11 open-source projects similar to usnistgov/jarvis, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
molfeat - the hub for all your molecular featurizers Docs | Homepage
DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti
license-image:https://img.shields.io/badge/license-GPL3.0-green.svg license-url:https://github.com/divelab/AIRS/blob/main/LICENSE contributing-image:https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
The QUIP package is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to other software such as LAMMPS, CP2K and also…
maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML for materials science as easy as possible.
AtomAI is a Pytorch-based package for deep and machine learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by…
The official sources for the RDKit library
MAST-ML is an open-source Python package designed to broaden and accelerate the use of machine learning in materials science research