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