Deepchem
DeepChem brings serious molecular ML infrastructure to Claude. You get property prediction for ADMET and toxicity, graph neural networks like GCN and MPNN, and direct access to MoleculeNet benchmarks with proper scaffold splitting to prevent data leakage. The featurization layer alone saves weeks of work, converting SMILES strings and SDFs into fingerprints or graph representations depending on your model choice. Pretrained models like ChemBERTa and GROVER are available for transfer learning when your dataset is small. If you're doing drug discovery ML or materials informatics and tired of stitching together RDKit, PyTorch Geometric, and custom loaders, this consolidates the stack. The API is opinionated about best practices, which is exactly what you want in chemistry ML where it's easy to leak signal.
- skills.sh installs
- 278
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill deepchem --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
Core Capabilities
1. Molecular Data Loading and Processing
Sponsors
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- Repository
- github.com/davila7/claude-code-templates
- Categories
- Data Science & ML, Sales & Marketing
- First seen
- Jun 3, 2026
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