— name: deepchem description: Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc. license: MIT tier: premium super_skill: true merged_from: [220, 885] domain: Research & Science subcategory: Bioinformatics & life science tags: [molecular-ml, deepchem, drug-discovery, admet, toxicity, gnn, moleculenet, cheminformatics, python] tools: [claude-code, cursor, gemini-cli] r …