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Zhongwei Li
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skills/rdkit/scripts/similarity_search.py
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295
skills/rdkit/scripts/similarity_search.py
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#!/usr/bin/env python3
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"""
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Molecular Similarity Search
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Perform fingerprint-based similarity screening against a database of molecules.
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Supports multiple fingerprint types and similarity metrics.
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Usage:
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python similarity_search.py "CCO" database.smi --threshold 0.7
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python similarity_search.py query.smi database.sdf --method morgan --output hits.csv
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"""
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import argparse
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import sys
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from pathlib import Path
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try:
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from rdkit import Chem
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from rdkit.Chem import AllChem, MACCSkeys
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from rdkit import DataStructs
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except ImportError:
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print("Error: RDKit not installed. Install with: conda install -c conda-forge rdkit")
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sys.exit(1)
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FINGERPRINT_METHODS = {
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'morgan': 'Morgan fingerprint (ECFP-like)',
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'rdkit': 'RDKit topological fingerprint',
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'maccs': 'MACCS structural keys',
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'atompair': 'Atom pair fingerprint',
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'torsion': 'Topological torsion fingerprint'
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}
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def generate_fingerprint(mol, method='morgan', radius=2, n_bits=2048):
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"""Generate molecular fingerprint based on specified method."""
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if mol is None:
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return None
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method = method.lower()
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if method == 'morgan':
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return AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=n_bits)
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elif method == 'rdkit':
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return Chem.RDKFingerprint(mol, maxPath=7, fpSize=n_bits)
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elif method == 'maccs':
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return MACCSkeys.GenMACCSKeys(mol)
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elif method == 'atompair':
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from rdkit.Chem.AtomPairs import Pairs
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return Pairs.GetAtomPairFingerprintAsBitVect(mol, nBits=n_bits)
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elif method == 'torsion':
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from rdkit.Chem.AtomPairs import Torsions
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return Torsions.GetHashedTopologicalTorsionFingerprintAsBitVect(mol, nBits=n_bits)
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else:
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raise ValueError(f"Unknown fingerprint method: {method}")
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def load_molecules(file_path):
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"""Load molecules from file."""
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path = Path(file_path)
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if not path.exists():
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print(f"Error: File not found: {file_path}")
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return []
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molecules = []
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if path.suffix.lower() in ['.sdf', '.mol']:
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suppl = Chem.SDMolSupplier(str(path))
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elif path.suffix.lower() in ['.smi', '.smiles', '.txt']:
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suppl = Chem.SmilesMolSupplier(str(path), titleLine=False)
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else:
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print(f"Error: Unsupported file format: {path.suffix}")
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return []
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for idx, mol in enumerate(suppl):
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if mol is None:
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print(f"Warning: Failed to parse molecule {idx+1}")
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continue
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# Try to get molecule name
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name = mol.GetProp('_Name') if mol.HasProp('_Name') else f"Mol_{idx+1}"
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smiles = Chem.MolToSmiles(mol)
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molecules.append({
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'index': idx + 1,
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'name': name,
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'smiles': smiles,
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'mol': mol
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})
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return molecules
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def similarity_search(query_mol, database, method='morgan', threshold=0.7,
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radius=2, n_bits=2048, metric='tanimoto'):
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"""
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Perform similarity search.
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Args:
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query_mol: Query molecule (RDKit Mol object)
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database: List of database molecules
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method: Fingerprint method
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threshold: Similarity threshold (0-1)
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radius: Morgan fingerprint radius
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n_bits: Fingerprint size
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metric: Similarity metric (tanimoto, dice, cosine)
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Returns:
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List of hits with similarity scores
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"""
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if query_mol is None:
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print("Error: Invalid query molecule")
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return []
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# Generate query fingerprint
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query_fp = generate_fingerprint(query_mol, method, radius, n_bits)
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if query_fp is None:
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print("Error: Failed to generate query fingerprint")
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return []
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# Choose similarity function
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if metric.lower() == 'tanimoto':
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sim_func = DataStructs.TanimotoSimilarity
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elif metric.lower() == 'dice':
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sim_func = DataStructs.DiceSimilarity
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elif metric.lower() == 'cosine':
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sim_func = DataStructs.CosineSimilarity
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else:
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raise ValueError(f"Unknown similarity metric: {metric}")
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# Search database
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hits = []
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for db_entry in database:
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db_fp = generate_fingerprint(db_entry['mol'], method, radius, n_bits)
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if db_fp is None:
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continue
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similarity = sim_func(query_fp, db_fp)
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if similarity >= threshold:
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hits.append({
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'index': db_entry['index'],
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'name': db_entry['name'],
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'smiles': db_entry['smiles'],
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'similarity': similarity
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})
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# Sort by similarity (descending)
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hits.sort(key=lambda x: x['similarity'], reverse=True)
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return hits
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def write_results(hits, output_file):
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"""Write results to CSV file."""
