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Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive…
DeepChem Overview DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models. When to Use This Skill This skill should be used when: Loading and processing molecular data (SMILES strings, SDF files, protein sequences) Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties) Training models on chemical/biological datasets Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.) Converting molecules to ML-ready features (fingerprints, graph representations, descriptors) Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP) Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer) Predicting crystal/materials properties (bandgap, formation energy) Analyzing protein or DNA sequences
don't have the plugin yet? install it then click "run inline in claude" again.