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

Ensemble of automated machine learning protocols that can be run sequentially through a single command line. The program works for regression and classification problems.

ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data. (based on Tensorflow)

Library of descriptors to aid in the data-mining of materials properties, created by the Lawrence Berkeley National Laboratory.

High-performance ML research

Library for fast calculations of **mo**lecula**r** **fe**at**u**re**s** from 3D structures for machine learning with a focus on steric descriptors.

Directed message passing neural networks for property prediction of molecules and reactions with uncertainty and interpretation.

Deep learning library for Chemistry based on Tensorflow

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

Descriptor library containing a variety of fingerprinting techniques, including the Smooth Overlap of Atomic Positions (SOAP).

A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models in R.

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation.

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.

OpenChem is a deep learning toolkit for Computational Chemistry with PyTorch backend.

DGL-LifeSci is a [DGL](https://www.dgl.ai/)-based package for various applications in life science with graph neural network.

A Library for Deep Learning in Biology and Chemistry.

A deep learning framework (based on Chainer) with applications in Biology and Chemistry.

Enables machine learning on three-dimensional molecular structure.

a robust molecular representation learning framework against distribution shifts.

Molecular property prediction with unified API for diverse models and respresentations,