Popular GitHub Repositories for Drug Discovery

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Message Passing Neural Networks for Molecule Property Prediction

A powerful and flexible machine learning platform for drug discovery

Python package for graph neural networks in chemistry and biology

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

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

Deep Reinforcement Learning for de-novo Drug Design

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)

TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

Protein-compound affinity prediction through unified RNN-CNN

Optimising chemical reactions using machine learning

DrugAssist: A Large Language Model for Molecule Optimization

Code for the paper: ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.

DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins

[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

This repo contains the codes for our paper "End-to-End Full-Atom Antibody Design"

Mixed continous/categorical flow-matching model for de novo molecule generation.