rdkit（RDKit 化学信息学）

RDKit: Python Cheminformatics Library

Summary

RDKit (v2023+) provides comprehensive Python APIs for molecular structure manipulation, property calculation, and chemical informatics. It requires Python 3 and NumPy, offering modular components for molecule parsing, descriptors, fingerprints, substructure search, conformer generation, and reaction processing.

Applicable Scenarios

This skill applies when you need to:

Module Organization

Setup

Install via pip or conda:

# Conda (recommended)
conda install -c conda-forge rdkit

# Pip
pip install rdkit-pypi

Standard imports:

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

Quick Reference

Parse and Validate Molecules

from rdkit import Chem

mol = Chem.MolFromSmiles('c1ccc(O)cc1')
if mol is None:
    print("Invalid SMILES")

Compute Properties

from rdkit.Chem import Descriptors

mw = Descriptors.MolWt(mol)
logp = Descriptors.MolLogP(mol)
tpsa = Descriptors.TPSA(mol)

Generate Fingerprints

from rdkit.Chem import rdFingerprintGenerator

gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
fp = gen.GetFingerprint(mol)

Similarity Search

from rdkit import DataStructs

similarity = DataStructs.TanimotoSimilarity(fp1, fp2)

Substructure Match

pattern = Chem.MolFromSmarts('[OH1][C]')  # Alcohol
has_alcohol = mol.HasSubstructMatch(pattern)

Generate 3D Conformer

from rdkit.Chem import AllChem

mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, randomSeed=42)
AllChem.MMFFOptimizeMolecule(mol)

Implementation Patterns

Drug-Likeness Assessment

from rdkit import Chem
from rdkit.Chem import Descriptors

def assess_druglikeness(smiles: str) -> dict | None:
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return None

    mw = Descriptors.MolWt(mol)
    logp = Descriptors.MolLogP(mol)
    hbd = Descriptors.NumHDonors(mol)
    hba = Descriptors.NumHAcceptors(mol)

    return {
        'MW': mw,
        'LogP': logp,
        'HBD': hbd,
        'HBA': hba,
        'TPSA': Descriptors.TPSA(mol),
        'RotBonds': Descriptors.NumRotatableBonds(mol),
        'Lipinski': mw <= 500 and logp <= 5 and hbd <= 5 and hba <= 10,
        'QED': Descriptors.qed(mol)
    }

Batch Similarity Search

from rdkit import Chem, DataStructs
from rdkit.Chem import rdFingerprintGenerator

def find_similar(query_smiles: str, database: list[str], threshold: float = 0.7) -> list:
    query = Chem.MolFromSmiles(query_smiles)
    if query is None:
        return []

    gen = rdFingerprintGenerator.GetMorganGenerator(radius=2)
    query_fp = gen.GetFingerprint(query)

    hits = []
    for idx, smi in enumerate(database):
        mol = Chem.MolFromSmiles(smi)
        if mol:
            fp = gen.GetFingerprint(mol)
            sim = DataStructs.TanimotoSimilarity(query_fp, fp)
            if sim >= threshold:
                hits.append((idx, smi, sim))

    return sorted(hits, key=lambda x: x[2], reverse=True)

Functional Group Screening

from rdkit import Chem

FUNCTIONAL_GROUPS = {
    'alcohol': '[OH1][C]',
    'amine': '[NH2,NH1][C]',
    'carboxylic_acid': 'C(=O)[OH1]',
    'amide': 'C(=O)N',
    'ester': 'C(=O)O[C]',
    'nitro': '[N+](=O)[O-]'
}

def detect_functional_groups(smiles: str) -> list[str]:
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return []

    found = []
    for name, smarts in FUNCTIONAL_GROUPS.items():
        pattern = Chem.MolFromSmarts(smarts)
        if mol.HasSubstructMatch(pattern):
            found.append(name)
    return found

Conformer Generation with Clustering

from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.ML.Cluster import Butina

def generate_diverse_conformers(smiles: str, n_confs: int = 50, rmsd_thresh: float = 0.5) -> list:
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return []

    mol = Chem.AddHs(mol)
    conf_ids = AllChem.EmbedMultipleConfs(mol, numConfs=n_confs, randomSeed=42)

    # Optimize all conformers
    for cid in conf_ids:
        AllChem.MMFFOptimizeMolecule(mol, confId=cid)

    # Cluster by RMSD to get diverse set
    if len(conf_ids) < 2:
        return list(conf_ids)

    dists = []
    for i in range(len(conf_ids)):
        for j in range(i):
            rmsd = AllChem.GetConformerRMS(mol, conf_ids[j], conf_ids[i])
            dists.append(rmsd)

    clusters = Butina.ClusterData(dists, len(conf_ids), rmsd_thresh, isDistData=True)
    return [conf_ids[c[0]] for c in clusters]  # Cluster centroids

Batch Processing SDF Files

from rdkit import Chem
from rdkit.Chem import Descriptors

def process_sdf(input_path: str, output_path: str, min_mw: float = 200, max_mw: float = 500):
    """Filter compounds by molecular weight and add property columns."""
    supplier = Chem.SDMolSupplier(input_path)
    writer = Chem.SDWriter(output_path)

    for mol in supplier:
        if mol is None:
            continue

        mw = Descriptors.MolWt(mol)
        if not (min_mw <= mw <= max_mw):
            continue

        # Add computed properties
        mol.SetProp('MW', f'{mw:.2f}')
        mol.SetProp('LogP', f'{Descriptors.MolLogP(mol):.2f}')
        mol.SetProp('TPSA', f'{Descriptors.TPSA(mol):.2f}')

        writer.write(mol)

    writer.close()

Guidelines

Always validate parsed molecules:

mol = Chem.MolFromSmiles(smiles)
if mol is None:
    print(f"Parse failed: {smiles}")
    continue

Use bulk operations for performance:

fps = [gen.GetFingerprint(m) for m in mols]
sims = DataStructs.BulkTanimotoSimilarity(fps[0], fps[1:])

Add hydrogens for 3D work:

mol = Chem.AddHs(mol)  # Required before EmbedMolecule
AllChem.EmbedMolecule(mol)

Stream large files:

# Memory-efficient: process one at a time
for mol in Chem.ForwardSDMolSupplier(file_handle):
    if mol:
        process(mol)

# Avoid: loading entire file
all_mols = list(Chem.SDMolSupplier('huge.sdf'))

Thread safety: Most operations are thread-safe except for concurrent access to MolSupplier objects.

Troubleshooting

Reference Documentation

Each reference file contains detailed API documentation:

External Resources

• RDKit Documentation: https://www.rdkit.org/docs/
• Getting Started Guide: https://www.rdkit.org/docs/GettingStartedInPython.html
• GitHub Repository: https://github.com/rdkit/rdkit
• Cookbook: https://www.rdkit.org/docs/Cookbook.html