Deepchem
技能 已验证 活跃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.
Accelerate molecular research by providing a robust and easy-to-use skill for property prediction, model training, and data featurization in chemistry and biology.
功能
- 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
使用场景
- Predicting molecular properties for drug discovery
- Quick experiments with pre-trained models
- Training custom ML models on chemical data
- Benchmarking models on standard datasets
非目标
- 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.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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