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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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | from __future__ import annotations
import math
import re
from pathlib import Path
from typing import Any, Dict, List, Tuple
import numpy as np
from rdkit import Chem
from rdkit.Chem import rdMolAlign
TAG_RE = re.compile(r"^>\s*<\s*([^>]+?)\s*>", flags=re.IGNORECASE)
def _safe_float(value: object) -> float | None:
try:
v = float(value) # type: ignore[arg-type]
except Exception:
return None
if not np.isfinite(v):
return None
return float(v)
def _split_sdf_blocks(text: str) -> List[str]:
blocks = []
for part in text.split("$$$$"):
block = part.strip()
if block:
blocks.append(block + "\n$$$$\n")
return blocks
def _parse_block_tags(block: str) -> Dict[str, str]:
lines = block.splitlines()
tags: Dict[str, str] = {}
i = 0
while i < len(lines):
m = TAG_RE.match(lines[i].strip())
if not m:
i += 1
continue
tag = m.group(1).strip()
value = ""
if i + 1 < len(lines):
value = lines[i + 1].strip()
tags[tag] = value
i += 2
return tags
def _pose_molecules(blocks: List[str]) -> List[Chem.Mol | None]:
out: List[Chem.Mol | None] = []
for b in blocks:
mol = None
try:
mol = Chem.MolFromMolBlock(b, sanitize=False, removeHs=False, strictParsing=False)
except Exception:
mol = None
out.append(mol)
return out
def _pose_rmsd_stats(poses: List[Dict[str, Any]], mols: List[Chem.Mol | None], top_k: int = 5) -> Dict[str, float | None]:
if not poses:
return {"top_pose_rmsd_consistency": None, "geometry_similarity_top5": None}
top = sorted(poses, key=lambda x: x["score"])[: max(1, min(top_k, len(poses)))]
top_idx = [int(x["pose_idx"]) for x in top]
if not top_idx:
return {"top_pose_rmsd_consistency": None, "geometry_similarity_top5": None}
ref_idx = int(top_idx[0])
ref_mol = mols[ref_idx] if 0 <= ref_idx < len(mols) else None
if ref_mol is None:
return {"top_pose_rmsd_consistency": None, "geometry_similarity_top5": None}
rmsds: List[float] = []
for idx in top_idx[1:]:
if idx < 0 or idx >= len(mols):
continue
mol = mols[idx]
if mol is None:
continue
try:
if ref_mol.GetNumAtoms() != mol.GetNumAtoms():
continue
rmsd = float(rdMolAlign.GetBestRMS(ref_mol, mol))
if np.isfinite(rmsd):
rmsds.append(rmsd)
except Exception:
continue
if not rmsds:
return {"top_pose_rmsd_consistency": None, "geometry_similarity_top5": None}
mean_rmsd = float(np.mean(np.asarray(rmsds, dtype=float)))
geom_sim = float(1.0 / (1.0 + mean_rmsd))
return {"top_pose_rmsd_consistency": mean_rmsd, "geometry_similarity_top5": geom_sim}
def parse_rdock_output(path: str | Path) -> Dict[str, Any]:
"""
Parse real rDock SDF output and compute native + derived multi-pose features.
Native features are direct tags parsed from rDock output (`SCORE*`).
Derived features are computed from real generated poses and are explicitly marked as proxies where needed.
"""
source = Path(path)
if not source.exists():
raise ValueError(f"rDock output file does not exist: {source}")
if source.stat().st_size == 0:
raise ValueError(f"rDock output file is empty: {source}")
raw = source.read_text(encoding="utf-8", errors="ignore")
blocks = _split_sdf_blocks(raw)
if not blocks:
raise ValueError(f"No molecule blocks found in real rDock output: {source}")
poses: List[Dict[str, Any]] = []
for i, block in enumerate(blocks):
tags = _parse_block_tags(block)
score = _safe_float(tags.get("SCORE"))
if score is None:
continue
poses.append({"pose_idx": i, "pose_rank": int(tags.get("RI", i) or i) + 1, "score": score, "tags": tags})
if not poses:
raise ValueError(f"No SCORE fields found in real rDock output: {source}")
poses = sorted(poses, key=lambda x: x["score"])
scores = [float(p["score"]) for p in poses]
best = poses[0]
top3 = scores[:3]
top5 = scores[:5]
native = {
"rdock_total_score": float(best["score"]),
"rdock_pose_rank": int(best.get("pose_rank", 1)),
"n_generated_poses": int(len(scores)),
"best_pose_score": float(best["score"]),
"mean_top3_pose_score": float(np.mean(top3)),
"mean_top5_pose_score": float(np.mean(top5)),
"std_top5_pose_score": float(np.std(np.asarray(top5, dtype=float), ddof=0)),
"pose_score_gap_1_2": float(scores[1] - scores[0]) if len(scores) > 1 else np.nan,
"rdock_restraint_term": _safe_float(best["tags"].get("SCORE.RESTR")),
"rdock_internal_ligand_term": _safe_float(best["tags"].get("SCORE.INTRA")),
"rdock_polar_term": _safe_float(best["tags"].get("SCORE.INTER.POLAR")),
"rdock_vdw_term": _safe_float(best["tags"].get("SCORE.INTER.VDW")),
"rdock_inter_term": _safe_float(best["tags"].get("SCORE.INTER")),
"rdock_intra_vdw_term": _safe_float(best["tags"].get("SCORE.INTRA.VDW")),
"rdock_intra_dih_term": _safe_float(best["tags"].get("SCORE.INTRA.DIHEDRAL")),
"rdock_norm_score": _safe_float(best["tags"].get("SCORE.norm")),
"rdock_heavy_atoms": _safe_float(best["tags"].get("SCORE.heavy")),
}
mols = _pose_molecules(blocks)
rmsd_stats = _pose_rmsd_stats(poses=poses, mols=mols, top_k=5)
std5 = native["std_top5_pose_score"]
if std5 is None or (isinstance(std5, float) and (not np.isfinite(std5))):
pose_stability = None
else:
pose_stability = float(math.exp(-float(std5)))
inter_scores: List[float] = []
for p in poses[:5]:
v = _safe_float(p["tags"].get("SCORE.INTER"))
if v is not None:
inter_scores.append(float(v))
inter_std = float(np.std(np.asarray(inter_scores), ddof=0)) if inter_scores else np.nan
# Explicitly proxy-derived (not directly reported by rDock tags).
derived = {
"n_valid_poses": int(len(scores)),
"top_pose_rmsd_consistency": rmsd_stats["top_pose_rmsd_consistency"],
"contact_overlap_consistency": (float(1.0 / (1.0 + inter_std)) if np.isfinite(inter_std) else np.nan),
"hotspot_contact_frequency": (
float(np.mean([1.0 if (_safe_float(p["tags"].get("SCORE.INTER")) or 0.0) < 0.0 else 0.0 for p in poses[:5]]))
if poses
else np.nan
),
"subpocket_match_score": pose_stability if pose_stability is not None else np.nan,
"consensus_contact_score": (float(-np.mean(np.asarray(inter_scores, dtype=float))) if inter_scores else np.nan),
"consensus_hotspot_coverage": (
float(np.mean([1.0 if (_safe_float(p["tags"].get("SCORE.INTER.VDW")) or 0.0) < 0.0 else 0.0 for p in poses[:5]]))
if poses
else np.nan
),
"pose_stability_proxy": pose_stability,
"geometry_similarity_top5": rmsd_stats["geometry_similarity_top5"],
}
feature_provenance: List[Dict[str, Any]] = []
for name, val in native.items():
feature_provenance.append(
{
"feature_name": name,
"feature_source": "rdock_native",
"pose_source": "best_pose" if name.startswith("rdock_") else "top5",
"raw_output_file": str(source),
"parsed_from": f"{source}::SDF_TAG",
"feature_type": "exact" if val is not None and np.isfinite(float(val)) else "unavailable",
"available": bool(val is not None and np.isfinite(float(val))),
}
)
for name, val in derived.items():
feature_provenance.append(
{
"feature_name": name,
"feature_source": "rdock_derived",
"pose_source": "top5" if ("top" in name or "consensus" in name) else "best_pose",
"raw_output_file": str(source),
"parsed_from": f"{source}::derived",
"feature_type": "proxy" if val is not None and np.isfinite(float(val)) else "unavailable",
"available": bool(val is not None and np.isfinite(float(val))),
}
)
return {
"pose_path": str(source),
"score": float(native["best_pose_score"]),
"score_tag": "SCORE",
"all_scores": scores,
"pose_count": int(len(scores)),
"poses": poses,
"native_features": native,
"derived_features": derived,
"feature_provenance": feature_provenance,
}
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