Deepchem

A batteries-included molecular ML library that shines when you need fast property prediction experiments without building everything from scratch. Ships with 30+ MoleculeNet benchmark datasets (Tox21, BBBP, Delaney) and extensive featurizers covering fingerprints, graph representations, and descriptors. The GNN implementations (GCN, GAT, AttentiveFP) work well for larger datasets, while traditional ML wrappers handle smaller ones. Real strength is the ecosystem: loaders for SMILES, SDF, and FASTA files, scaffold-based splitting to prevent data leakage, and pretrained models for transfer learning. Note the Python 3.11 cap and backend installation quirks (install PyTorch or TensorFlow first). If you want a more PyTorch-native graph workflow, consider torchdrug instead.