ML+AI frameworks for chemistry

• AIRS: AIRS is a collection of open-source software tools, datasets, and benchmarks associated with our paper entitled “Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems”
  

  

• AMPL: A Data-Driven Modeling Pipeline for Drug Discovery (ATOM Modeling PipeLine) (standalone)
  

  

• ChemML: ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
  

  

• DGL-LifeSci: DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
  

  

• DeepChem:
  

• DeepMol:
  

  

• FlexMol: FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
  

  

• Minervachem: Linear Graphlet Models for Accurate and Interpretable Cheminformatics
  

  

• MolFlux:
  

• MolPipeline: MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn
  

  

• MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery (needs registration) (online, 2024).

  

• OpenChem: OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
  

  

• Scikit-mol: Scikit-Learn classes for molecular vectorization using RDKit (standalone)
  

• Summit: Summit: Benchmarking Machine Learning Methods for Reaction Optimisation
  

  

• TorchDrug: A powerful and flexible machine learning platform for drug discovery
  

  

• cgcnn: Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
  

  

• chainer-chemistry: BayesGrad: Explaining Predictions of Graph Convolutional Networks
  

  

• chemml:
  

• chemprop: Analyzing Learned Molecular Representations for Property Prediction
  

  

• olorenchemengine:
  

• pytorch-geometric: