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

A batteries-included toolkit for the GPU-accelerated OpenMM molecular simulation engine.

A library for building, manipulating, analyzing and automatic design of molecules, including a genetic algorithm.

ChemFormula provides a class for working with chemical formulas. It allows parsing chemical formulas, calculating formula weights, and generating formatted output strings (e.g. in HTML, LaTeX, or Unicode).

A library for computational chemistry (DFT) for input file generation, data extraction, method screening and analysis.

Experiments with expanded ensembles to explore chemical space.

A Package For Training SNAP Interatomic Potentials for use in the LAMMPS molecular dynamics package.

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

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)

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 library for estimating thermochemical properties of molecules and adsorbates using group additivity.

General purpose tools for high-throughput catalysis.

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

A module for solving and visualizing the Schrödinger equation.

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

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

Usage-Instructions) - A program to visualize reaction networks.

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

A benchmarking platform for molecular generation models.

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

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

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.

An open, extensible Python framework for GPU-accelerated alchemical free energy calculations.

A Jupyter book demonstrating working with biochemical data using the scikit-bio library for tasks such as sequence alignment and calculating Hamming distances.

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.

Open Drug Discovery Toolkit, a modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc.

Hierarchical Generation of Molecular Graphs using Structural Motifs.

Jupyter Widgets to interact with molecular datasets.

Crystal property prediction

A calculator incorporating various empirical pair and many-body potentials.

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

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