| 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 |
|
|
| |
| for idx, ligand_id in enumerate(ligand_ids): |
| mapping.setdefault(ligand_id, max(mapping.values(), default=-1) + idx + 1) |
| return mapping |
|
|
