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SchNetPack is a Python toolkit for building and training atomistic neural networks, with SchNet and PaiNN representations, quantum-chemical property outputs, and molecular dynamics components.

Molecular Property Prediction · Quantum Chemistry

AiZynthFinder is a Python retrosynthetic planning toolkit that uses neural-network-guided search to propose routes from target molecules to precursors in a configured stock.

Retrosynthesis

DeepChem is an open-source Python toolchain for scientific deep learning in drug discovery, materials science, quantum chemistry and biology, with backend-specific dependencies and guided tutorials.

Drug Discovery

Chemprop is a PyTorch-based toolkit for training and evaluating message-passing neural networks for molecular property prediction, with CLI workflows, Python modules and task-specific notebooks.

Molecular Property Prediction

RDKit is an open-source cheminformatics toolkit for 2D and 3D molecular operations, descriptor and fingerprint generation, and chemical searching through a PostgreSQL cartridge.

Cheminformatics