---
name: rdkit-chemistry-fe2731553904
description: "Molecular chemistry operations via RDKit. Use when: user asks about molecular structures, SMILES, chemical properties, or fingerprints. NOT for: reaction databases or wet lab protocols."
metadata: { "openclaw": { "emoji": "\u2697\uFE0F", "requires": { "bins": ["python3"] }, "install": [{ "id": "uv-rdkit", "kind": "uv", "package": "rdkit" }] } }
---

# RDKit Chemistry

Molecular chemistry operations using RDKit.

## When to Use

- Molecular structures, SMILES parsing, or validation
- Molecular properties (MW, logP, TPSA, HBD/HBA)
- Substructure searching or molecular filtering
- Fingerprints (Morgan, MACCS) and similarity calculations
- 2D depiction or molecular image generation

## When NOT to Use

- Reaction databases or retrosynthesis planning
- Wet lab protocols or experimental procedures
- Protein structure analysis (use biopython-bio)
- Quantum chemistry or DFT calculations

## SMILES Parsing and Properties

```python
from rdkit import Chem
from rdkit.Chem import Descriptors, rdMolDescriptors

mol = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')  # Aspirin
if mol is None:
    print("Invalid SMILES")

canonical = Chem.MolToSmiles(mol)                      # Canonical SMILES
mw = Descriptors.MolWt(mol)                            # Molecular weight
logp = Descriptors.MolLogP(mol)                        # Partition coefficient
tpsa = Descriptors.TPSA(mol)                           # Topological polar surface area
hbd = rdMolDescriptors.CalcNumHBD(mol)                 # H-bond donors
hba = rdMolDescriptors.CalcNumHBA(mol)                 # H-bond acceptors
rotatable = rdMolDescriptors.CalcNumRotatableBonds(mol)

# SMARTS substructure match
pattern = Chem.MolFromSmarts('[OH]')
has_oh = mol.HasSubstructMatch(pattern)
```

## Lipinski Rule of Five

```python
def lipinski(smi):
    mol = Chem.MolFromSmiles(smi)
    return {
        'MW <= 500': Descriptors.MolWt(mol) <= 500,
        'LogP <= 5': Descriptors.MolLogP(mol) <= 5,
        'HBD <= 5': rdMolDescriptors.CalcNumHBD(mol) <= 5,
        'HBA <= 10': rdMolDescriptors.CalcNumHBA(mol) <= 10,
    }
```

## Substructure Search

```python
molecules = [Chem.MolFromSmiles(s) for s in ['CCO', 'CC(=O)O', 'c1ccccc1', 'c1ccccc1O']]
pattern = Chem.MolFromSmarts('c1ccccc1')  # Benzene ring
hits = [m for m in molecules if m.HasSubstructMatch(pattern)]
```

## Fingerprints and Similarity

```python
from rdkit.Chem import AllChem, MACCSkeys
from rdkit import DataStructs

mol1 = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O')
mol2 = Chem.MolFromSmiles('CC(=O)Nc1ccc(O)cc1')

# Morgan (circular) fingerprints — radius 2 ~ ECFP4
fp1 = AllChem.GetMorganFingerprintAsBitVect(mol1, radius=2, nBits=2048)
fp2 = AllChem.GetMorganFingerprintAsBitVect(mol2, radius=2, nBits=2048)

# MACCS keys
mfp1 = MACCSkeys.GenMACCSKeys(mol1)

# Tanimoto / Dice similarity
tanimoto = DataStructs.TanimotoSimilarity(fp1, fp2)
dice = DataStructs.DiceSimilarity(fp1, fp2)
```

## 2D Depiction

```python
from rdkit.Chem import Draw

Draw.MolToFile(mol, 'molecule.png', size=(300, 300))

# Grid of molecules
mols = [Chem.MolFromSmiles(s) for s in ['CCO', 'CC(=O)O', 'c1ccccc1O']]
img = Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(300, 300))
img.save('grid.png')
```

## Quick One-liner

```bash
python3 -c "
from rdkit import Chem; from rdkit.Chem import Descriptors
mol = Chem.MolFromSmiles('CCO')
print(f'MW: {Descriptors.MolWt(mol):.2f}, LogP: {Descriptors.MolLogP(mol):.2f}')
"
```

## Best Practices

1. Always check `MolFromSmiles()` return for `None` (invalid SMILES).
2. Use canonical SMILES for consistent comparisons.
3. Morgan radius=2 (ECFP4) is standard for similarity screening.
4. Sanitize molecules before property calculations.
5. Use `Chem.AddHs(mol)` before 3D coordinate generation.


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/rdkit-chemistry/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

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: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/rdkit-chemistry/SKILL.md
Attribution basis: Source repository record

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: d7a0abaedfe0837696a334791077661e8de937c488a4ff1fba349a3e113e333c