- DeepChem: Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.
- SchNetPack: Deep Neural Networks for Atomistic Systems.
- TorchANI: Accurate Neural Network Potential on PyTorch.
- AmpTorch: Atomistic Machine-learning Package.
- KLIFF: KIM-based Learning-Integrated Fitting Framework.
- NequIP: An open-source code for building E(3)-equivariant interatomic potentials.
- OpenChem: A deep learning toolkit for Computational Chemistry with PyTorch backend.
- PyXtal FF: A Python package for Machine learning of interatomic force field.
- MLatom: A Package for Atomistic Simulations with Machine Learning.
- REANN: A PyTorch-based end-to-end multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems.
- ML4Chem: Machine Learning for Chemistry and Materials Science.
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects. Paper here.
- megnet: Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- TensorMol: Tensorflow + Molecules = TensorMol.
- n2p2: A Neural Network Potential Package.
- TurboGAP
- PYSEQM: PyTorch-based Semi-Empirical Quantum Mechanics
- DGL: Python package built to ease deep learning on graph, on top of existing DL frameworks.
- TorchDrug: A powerful and flexible machine learning platform for drug discovery.
- awesome-python-chemistry: A curated list of Python packages related to chemistry.
- awesome-cheminformatics: Another list focuses on Cheminformatics, including tools not only in Python.
- awesome-small-molecule-ml: A collection of papers, datasets, and packages for small-molecule drug discovery.
- awesome-pytorch-list: A comprehensive list of pytorch related content on github,such as different models, implementations, helper libraries, tutorials etc.