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Provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks.

Scientific Computing for Chemists with Python is a Jupyter book teaching basic python in chemistry skills, including relevant libraries, and applies them to solving chemical problems.

Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

Neural Network Force Field based on PyTorch.

A [Jupyter](https://jupyter.org/) widget to interactively view molecular structures and trajectories.

Automate downloading and querying the latest (or a given) version of ChEMBL.

Therapeutics Data Commons: 66 AI-ready datasets across 22 drug discovery tasks with 29 leaderboards, covering target identification, molecular generation, ADMET prediction, and clinical trial outcomes (Harvard MIMS, NeurIPS 2021/2024)

A python package for optimizing chemical reactions using machine learning (contains 10 algorithms + several benchmarks).

Library with several compositional and structural material descriptors, along with a few pre-trained neural network models of material properties.

A Deep Learning Library for Compound and Protein Modeling DTI, Drug Property, PPI, DDI, Protein Function Prediction.

Psi4-based reference implementations and Jupyter notebook-based tutorials for foundational quantum chemistry methods.

A Chemical Knowledge Graph and Toolkit, writting in IUPAC/SMILES/SMARTS, for common small molecules from diverse communities to aid users in selecting compounds for forcefield parametirization.