Bio Similarity Searching

Version Compatibility

Reference examples tested with: RDKit 2024.09+, scikit-learn 1.4+, mhfp 1.9+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Similarity Searching

Find structurally similar compounds and cluster libraries by similarity. The choice of similarity coefficient and fingerprint is task-aware: Tanimoto for symmetric similarity in lead optimization, Tversky for asymmetric "substructure-like" queries, Dice for higher sensitivity in low-similarity regimes, and MaxCommon Substructure (MCS) for scaffold-hopping. Tanimoto similarity above 0.7 is not a guarantee of activity preservation; activity cliffs (similar molecules with dissimilar activities) are common (Maggiora 2014).

For fingerprint choice, see chemoinformatics/molecular-descriptors. For 3D shape similarity, see chemoinformatics/shape-similarity.

Similarity Coefficient Taxonomy

Where a = set bits in fp1, b = set bits in fp2, c = bits in common.

When to Use Which Coefficient

Tanimoto Thresholds (Repository Starting Heuristics)

These bands are working defaults for ECFP4-like fingerprints, not transferable calibration. Inspect the target dataset's similarity distribution and known series before setting a cutoff. Maggiora's similarity principle states "similar molecules tend to have similar activity" -- but activity cliffs (Stumpfe & Bajorath 2012) violate this. Treat high ECFP4 similarity as a prioritization signal, not evidence that activity will be preserved.

Decision Tree by Scenario

Tanimoto Similarity (single query, large library)

Goal: Rank a library by ECFP4 Tanimoto similarity to a query molecule, returning hits above a threshold.

Approach: Generate ECFP4 fingerprints for all molecules once, then use BulkTanimotoSimilarity for O(N) lookup.

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

def precompute_fps(smiles_list, radius=2, nBits=2048):
    generator = rdFingerprintGenerator.GetMorganGenerator(
        radius=radius, fpSize=nBits)
    fps = []
    for smi in smiles_list:
        mol = Chem.MolFromSmiles(smi)
        if mol is None:
            fps.append(None)
        else:
            fps.append(generator.GetFingerprint(mol))
    return fps

def search(query_smi, library_fps, threshold=0.7):
    qmol = Chem.MolFromSmiles(query_smi)
    if qmol is None:
        raise ValueError('invalid query SMILES')
    generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
    qfp = generator.GetFingerprint(qmol)
    valid = [(i, fp) for i, fp in enumerate(library_fps) if fp is not None]
    sims = DataStructs.BulkTanimotoSimilarity(qfp, [fp for _, fp in valid])
    return [(source_i, sim) for (source_i, _), sim in zip(valid, sims)
            if sim >= threshold]

Tversky for Asymmetric Substructure-Like Search

Goal: Rank a library by how much each compound "contains" the features of the query (asymmetric).

Approach: Tversky with alpha=1, beta=0 rewards compounds containing query bits (substructure-like) while ignoring extra bits in the compound.

from rdkit import DataStructs

def tversky_substructure_like(qfp, lib_fps, alpha=1.0, beta=0.0):
    return [DataStructs.TverskySimilarity(qfp, f, alpha, beta) for f in lib_fps if f]

Use case: identifying analogs that extend a pharmacophore vs. exact-similarity ranking.

Butina Clustering

Goal: Group a library around Taylor-Butina centroids whose assigned neighbors are within the selected distance cutoff.

Approach: Compute upper-triangle distance matrix, apply Taylor-Butina with chosen distance cutoff.

from rdkit.ML.Cluster import Butina

def cluster(mols, cutoff=0.4):
    generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
    fps = [generator.GetFingerprint(m) for m in mols]
    n = len(fps)
    dists = []
    for i in range(1, n):
        sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i])
        dists.extend([1 - s for s in sims])
    return Butina.ClusterData(dists, n, cutoff, isDistData=True)

cutoff=0.4 means each assigned member was a neighbor of its selected centroid at Tanimoto >= 0.6. It does not guarantee that every pair of non-centroid members has Tanimoto >= 0.6. The first molecule in each returned cluster is the cluster centroid.

