Alterlab Rdkit
技能 已验证 活跃Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms. Part of the AlterLab Academic Skills suite.
To equip researchers and developers with a powerful and flexible toolkit for detailed molecular analysis and manipulation, enabling advanced cheminformatics tasks in research and discovery.
功能
- Molecular I/O and creation from various formats (SMILES, SDF, MOL, etc.)
- Comprehensive molecular descriptors and property calculations (MW, LogP, TPSA)
- Generation and comparison of diverse molecular fingerprints (Morgan, MACCS, etc.)
- Precise substructure searching and SMARTS pattern matching
- 2D and 3D coordinate generation and optimization
- Chemical reaction definition and application
使用场景
- Analyzing drug-likeness and lead-likeness of molecules
- Screening large compound libraries for similarity to a query
- Identifying molecules containing specific functional groups or structural motifs
- Generating and visualizing 3D molecular conformers for docking studies
- Working with chemical reactions for synthesis planning
非目标
- Providing a simplified interface for common workflows (use datamol for that)
- Performing basic file format conversions without cheminformatics context
- Acting as a general-purpose chemistry simulation engine beyond RDKit's scope
安装
npx skills add AlterLab-IEU/AlterLab-Academic-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
RDKit Cheminformatics Toolkit
99Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
Datamol Cheminformatics Skill
99Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly. Part of the AlterLab Academic Skills suite.
Datamol
97Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Molfeat
99Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Deepchem
99Molecular 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.
PyTDC (Therapeutics Data Commons)
99Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.