Awesome-omni-skill rdkit

Modern RDKit workflows for cheminformatics, including molecular fingerprints, drawing, and property calculations. Use when working with molecules, SMILES, molecular fingerprints (Morgan, ECFP, RDKit, atom pairs, topological torsions), molecule visualization/drawing, substructure search, or chemical property calculations. This skill provides up-to-date syntax patterns as RDKit's API evolves.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/backend/rdkit" ~/.claude/skills/diegosouzapw-awesome-omni-skill-rdkit && rm -rf "$T"

manifest: skills/backend/rdkit/SKILL.md

safety · automated scan (low risk)

This is a pattern-based risk scan, not a security review. Our crawler flagged:

pip install

Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.

source content

RDKit Skill

This skill provides up-to-date RDKit patterns. LLMs often have outdated RDKit syntax — always follow the patterns here.

Installation

uv pip install rdkit

Core Imports

from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors, Draw, rdFingerprintGenerator
from rdkit.Chem import rdDepictor
from rdkit import DataStructs

Molecule I/O

# From SMILES
mol = Chem.MolFromSmiles('CCO')

# From file
mol = Chem.MolFromMolFile('molecule.mol')
mols = [m for m in Chem.SDMolSupplier('molecules.sdf') if m is not None]

# To SMILES
smiles = Chem.MolToSmiles(mol)

Fingerprints — USE THE NEW API

CRITICAL: Use

rdFingerprintGenerator

module, NOT the old functions like

GetMorganFingerprint()

See

references/fingerprints.md

for complete documentation.

Quick Reference

from rdkit.Chem import rdFingerprintGenerator

# Morgan/ECFP fingerprints (ECFP4 = radius 2)
mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)

# RDKit fingerprint
rdkgen = rdFingerprintGenerator.GetRDKitFPGenerator(fpSize=2048)

# Atom pairs
apgen = rdFingerprintGenerator.GetAtomPairGenerator(fpSize=2048)

# Topological torsions
ttgen = rdFingerprintGenerator.GetTopologicalTorsionGenerator(fpSize=2048)

# Generate fingerprints (same API for all types)
fp = mfpgen.GetFingerprint(mol)           # bit vector
cfp = mfpgen.GetCountFingerprint(mol)     # count vector
np_fp = mfpgen.GetFingerprintAsNumPy(mol) # numpy array

Drawing Molecules

Use

MolDraw2DCairo

or
MolDraw2DSVG
with

drawOptions()

for customization.

See

references/drawing.md

for complete documentation.

Quick Reference

from rdkit.Chem import Draw, rdDepictor

# Generate 2D coordinates
rdDepictor.Compute2DCoords(mol)
rdDepictor.StraightenDepiction(mol)

# Simple drawing
img = Draw.MolToImage(mol, size=(300, 300))
img.save('molecule.png')

# Customized drawing
d2d = Draw.MolDraw2DCairo(350, 300)
dopts = d2d.drawOptions()
dopts.addAtomIndices = True  # show atom indices
d2d.DrawMolecule(mol)
d2d.FinishDrawing()
with open('molecule.png', 'wb') as f:
    f.write(d2d.GetDrawingText())

# Grid of molecules
img = Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(200, 200))

Molecular Properties

from rdkit.Chem import Descriptors

mw = Descriptors.MolWt(mol)
logp = Descriptors.MolLogP(mol)
hbd = Descriptors.NumHDonors(mol)
hba = Descriptors.NumHAcceptors(mol)
tpsa = Descriptors.TPSA(mol)
rotatable = Descriptors.NumRotatableBonds(mol)

Substructure Search

# SMARTS pattern matching
pattern = Chem.MolFromSmarts('[OH]')
matches = mol.GetSubstructMatches(pattern)  # returns tuple of tuples

# Check if has substructure
has_oh = mol.HasSubstructMatch(pattern)

Similarity

Always specify which fingerprint when reporting similarity — different fingerprints give very different values for the same molecule pair (e.g., RDKit FP: 0.79 vs Morgan2: 0.43).

from rdkit import DataStructs

# Tanimoto similarity between two fingerprints
sim = DataStructs.TanimotoSimilarity(fp1, fp2)

# Bulk similarity (one vs many)
sims = DataStructs.BulkTanimotoSimilarity(fp1, [fp2, fp3, fp4])

Molecule Properties

# Get/set/check properties on molecules, atoms, bonds
mol.SetProp('name', 'aspirin')
mol.GetProp('name')
mol.HasProp('name')
mol.ClearProp('name')

# Typed getters/setters
mol.SetDoubleProp('score', 3.14)
mol.GetDoubleProp('score')
mol.SetIntProp('count', 42)

# Get all properties
mol.GetPropsAsDict()  # {key: value, ...}

# Private props (start with _) hidden by default
mol.GetPropsAsDict(includePrivate=True)

Pickling Gotcha

Properties are lost by default when pickling/serializing:

from rdkit import Chem

# Enable property preservation
Chem.SetDefaultPickleProperties(Chem.PropertyPickleOptions.AllProps)

# Or per-molecule
binary = mol.ToBinary(Chem.PropertyPickleOptions.AllProps)

Parsing & Sanitization

Control chemistry perception during parsing. See

references/parsing.md

for details.

# Custom parsing without sanitization
params = Chem.SmilesParserParams()
params.sanitize = False
params.removeHs = False
mol = Chem.MolFromSmiles(smiles, params=params)

# Manual sanitization after preprocessing
Chem.SanitizeMol(mol)
Chem.AssignStereochemistry(mol, cleanIt=True, force=True)