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A benchmarking platform for molecular generation models.

A package for benchmarking of models for _de novo_ molecular design.

A platform for graph-based molecular generation using graph neural 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 library for processing, analyzing and modeling spectroscopic data.

A toolkit for visualizations in materials informatics.

Predicts the pKa values of ionizable groups in proteins and protein-ligand complexes based in the 3D structure.

This package provides a periodic table of the elements with support for mass, density and xray/neutron scattering information.

Calculate mass, elemental composition, and mass distribution spectrum of a molecule given by its chemical formula, relative element weights, or sequence.

A package for working with nuclear magnetic resonance (NMR) data including functions for reading common binary file formats and processing NMR data.

Hierarchical Generation of Molecular Graphs using Structural Motifs.

A Python program to compute quasi-harmonic thermochemical data from Gaussian frequency calculations.

Ensemble of automated QM workflows that can be run through jupyter notebooks, command lines and yaml files.

End-to-end molecular dynamics engine built on PyTorch, enabling differentiable simulations with neural network potentials and GPU acceleration for machine learning-accelerated molecular dynamics (MIT License, 707+ stars)

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)

AstraZeneca's industrial-grade retrosynthetic planning tool using MCTS to recursively decompose molecules into purchasable precursors, with multi-step route scoring and support for custom one-step models (v4.0, 2024)

Crystal property prediction

PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)

NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)

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.

Descriptor computation(chemistry) and (optional) storage for machine learning.

Simple RDKit molecule editor GUI using PySide.

- Molecular Manipulation Made Easy. A light wrapper build on top of RDKit.

Universal molecular toolkit that can be used for molecular fingerprinting, substructure search, and molecular visualization written in C++ package, with Java, C#, and Python wrappers.