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

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

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

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

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.

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

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.

a robust molecular representation learning framework against distribution shifts.

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

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

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

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

Enables machine learning on three-dimensional molecular structure.

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

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

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