Trade-off: Butina materializes O(N^2) pairwise distances. Benchmark memory and runtime on the actual library; for much larger collections, use an approximate method such as an MHFP6 LSH forest.

Diversity Selection (MaxMin)

Goal: Select N diverse compounds from a library by maximizing the minimum pairwise distance.

from rdkit.SimDivFilters import rdSimDivPickers

picker = rdSimDivPickers.MaxMinPicker()
n_pick = 100
n_lib = len(fps)
selected = picker.LazyBitVectorPick(fps, n_lib, n_pick, seed=42)

LazyBitVectorPick is memory-efficient (does not materialize full distance matrix).

Maximum Common Substructure

Goal: Find the largest substructure shared across a set of molecules.

Approach: rdFMCS.FindMCS with parameters controlling atom/bond equivalence.

from rdkit.Chem import rdFMCS

def mcs_smarts(mols, timeout=60, ring_match='strict', atom_match='elements'):
    params = rdFMCS.MCSParameters()
    params.Timeout = timeout
    if ring_match == 'strict':
        params.BondCompareParameters.MatchFusedRings = True
        params.BondCompareParameters.MatchFusedRingsStrict = True
        params.BondCompareParameters.RingMatchesRingOnly = True
    if atom_match == 'elements':
        params.AtomCompareParameters.MatchValences = False
    result = rdFMCS.FindMCS(mols, params)
    return result.smartsString, result.numAtoms, result.numBonds

Use cases: identify scaffold across a series, build scaffold hopping queries, generate consensus pharmacophore.

Limit: MCS search can become combinatorial as input count, size, and structural divergence grow. Set a finite timeout, inspect result.canceled, and consider pre-clustering or reducing the comparison set; no molecule-count or atom-count boundary guarantees tractability.

Activity Cliff Diagnosis

Goal: Detect pairs of similar molecules with dissimilar activities (cliffs).

Approach: Compute pairwise ECFP4 Tanimoto + pIC50 delta. Flag pairs with high similarity and large activity gap.

def activity_cliffs(df, sim_threshold=0.85, activity_gap=2.0, activity_col='pIC50'):
    generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
    mols = [Chem.MolFromSmiles(s) for s in df['smiles']]
    if any(mol is None for mol in mols):
        raise ValueError('activity-cliff input contains invalid SMILES')
    fps = [generator.GetFingerprint(mol) for mol in mols]
    cliffs = []
    for i in range(len(fps)):
        sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[i+1:])
        for j_off, sim in enumerate(sims):
            j = i + 1 + j_off
            if sim >= sim_threshold:
                gap = abs(df[activity_col].iloc[i] - df[activity_col].iloc[j])
                if gap >= activity_gap:
                    cliffs.append((i, j, sim, gap))
    return cliffs

Activity cliffs flag (a) measurement noise, (b) cryptic SAR (e.g. ring-flip changing dihedral), (c) protein conformational selection, or (d) actually informative SAR. Cliffs are an opportunity for medchem investigation, not necessarily an error.

Large-Library Nearest Neighbor (MHFP6 + LSH Forest)

For large libraries, direct all-pairs comparison becomes expensive. The mhfp package provides an LSH-forest helper for approximate nearest-neighbor retrieval over MHFP6 fingerprints. Measure recall and latency against an exact subset for the project dataset.

from mhfp.encoder import MHFPEncoder
from mhfp.lsh_forest import LSHForestHelper

encoder = MHFPEncoder(2048)

def build_index(smiles_list):
    forest = LSHForestHelper()
    fingerprints = []
    for i, smiles in enumerate(smiles_list):
        fp = encoder.encode(smiles, radius=3)
        fingerprints.append(fp)
        forest.add(i, fp)
    forest.index()
    return forest, fingerprints

def query_index(forest, qmol, fingerprints, k=10):
    qfp = encoder.encode_mol(qmol, radius=3)
    return forest.query(qfp, k=k, data=fingerprints)

The returned neighbors are approximate in MHFP6 space. Benchmark them against an exact MHFP-distance search on a representative subset before choosing LSH parameters.