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import argparse
import csv
import json
import random
import shutil
import sys
import time
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from docking_pipeline.provenance import CommandRunner, RDockPipelineError, fail_if_bad_command, probe_version, require_executable, require_file
from docking_pipeline.rdock import RDockEngine, RDockRunConfig, TargetConfig
from docking_pipeline.reports.plots import plot_adaptive_benchmark_outputs, plot_score_outputs
from docking_pipeline.sdf import ligand_id_from_block, parse_tags, split_sdf_file, write_rows_csv, write_sdf_blocks
def _read_rows(path: str | Path) -> list[dict[str, str]]:
with Path(path).open("r", encoding="utf-8", newline="") as handle:
return list(csv.DictReader(handle))
def _float(value: object, default: float = 0.0) -> float:
try:
text = str(value).strip()
if not text:
return default
return float(text)
except Exception:
return default
def _boolish(value: object) -> bool:
return str(value).strip().lower() in {"1", "true", "yes", "y"}
def _write_json(path: Path, payload: dict[str, Any] | list[dict[str, Any]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
def _load_targets(path: Path) -> list[dict[str, str]]:
text = require_file(path, "benchmark targets config").read_text(encoding="utf-8")
payload = json.loads(text)
targets = payload.get("targets", [])
if not isinstance(targets, list) or not targets:
raise RDockPipelineError(f"No targets defined in {path}")
return [dict(item) for item in targets]
def _extract_receptor_and_ligand(
pdb: Path,
receptor_chain: str,
ligand_resname: str,
ligand_chain: str,
out_dir: Path,
) -> tuple[Path, Path]:
out_dir.mkdir(parents=True, exist_ok=True)
receptor = out_dir / "receptor.pdb"
ligand_pdb = out_dir / "reference_ligand.pdb"
receptor_lines: list[str] = []
ligand_lines: list[str] = []
chains = {c.strip() for c in receptor_chain.split(",") if c.strip()}
wanted_resname = ligand_resname.upper().strip()
wanted_chain = ligand_chain.strip()
for line in pdb.read_text(encoding="utf-8", errors="ignore").splitlines():
record = line[:6].strip()
chain = line[21:22].strip()
resname = line[17:20].strip().upper()
if record == "ATOM" and (not chains or chain in chains):
receptor_lines.append(line)
if record == "HETATM" and resname == wanted_resname and (not wanted_chain or chain == wanted_chain):
ligand_lines.append(line)
if not receptor_lines:
raise RDockPipelineError(f"No receptor atoms found in {pdb} for chain(s) {receptor_chain}")
if not ligand_lines:
raise RDockPipelineError(f"No reference ligand {wanted_resname} chain {wanted_chain or '*'} found in {pdb}")
receptor.write_text("\n".join(receptor_lines + ["END", ""]), encoding="utf-8")
ligand_pdb.write_text("\n".join(ligand_lines + ["END", ""]), encoding="utf-8")
return receptor, ligand_pdb
def _obabel_convert(runner: CommandRunner, stage: str, input_path: Path, output_path: Path, extra_args: list[str], cwd: Path) -> Path:
obabel = require_executable("obabel")
rec = runner.run(
stage,
[obabel, str(input_path.resolve()), *extra_args, "-O", str(output_path.resolve())],
cwd,
cwd / f"{stage}.stdout.log",
cwd / f"{stage}.stderr.log",
)
fail_if_bad_command(rec, f"OpenBabel {stage}")
return require_file(output_path, f"OpenBabel output {stage}")
def _count_sdf(path: Path) -> int:
return len(split_sdf_file(path))
def _write_ligand_smi(rows: list[dict[str, str]], out_path: Path, count: int) -> Path:
selected = rows[:count]
if len(selected) != count:
raise RDockPipelineError(f"Requested {count} ligands but only found {len(selected)} rows in {out_path.parent}")
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text("".join(f"{row['smiles']} {row['ligand_id']}\n" for row in selected), encoding="utf-8")
return out_path
def _load_input_block_map(sdf_path: Path) -> dict[str, str]:
block_map: dict[str, str] = {}
for idx, block in enumerate(split_sdf_file(sdf_path)):
tags = parse_tags(block)
ligand_id = ligand_id_from_block(block, tags, idx)
block_map[ligand_id] = block
return block_map
def _write_selected_sdf(block_map: dict[str, str], ligand_ids: list[str], out_path: Path) -> Path:
