chemistry-rdkit
Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=aiming-lab-autoresearchclaw-claude-skills-chemistry-rdkit-skill-md&format=skill
下载 .skill
标准格式，含 system_prompt 与 model_config，导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name chemistry-rdkit description Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks. metadata {"category":"domain","trigger-keywords":"molecule,SMILES,chemical,drug,rdkit,fingerprint,molecular,compound,reaction,cheminformatics","applicable-stages":"9,10,12","priority":"4","version":"1.0","author":"researchclaw","references":"adapted from K-Dense-AI/claude-scientific-skills"} RDKit Cheminformatics Best Practice Molecular I/O Create molecules from SMILES: mol = Chem.MolFromSmiles('CCO') Always check for None: MolFromSmiles returns None on invalid input Convert to canonical SMILES: Chem.MolToSmiles(mol) Read SDF files: suppl = Chem.SDMolSupplier('file.sdf') Read SMILES files: suppl = Chem.SmilesMolSupplier('file.smi') Write molecules: writer = Chem.SDWriter('output.sdf') Molecular Descriptors Molecular weight: Descriptors.MolWt(mol) LogP (lipophilicity): Descriptors.MolLogP(mol) TPSA (polar surface area): Descriptors.TPSA(mol) H-bond donors/acceptors: Descriptors.NumHDonors(mol) , Descriptors.NumHAcceptors(mol) Rotatable bonds: Descriptors.NumRotatableBonds(mol) Lipinski Rule of 5: MW <= 500, LogP <= 5, HBD <= 5, HBA <= 10 Fingerprints and Similarity Morgan (circular) fingerprints: AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048) RDKit fingerprints: Chem.RDKFingerprint(mol) MACCS keys: MACCSkeys.GenMACCSKeys(mol) Tanimoto similarity: DataStructs.TanimotoSimilarity(fp1, fp2) Use radius=2 (ECFP4 equivalent) as default for most applications For virtual screening, Tanimoto > 0.7 suggests structural similarity Substructure Search SMARTS patterns: pattern = Chem.MolFromSmarts('[OH]') Check match: mol.HasSubstructMatch(pattern) Get all matches: mol.GetSubstructMatches(pattern) Common SMARTS: [#6](=O)[OH] (carboxylic acid), [NH2] (primary amine) Filter compound libraries by functional group presence Property Calculation Patterns Batch processing: iterate over SDMolSupplier, skip None entries Use Chem.Descriptors.descList for all available descriptors For ADMET filtering, calculate Lipinski, Veber, and PAINS filters Generate 3D coordinates: AllChem.EmbedMolecule(mol, AllChem.ETKDG()) Minimize energy: AllChem.MMFFOptimizeMolecule(mol) Common Pitfalls Always sanitize molecules (default behavior) — disable only when needed Add hydrogens explicitly for 3D work: Chem.AddHs(mol) Handle stereochemistry: use Chem.AssignStereochemistry(mol) Large SDF files: use ForwardSDMolSupplier for memory efficiency Kekulization errors usually indicate invalid SMILES input
Agent 识别该技能的关键词，点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识（skill/v1） |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类（数组） |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接（本页地址） |
| exported_at | 导出时间（每次下载生成） |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数：provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明（Coze / Dify / Claude / 自定义框架） |
