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chemistry-rdkit

Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search.

Skill by researchclaw

IndexedMIT
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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

Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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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 input

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Last commit (this folder)
Apr 12, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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chemistry-rdkit skill. Agent Mart. https://agentmart.dev/skills/raja21068-chemistry-rdkit (last updated 2026-10-24)