File size: 6,743 Bytes
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 | from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterable
import numpy as np
import pandas as pd
REQUIRED_REFERENCE_DIAG_COLUMNS = [
"dataset",
"target_name",
"reference_ligand_id",
"reference_ligand_score",
"reference_ligand_rank_percentile",
"adaptive_best_score",
"exhaustive_best_score",
]
REQUIRED_BACKEND_COMPARISON_COLUMNS = [
"dataset",
"backend",
"analysis_scope",
"available",
"n_ligands",
"runtime_seconds",
"runtime_per_ligand",
"reference_ligand_score",
"reference_ligand_rank_percentile",
"best_score",
"score_gap_reference_vs_best",
"reference_pose_centroid_distance_A",
"reference_pose_in_expected_pocket",
]
REQUIRED_POCKET_PREP_COLUMNS = [
"dataset",
"backend",
"pocket_variant",
"prep_variant",
"reference_ligand_score",
"reference_pose_centroid_distance_A",
"reference_pose_in_expected_pocket",
]
@dataclass(frozen=True)
class ManualAgentsCheck:
exists: bool
path: str
manually_read: bool
def manual_agents_check(path: str | Path, manually_read: bool = True) -> ManualAgentsCheck:
p = Path(path).expanduser().resolve()
return ManualAgentsCheck(exists=p.exists(), path=str(p), manually_read=bool(manually_read and p.exists()))
def _safe_float(x: Any, default: float = np.nan) -> float:
try:
v = float(x)
return float(v) if np.isfinite(v) else float(default)
except Exception:
return float(default)
def compute_rank_percentile(df: pd.DataFrame, ligand_id: str, score_col: str, lower_is_better: bool = True) -> float:
if df.empty or score_col not in df.columns:
return np.nan
d = df[["ligand_id", score_col]].copy()
d["ligand_id"] = d["ligand_id"].astype(str)
d[score_col] = pd.to_numeric(d[score_col], errors="coerce")
d = d.dropna(subset=[score_col]).copy()
if d.empty:
return np.nan
d = d.sort_values(score_col, ascending=bool(lower_is_better)).reset_index(drop=True)
hit = d.index[d["ligand_id"] == str(ligand_id)]
if len(hit) == 0:
return np.nan
rank = int(hit[0]) + 1
return float(100.0 * rank / max(1, d.shape[0]))
def select_suspicious_datasets(reference_diag_df: pd.DataFrame, threshold_pct: float = 25.0, max_deep: int = 2) -> list[str]:
if reference_diag_df.empty:
return []
d = reference_diag_df.copy()
d["reference_ligand_rank_percentile"] = pd.to_numeric(d["reference_ligand_rank_percentile"], errors="coerce")
d = d.dropna(subset=["reference_ligand_rank_percentile"]).copy()
d = d[d["reference_ligand_rank_percentile"] >= float(threshold_pct)].copy()
if d.empty:
return []
d = d.sort_values("reference_ligand_rank_percentile", ascending=False)
return d["dataset"].astype(str).head(int(max(1, max_deep))).tolist()
def validate_required_columns(df: pd.DataFrame, required_columns: Iterable[str]) -> list[str]:
cols = set(df.columns)
missing = [c for c in required_columns if c not in cols]
return missing
def recommend_toolchain(
backend_comparison_df: pd.DataFrame,
pocket_prep_df: pd.DataFrame,
) -> dict[str, Any]:
"""
Choose practical recommendation from diagnostic evidence.
Policy:
- Prefer backend with best median reference rank percentile under "subset_default" scope.
- Penalize poor runtime-per-ligand if >2x best runtime.
- Use pocket/prep gain to detect whether core issue is mostly preparation/pocket.
"""
out: dict[str, Any] = {
"recommended_main_backend": "rdock",
"recommended_rescoring": "gnina_or_haddock_shortlist_optional",
"keep_haddock": "optional_shortlist_only",
"main_failure_source": "mixed",
"evidence": [],
}
bdf = backend_comparison_df.copy()
if not bdf.empty:
bdf = bdf[bdf["analysis_scope"].astype(str) == "subset_default"].copy()
if not bdf.empty:
bdf["runtime_per_ligand"] = pd.to_numeric(bdf["runtime_per_ligand"], errors="coerce")
bdf["reference_ligand_rank_percentile"] = pd.to_numeric(bdf["reference_ligand_rank_percentile"], errors="coerce")
agg = (
bdf.groupby("backend", as_index=False)
.agg(
median_ref_rank_pct=("reference_ligand_rank_percentile", "median"),
median_runtime=("runtime_per_ligand", "median"),
available_ratio=("available", "mean"),
)
.sort_values(["median_ref_rank_pct", "median_runtime"], ascending=[True, True])
)
if not agg.empty:
best = agg.iloc[0]
best_runtime = max(1e-9, _safe_float(best["median_runtime"], 1.0))
candidate = str(best["backend"])
if _safe_float(best["available_ratio"], 0.0) < 0.5:
out["evidence"].append("best_rank_backend_not_reliably_available")
else:
out["recommended_main_backend"] = candidate
rd = agg[agg["backend"].astype(str).str.lower() == "rdock"]
if not rd.empty:
rd_runtime = max(1e-9, _safe_float(rd.iloc[0]["median_runtime"], best_runtime))
if best_runtime > 2.0 * rd_runtime and candidate != "rdock":
out["evidence"].append("candidate_backend_too_slow_vs_rdock")
out["recommended_main_backend"] = "rdock"
out["evidence"].append(f"rank_runtime_table={agg.to_dict(orient='records')}")
pdf = pocket_prep_df.copy()
if not pdf.empty:
pdf["reference_ligand_score"] = pd.to_numeric(pdf["reference_ligand_score"], errors="coerce")
deltas: list[float] = []
for (_, _), sub in pdf.groupby(["dataset", "backend"]):
default = sub[(sub["pocket_variant"] == "default") & (sub["prep_variant"] == "default")]
if default.empty:
continue
default_score = _safe_float(default.iloc[0]["reference_ligand_score"], np.nan)
best_score = _safe_float(sub["reference_ligand_score"].min(), np.nan)
if np.isfinite(default_score) and np.isfinite(best_score):
deltas.append(default_score - best_score)
mean_gain = float(np.mean(np.asarray(deltas, dtype=float))) if deltas else 0.0
if mean_gain > 1.0:
out["main_failure_source"] = "pocket_or_preparation"
elif mean_gain > 0.2:
out["main_failure_source"] = "mixed"
else:
out["main_failure_source"] = "backend_scoring_limitations"
out["evidence"].append(f"mean_reference_gain_from_pocket_prep={mean_gain:.3f}")
out["keep_haddock"] = "optional_shortlist_only"
return out
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