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c289d87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | from __future__ import annotations
from typing import Dict, Iterable, List, Sequence
import numpy as np
from rdkit import DataStructs
from rdkit.Chem import rdchem
from rdkit.ML.Cluster import Butina
def _np_to_explicit_bitvect(fp: np.ndarray) -> DataStructs.ExplicitBitVect:
bitvect = DataStructs.ExplicitBitVect(fp.shape[0])
on_bits = np.where(fp > 0)[0]
for idx in on_bits:
bitvect.SetBit(int(idx))
return bitvect
def cluster_ligands_butina(
ligand_ids: Sequence[str],
fingerprints: Sequence[np.ndarray],
cutoff: float = 0.35,
) -> Dict[str, int]:
"""Cluster ligands with Butina on Tanimoto distance over Morgan fingerprints."""
if not ligand_ids:
return {}
if len(ligand_ids) != len(fingerprints):
raise ValueError("ligand_ids and fingerprints must have same length")
fp_bv = [_np_to_explicit_bitvect(np.asarray(fp, dtype=int)) for fp in fingerprints]
dists: List[float] = []
for i in range(1, len(fp_bv)):
sims = DataStructs.BulkTanimotoSimilarity(fp_bv[i], fp_bv[:i])
dists.extend([1.0 - x for x in sims])
clusters = Butina.ClusterData(dists, len(fp_bv), cutoff, isDistData=True)
mapping: Dict[str, int] = {}
for c_idx, cluster in enumerate(clusters):
for member in cluster:
mapping[ligand_ids[member]] = c_idx
# Ensure all ligands map to a cluster, even edge cases.
for idx, ligand_id in enumerate(ligand_ids):
mapping.setdefault(ligand_id, max(mapping.values(), default=-1) + idx + 1)
return mapping
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