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import csv
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with open(output_file, 'w', newline='') as f:
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fieldnames = ['Rank', 'Index', 'Name', 'SMILES', 'Similarity']
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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for rank, hit in enumerate(hits, 1):
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writer.writerow({
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'Rank': rank,
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'Index': hit['index'],
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'Name': hit['name'],
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'SMILES': hit['smiles'],
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'Similarity': f"{hit['similarity']:.4f}"
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})
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def print_results(hits, max_display=20):
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"""Print results to console."""
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if not hits:
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print("\nNo hits found above threshold")
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return
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print(f"\nFound {len(hits)} similar molecules:")
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print("="*80)
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print(f"{'Rank':<6} {'Index':<8} {'Similarity':<12} {'Name':<20} {'SMILES'}")
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print("-"*80)
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for rank, hit in enumerate(hits[:max_display], 1):
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name = hit['name'][:18] + '..' if len(hit['name']) > 20 else hit['name']
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smiles = hit['smiles'][:40] + '...' if len(hit['smiles']) > 43 else hit['smiles']
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print(f"{rank:<6} {hit['index']:<8} {hit['similarity']:<12.4f} {name:<20} {smiles}")
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if len(hits) > max_display:
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print(f"\n... and {len(hits) - max_display} more")
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print("="*80)
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def main():
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parser = argparse.ArgumentParser(
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description='Molecular similarity search using fingerprints',
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog=f"""
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Available fingerprint methods:
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{chr(10).join(f' {k:12s} - {v}' for k, v in FINGERPRINT_METHODS.items())}
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Similarity metrics:
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tanimoto - Tanimoto coefficient (default)
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dice - Dice coefficient
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cosine - Cosine similarity
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Examples:
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# Search with SMILES query
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python similarity_search.py "CCO" database.smi --threshold 0.7
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# Use different fingerprint
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python similarity_search.py query.smi database.sdf --method maccs
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# Save results
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python similarity_search.py "c1ccccc1" database.smi --output hits.csv
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# Adjust Morgan radius
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python similarity_search.py "CCO" database.smi --method morgan --radius 3
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"""
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)
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parser.add_argument('query', help='Query SMILES or file')
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parser.add_argument('database', help='Database file (SDF or SMILES)')
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parser.add_argument('--method', '-m', default='morgan',
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choices=FINGERPRINT_METHODS.keys(),
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help='Fingerprint method (default: morgan)')
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parser.add_argument('--threshold', '-t', type=float, default=0.7,
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help='Similarity threshold (default: 0.7)')
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parser.add_argument('--radius', '-r', type=int, default=2,
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help='Morgan fingerprint radius (default: 2)')
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parser.add_argument('--bits', '-b', type=int, default=2048,
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help='Fingerprint size (default: 2048)')
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parser.add_argument('--metric', default='tanimoto',
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choices=['tanimoto', 'dice', 'cosine'],
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help='Similarity metric (default: tanimoto)')
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parser.add_argument('--output', '-o', help='Output CSV file')
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parser.add_argument('--max-display', type=int, default=20,
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help='Maximum hits to display (default: 20)')
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args = parser.parse_args()
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# Load query
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query_path = Path(args.query)
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if query_path.exists():
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# Query is a file
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query_mols = load_molecules(args.query)
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if not query_mols:
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print("Error: No valid molecules in query file")
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sys.exit(1)
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query_mol = query_mols[0]['mol']
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query_smiles = query_mols[0]['smiles']
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else:
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# Query is SMILES string
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query_mol = Chem.MolFromSmiles(args.query)
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query_smiles = args.query
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if query_mol is None:
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print(f"Error: Failed to parse query SMILES: {args.query}")
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sys.exit(1)
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print(f"Query: {query_smiles}")
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print(f"Method: {args.method}")
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print(f"Threshold: {args.threshold}")
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print(f"Loading database: {args.database}...")
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# Load database
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database = load_molecules(args.database)
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if not database:
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print("Error: No valid molecules in database")
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sys.exit(1)
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print(f"Loaded {len(database)} molecules")
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print("Searching...")
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# Perform search
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hits = similarity_search(
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query_mol, database,
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method=args.method,
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threshold=args.threshold,
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radius=args.radius,
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n_bits=args.bits,
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metric=args.metric
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)
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# Output results
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if args.output:
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write_results(hits, args.output)
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print(f"\nResults written to: {args.output}")
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print_results(hits, args.max_display)
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if __name__ == '__main__':
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main()
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