deepchemlisted

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.
tassiovale/claude-code-kit · ★ 10 · AI & Automation · score 75

Install: claude install-skill tassiovale/claude-code-kit

# 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. **Version note:** Examples target **deepchem 2.8.0** (PyPI stable, Apr 2024). Requires **Python 3.7–3.11** (`<3.12` on PyPI). Core utilities (loaders, featurizers, MoleculeNet) work without a DL backend; GNN and transformer models need the matching extra (`torch`, `tensorflow`, or `jax`). Install the backend framework first when using GPU builds. ## 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 ## Core Capabilities ### 1. Molecular Data Loading and Processing DeepChem provides speci