Deepchem
Skill Verifiziert AktivMolecular 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.
Accelerate molecular research by providing a robust and easy-to-use skill for property prediction, model training, and data featurization in chemistry and biology.
Funktionen
- Molecular property prediction (ADMET, toxicity)
- Diverse molecular featurizers (graph, fingerprint, descriptor)
- Loading and processing of various molecular data formats
- Training and evaluation of ML/GNN models
- Access to MoleculeNet benchmark datasets
Anwendungsfälle
- Predicting molecular properties for drug discovery
- Quick experiments with pre-trained models
- Training custom ML models on chemical data
- Benchmarking models on standard datasets
Nicht-Ziele
- Serving as a replacement for graph-first PyTorch workflows (use torchdrug)
- Providing access to benchmark datasets outside DeepChem's scope (use pytdc)
- Executing complex molecular generation tasks (refer to dedicated generative models)
Execution
- info:Pinned dependenciesThe README recommends using `uv` for installation, which generally leads to pinned dependencies, but explicit lockfiles or version pinning in the script's frontmatter are not present.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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