---
name: chemistry-rdkit-7cae29200713
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
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


---
Source attribution: aiming-lab
Original project: https://github.com/aiming-lab/AutoResearchClaw
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: aiming-lab
Original project: https://github.com/aiming-lab/AutoResearchClaw
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Education & Research
Roles: Data Scientist, ML / AI Engineer, Researcher
Specialties: API
Tools: Rdkit
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 119e3fe023578933c46695a36a086e83fb6faf5de4c1584be53a46c4d34cf0f4