chemistry-rdkit
Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search.
Skill by researchclaw
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/chemistry-rdkit
What's inside
- Skill 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.
- License
- MIT
- Metadata
- author: researchclaw · version: 1.0 · category: domain · priority: 4 · references: adapted from K-Dense-AI/claude-scientific-skills · trigger-keywords: molecule,SMILES,chemical,drug,rdkit,fingerprint,molecular,compound,reaction,cheminformatics · applicable-stages: 9,10,12
- Instructions
- 48 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
## RDKit Cheminformatics Best Practice
### Molecular I/O
1. Create molecules from SMILES: `mol = Chem.MolFromSmiles('CCO')`
2. Always check for None: `MolFromSmiles` returns None on invalid input
3. Convert to canonical SMILES: `Chem.MolToSmiles(mol)`
4. Read SDF files: `suppl = Chem.SDMolSupplier('file.sdf')`
5. Read SMILES files: `suppl = Chem.SmilesMolSupplier('file.smi')`
6. Write molecules: `writer = Chem.SDWriter('output.sdf')`
### Molecular Descriptors
1. Molecular weight: `Descriptors.MolWt(mol)`
2. LogP (lipophilicity): `Descriptors.MolLogP(mol)`
3. TPSA (polar surface area): `Descriptors.TPSA(mol)`
4. H-bond donors/acceptors: `Descriptors.NumHDonors(mol)`, `Descriptors.NumHAcceptors(mol)`
5. Rotatable bonds: `Descriptors.NumRotatableBonds(mol)`
6. Lipinski Rule of 5: MW <= 500, LogP <= 5, HBD <= 5, HBA <= 10
### Fingerprints and Similarity
1. Morgan (circular) fingerprints: `AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)`
2. RDKit fingerprints: `Chem.RDKFingerprint(mol)`
3. MACCS keys: `MACCSkeys.GenMACCSKeys(mol)`
4. Tanimoto similarity: `DataStructs.TanimotoSimilarity(fp1, fp2)`
5. Use radius=2 (ECFP4 equivalent) as default for most applications
6. For virtual screening, Tanimoto > 0.7 suggests structural similarity
### Substructure Search
1. SMARTS patterns: `pattern = Chem.MolFromSmarts('[OH]')`
2. Check match: `mol.HasSubstructMatch(pattern)`
3. Get all matches: `mol.GetSubstructMatches(pattern)`
4. Common SMARTS: `[#6](=O)[OH]` (carboxylic acid), `[NH2]` (primary amine)
5. Filter compound libraries by functional group presence
### Property Calculation Patterns
1. Batch processing: iterate over SDMolSupplier, skip None entries
2. Use `Chem.Descriptors.descList` for all available descriptors
3. For ADMET filtering, calculate Lipinski, Veber, and PAINS filters
4. Generate 3D coordinates: `AllChem.EmbedMolecule(mol, AllChem.ETKDG())`
5. Minimize energy: `AllChem.MMFFOptimizeMolecule(mol)`
### Common Pitfalls
1. Always sanitize molecules (default behavior) — disable only when needed
2. Add hydrogens explicitly for 3D work: `Chem.AddHs(mol)`
3. Handle stereochemistry: use `Chem.AssignStereochemistry(mol)`
4. Large SDF files: use `ForwardSDMolSupplier` for memory efficiency
5. Kekulization errors usually indicate invalid SMILES inputFrom raja21068/AutoScholar, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Apr 12, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.0
- License
- MIT
- Repository
- github.com/raja21068/AutoScholar
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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chemistry-rdkit skill. Agent Mart. https://agentmart.dev/skills/raja21068-chemistry-rdkit (last updated 2026-10-24)