missing = [ligand_id for ligand_id in ligand_ids if ligand_id not in block_map]
if missing:
raise RDockPipelineError(f"Missing {len(missing)} ligand IDs in prepared SDF: {missing[:10]}")
write_sdf_blocks([block_map[ligand_id] for ligand_id in ligand_ids], out_path)
return out_path
def _prepare_library(
runner: CommandRunner,
ligands_csv: Path,
ligand_count: int,
out_dir: Path,
resume: bool,
) -> tuple[list[dict[str, str]], Path]:
rows = _read_rows(ligands_csv)
if len(rows) < ligand_count:
raise RDockPipelineError(f"{ligands_csv} contains {len(rows)} ligands, expected at least {ligand_count}")
selected = rows[:ligand_count]
smi = out_dir / "ligands" / "all_ligands.smi"
sdf = out_dir / "ligands" / "all_ligands.sdf"
if not (resume and sdf.exists() and _count_sdf(sdf) == ligand_count):
_write_ligand_smi(selected, smi, ligand_count)
_obabel_convert(runner, "smiles_to_all_ligands_sdf", smi, sdf, ["--gen3d", "-h"], out_dir)
if _count_sdf(sdf) != ligand_count:
raise RDockPipelineError(f"{sdf} does not contain exactly {ligand_count} ligands")
return selected, sdf
def _build_model_rows(rows: list[dict[str, str]]) -> list[dict[str, Any]]:
numeric_keys = [
"reference_similarity",
"molecular_weight",
"xlogp",
"tpsa",
"hbd",
"hba",
"rotatable_bonds",
"heavy_atom_count",
]
features: dict[str, list[float]] = {key: [] for key in numeric_keys}
parsed: list[dict[str, Any]] = []
for row in rows:
item: dict[str, Any] = dict(row)
item["scaffold_match_num"] = 1.0 if _boolish(row.get("scaffold_match", "")) else 0.0
item["is_reference_num"] = 1.0 if _boolish(row.get("is_reference", "")) else 0.0
sim = _float(row.get("reference_similarity"), 0.0)
item["model_score"] = sim + 0.1 * item["scaffold_match_num"] + 0.05 * item["is_reference_num"]
for key in numeric_keys:
value = _float(row.get(key), 0.0)
item[key] = value
features[key].append(value)
parsed.append(item)
means = {key: (sum(vals) / len(vals) if vals else 0.0) for key, vals in features.items()}
stdevs = {}
for key, vals in features.items():
if not vals:
stdevs[key] = 1.0
continue
mean = means[key]
var = sum((value - mean) ** 2 for value in vals) / max(1, len(vals))
stdevs[key] = var**0.5 or 1.0
for item in parsed:
item["feature_vector"] = [
(float(item[key]) - means[key]) / stdevs[key]
for key in numeric_keys
] + [float(item["scaffold_match_num"]), float(item["is_reference_num"])]
item["adaptive_priority"] = float(item["model_score"])
item["actually_docked"] = False
return parsed
def _distance(a: list[float], b: list[float]) -> float:
return sum((x - y) ** 2 for x, y in zip(a, b)) ** 0.5
class AdaptiveSurrogateScheduler:
def __init__(self, rows: list[dict[str, Any]], budget: int, batch_size: int) -> None:
self.rows = [dict(row) for row in rows]
self.by_id = {str(row["ligand_id"]): row for row in self.rows}
self.budget = int(budget)
self.batch_size = int(batch_size)
self.completed: list[str] = []
self.observed_scores: dict[str, float] = {}
self.attempted: set[str] = set()
def next_batch(self) -> list[str]:
remaining = [row for row in self.rows if str(row["ligand_id"]) not in self.attempted]
remaining_budget = self.budget - len(self.attempted)
if remaining_budget <= 0 or not remaining:
return []
ordered = sorted(
remaining,
key=lambda row: (-float(row["adaptive_priority"]), -float(row["model_score"]), str(row["ligand_id"])),
)
batch = [str(row["ligand_id"]) for row in ordered[: min(self.batch_size, remaining_budget)]]
return batch
def update(self, selected_ids: list[str], best_rows: list[dict[str, str]]) -> float:
start = time.time()
for ligand_id in selected_ids:
self.attempted.add(str(ligand_id))
for row in best_rows:
ligand_id = str(row["ligand_id"])
score = _float(row.get("SCORE"), 0.0)
self.observed_scores[ligand_id] = score
self.by_id[ligand_id]["actually_docked"] = True
self.completed.append(ligand_id)
docked_ids = list(self.observed_scores)
if not docked_ids:
return 0.0
for row in self.rows:
ligand_id = str(row["ligand_id"])
if ligand_id in self.observed_scores:
row["adaptive_priority"] = 1e9 - self.observed_scores[ligand_id]
