA, RNA, and ligands.

DeepChemdeepchem/deepchem

An open-source Python library for applying deep learning to drug discovery, materials science, quantum chemistry, and biology.

DiffDockgcorso/DiffDock

A state-of-the-art diffusion model for predicting how small molecules (ligands) bind to proteins.

DeepPurposekexinhuang12345/DeepPurpose

A deep learning library for drug-target interaction, drug property, protein-protein interaction, drug-drug interaction, and protein function prediction in bioinformatics.

TeachOpenCADDvolkamerlab/TeachOpenCADD

A teaching platform providing interactive Jupyter Notebooks for learning computer-aided drug design (CADD) using open-source tools.

MOSESmolecularsets/moses

A benchmarking platform for molecular generation models, providing datasets, implementations, and evaluation metrics for drug discovery research.

DGL-LifeSciawslabs/dgl-lifesci

A Python package for applying graph neural networks to molecular graphs and biological networks in life science research.

OpenChemMariewelt/OpenChem

A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.

chainer-chemistrypfnet-research/chainer-chemistry

A deep learning library built on Chainer for molecular property prediction using graph convolutional neural networks.

JTVAEwengong-jin/icml18-jtnn

A junction tree variational autoencoder for generating valid molecular graphs with desired chemical properties.

datamoldatamol-org/datamol

A Python library for molecular processing built on RDKit with a simple API and good defaults.

Datamoldatamol-io/datamol

A Python library for molecular processing built on RDKit with a simple API and good defaults.

GuacaMolBenevolentAI/guacamol

A Python package for benchmarking generative models in de novo molecular design.

Molecular Transformerpschwllr/MolecularTransformer

A sequence-to-sequence transformer model for predicting chemical reaction pathways (retrosynthesis) with uncertainty calibration.

TargetDiffguanjq/targetdiff

Official implementation of a 3D equivariant diffusion model for generating drug-like molecules that bind to specific protein targets and predicting their binding affinity.

GraphDTAthinng/GraphDTA

GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.

mol2vecsamoturk/mol2vec

An unsupervised machine learning approach to learn vector representations of molecular substructures for cheminformatics.

DescriptaStorusbp-kelley/descriptastorus

A Python library for fast random access to chemical descriptors and molecule indices, optimized for machine learning workflows.

ChEMBL_Structure_Pipeline (formerly standardiser)chembl/ChEMBL_Structure_Pipeline

Standardizes and processes chemical molecule structures for the ChEMBL database using RDKit.

DOCKSTRINGdockstring/dockstring

A Python package for easy molecular docking with a curated dataset and benchmark tasks for drug discovery.

RDchiralconnorcoley/rdchiral

A Python wrapper for RDKit's RunReactants that improves stereochemistry handling in chemical reaction applications.

rdeditorEBjerrum/rdeditor

A simple, open-source graphical molecule editor built with RDKit and PySide6 for chemical structure drawing and editing.

ChemMLhachmannlab/chemml

A Python machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.

rd_filtersPatWalters/rd_filters

A Python script to filter chemical compounds using structural alerts from ChEMBL and property filters from RDKit.

ChatDrugchao1224/ChatDrug

A conversational AI framework for editing small molecules, peptides, and proteins using retrieval-augmented generation and domain feedback.

TransformerCPIlifanchen-simm/transformerCPI

A deep learning model using transformer architecture to predict compound-protein interactions from molecular and protein sequences.

DrugBANpeizhenbai/DrugBAN

A deep bilinear attention network framework with adversarial domain adaptation for interpretable drug-target interaction prediction.