A Euclidean diffusion model for structure-based drug design.

MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure

Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

ATOM3D: tasks on molecules in three dimensions

learning coarse-grained force fields

SchNetPack - Deep Neural Networks for Atomistic Systems

OpenMM is a toolkit for molecular simulation using high performance GPU code.

A resource for learning about Machine learning & Deep Learning

SMILES Pair Encoding: A data-driven substructure representation of chemicals

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (NIPS 2017)

Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

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

Benchmark datasets, data loaders, and evaluators for graph machine learning

Open Babel is a chemical toolbox designed to speak the many languages of chemical data.

The official sources for the RDKit library

The fundamental package for scientific computing with Python.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

HTMD: Programming Environment for Molecular Discovery