continue
neighbors: list[tuple[float, float]] = []
for docked_id in docked_ids:
docked_row = self.by_id[docked_id]
dist = _distance(row["feature_vector"], docked_row["feature_vector"])
neighbors.append((dist, self.observed_scores[docked_id]))
neighbors.sort(key=lambda item: item[0])
top = neighbors[: min(12, len(neighbors))]
weights = [1.0 / (1.0 + dist) for dist, _ in top]
total_weight = sum(weights) or 1.0
predicted = sum(weight * score for weight, (_, score) in zip(weights, top)) / total_weight
prior = -10.0 * float(row["model_score"])
uncertainty = sum(dist for dist, _ in top) / max(1, len(top))
blended = (0.7 * predicted) + (0.3 * prior)
row["adaptive_priority"] = -blended + (0.05 * uncertainty)
return time.time() - start
def _read_result_rows(path: Path) -> list[dict[str, str]]:
return _read_rows(path) if path.exists() else []
def _augment_full_rows(rows: list[dict[str, str]]) -> list[dict[str, Any]]:
ordered = sorted(rows, key=lambda row: (_float(row.get("SCORE"), float("inf")), str(row.get("ligand_id", ""))))
total = max(1, len(ordered))
enriched: list[dict[str, Any]] = []
for idx, row in enumerate(ordered, start=1):
item: dict[str, Any] = dict(row)
item["full_rank"] = idx
item["full_percentile"] = 100.0 if total == 1 else 100.0 * (1.0 - ((idx - 1) / (total - 1)))
enriched.append(item)
return enriched
def _percentile_from_rank(rank: int, total: int) -> float:
if total <= 1:
return 100.0
return 100.0 * (1.0 - ((rank - 1) / (total - 1)))
def _append_rank_metrics(rows: list[dict[str, Any]], full_rank_map: dict[str, int], total: int) -> list[dict[str, Any]]:
enriched: list[dict[str, Any]] = []
for row in rows:
item = dict(row)
ligand_id = str(item["ligand_id"])
rank = full_rank_map.get(ligand_id)
item["full_rank"] = rank if rank is not None else ""
item["full_percentile"] = _percentile_from_rank(rank, total) if rank is not None else ""
enriched.append(item)
return enriched
def _top_overlap(full_rows: list[dict[str, Any]], sample_rows: list[dict[str, Any]], n: int) -> int:
full_top = {str(row["ligand_id"]) for row in full_rows[:n]}
sample_top = {str(row["ligand_id"]) for row in sorted(sample_rows, key=lambda row: _float(row.get("SCORE"), float("inf")))[:n]}
return len(full_top & sample_top)
def _copy_full_aliases(root: Path, target_config: TargetConfig, full_rows: list[dict[str, Any]], n_runs: int) -> None:
target_dir = root / "target"
target_dir.mkdir(parents=True, exist_ok=True)
shutil.copy2(require_file(target_config.receptor_mol2, "target mol2"), target_dir / "target.mol2")
shutil.copy2(require_file(target_config.reference_ligand, "reference ligand"), target_dir / "reference_ligand.sdf")
shutil.copy2(require_file(target_config.receptor_prm, "target prm"), target_dir / "target.prm")
shutil.copy2(require_file(target_config.cavity_as, "cavity"), target_dir / Path(target_config.cavity_as).name)
best_path = root / "poses" / f"best_ligand_{n_runs}.sdf"
require_file(best_path, f"best_ligand_{n_runs}.sdf")
if not full_rows:
raise RDockPipelineError(f"No full docking rows found in {root}")
def _target_complete(root: Path, ligand_count: int, budget: int, n_runs: int) -> bool:
required = [
root / "ligands" / "all_ligands.sdf",
root / "tables" / "full_docking_scores.csv",
root / "tables" / "adaptive_scores.csv",
root / "tables" / "random_baseline_scores.csv",
root / "metrics" / "adaptive_benchmark_metrics.json",
root / "metrics" / "rdock_metrics.json",
root / "poses" / f"best_ligand_{n_runs}.sdf",
]
if not all(path.exists() for path in required):
return False
if _count_sdf(root / "ligands" / "all_ligands.sdf") != ligand_count:
return False
adaptive_rows = _read_result_rows(root / "tables" / "adaptive_scores.csv")
random_rows = _read_result_rows(root / "tables" / "random_baseline_scores.csv")
return len(adaptive_rows) >= budget and len(random_rows) >= budget
def _run_full_docking(
engine: RDockEngine,
target_config: TargetConfig,
all_ligands_sdf: Path,
target_root: Path,
n_runs: int,
jobs: str,
resume: bool,
) -> tuple[dict[str, Any], list[dict[str, Any]], float]:
start = time.time()
