Fingerprints & Similarity

Fingerprints let you encode molecular structure as bit vectors for similarity searching and substructure screening across a library. The fingerprint type (Morgan, RDKit topological, or pattern), its radius and bit count, and the similarity metric are what most affect which compounds surface as hits.

Common fingerprints

• Morgan: AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
• RDKit topological: Chem.RDKFingerprint(mol, fpSize=2048)
• Pattern: Chem.PatternFingerprint(mol)

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

query = Chem.MolFromSmiles("c1ccccc1O")
library = [Chem.MolFromSmiles(s) for s in ["c1ccccc1", "CCO", "CCN"]]

query_fp = AllChem.GetMorganFingerprintAsBitVect(query, radius=2, nBits=2048)
scores: list[float] = []

for mol in library:
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
    scores.append(DataStructs.TanimotoSimilarity(query_fp, fp))

print(scores)

Substructure screening

1. Precompute pattern fingerprints for the library.
2. Filter by HasSubstructMatch only on candidates that pass fingerprint screening.

pattern = Chem.MolFromSmarts("c1ccc(cc1)O")
hits = []
for mol in library:
    if not mol.HasSubstructMatch(pattern):
        continue
    hits.append(mol)

Batch performance tips

• Use bit vectors (ExplicitBitVect) and avoid converting to hex strings during scoring.
• Chunk large libraries and parallelize scoring if CPU allows.
• Persist fingerprints to disk (e.g., .fp via DataStructs.BitVectToBinaryText) to skip recompute.