artifacts = engine.dock_sdf(target_config, all_ligands_sdf, target_root, n_runs=n_runs, jobs=jobs, run_id=target_root.name, resume=resume)
elapsed = time.time() - start
full_rows = _augment_full_rows(_read_rows(artifacts.best_per_ligand_csv))
write_rows_csv(full_rows, target_root / "tables" / "best_per_ligand.csv")
write_rows_csv(full_rows, target_root / "tables" / "full_docking_scores.csv")
metrics = {
"library_size": len(full_rows),
"successful_ligands": len(full_rows),
"failed_ligands": max(0, _count_sdf(all_ligands_sdf) - len(full_rows)),
"pose_count": _count_sdf(Path(artifacts.all_poses_sdf)),
"best_SCORE": _float(full_rows[0]["SCORE"]) if full_rows else None,
"full_docking_seconds": elapsed,
"n_runs": int(n_runs),
"best_ligand_id": full_rows[0]["ligand_id"] if full_rows else None,
}
_write_json(target_root / "metrics" / "rdock_metrics.json", metrics)
return metrics, full_rows, elapsed
def _run_random_baseline(
engine: RDockEngine,
target_config: TargetConfig,
target_root: Path,
all_blocks: dict[str, str],
rows: list[dict[str, Any]],
budget: int,
n_runs: int,
jobs: str,
resume: bool,
seed: int = 42,
) -> tuple[list[dict[str, Any]], float]:
random_root = target_root / "random_run"
existing = _read_result_rows(random_root / "tables" / "best_per_ligand.csv")
if resume and len(existing) >= budget:
return [dict(row) for row in existing[:budget]], 0.0
random_root.mkdir(parents=True, exist_ok=True)
population = [str(row["ligand_id"]) for row in rows]
selected_ids = random.Random(seed).sample(population, budget)
_write_json(random_root / "selection.json", {"seed": seed, "ligand_ids": selected_ids})
baseline_sdf = random_root / "ligands" / "random_baseline.sdf"
_write_selected_sdf(all_blocks, selected_ids, baseline_sdf)
start = time.time()
engine.dock_sdf(target_config, baseline_sdf, random_root, n_runs=n_runs, jobs=jobs, run_id=f"{target_root.name}_random", resume=resume)
elapsed = time.time() - start
random_rows = []
selected_map = {ligand_id: idx + 1 for idx, ligand_id in enumerate(selected_ids)}
for row in _read_rows(random_root / "tables" / "best_per_ligand.csv"):
item: dict[str, Any] = dict(row)
item["selection_order"] = selected_map.get(str(item["ligand_id"]), "")
item["strategy"] = "random"
random_rows.append(item)
random_rows.sort(key=lambda row: int(row.get("selection_order", 0) or 0))
write_rows_csv(random_rows, target_root / "tables" / "random_baseline_scores.csv")
return random_rows, elapsed
def _run_adaptive_benchmark(
engine: RDockEngine,
target_config: TargetConfig,
target_root: Path,
all_blocks: dict[str, str],
rows: list[dict[str, Any]],
budget: int,
batch_size: int,
n_runs: int,
jobs: str,
resume: bool,
) -> tuple[list[dict[str, Any]], list[dict[str, Any]], float, float]:
adaptive_root = target_root / "adaptive_run"
adaptive_root.mkdir(parents=True, exist_ok=True)
scheduler = AdaptiveSurrogateScheduler(rows, budget=budget, batch_size=batch_size)
adaptive_rows: list[dict[str, Any]] = []
batch_metrics: list[dict[str, Any]] = []
docking_seconds = 0.0
model_seconds = 0.0
batch_index = 0
while True:
batch_ids = scheduler.next_batch()
if not batch_ids:
break
batch_dir = adaptive_root / "rdock" / f"batch_{batch_index:03d}"
selection_path = batch_dir / "selection.json"
if resume and selection_path.exists():
payload = json.loads(selection_path.read_text(encoding="utf-8"))
batch_ids = [str(x) for x in payload.get("ligand_ids", batch_ids)]
else:
batch_dir.mkdir(parents=True, exist_ok=True)
selection_payload = {
"batch_index": batch_index,
"ligand_ids": batch_ids,
"selection_state": [
{
"ligand_id": ligand_id,
"model_score": scheduler.by_id[ligand_id]["model_score"],
"adaptive_priority": scheduler.by_id[ligand_id]["adaptive_priority"],
}
for ligand_id in batch_ids
],
}
_write_json(selection_path, selection_payload)
batch_sdf = adaptive_root / "ligands" / f"batch_{batch_index:03d}.sdf"
if not (resume and batch_sdf.exists() and _count_sdf(batch_sdf) == len(batch_ids)):
_write_selected_sdf(all_blocks, batch_ids, batch_sdf)
batch_start = time.time()
engine.dock_sdf(target_config, batch_sdf, batch_dir, n_runs=n_runs, jobs=jobs, run_id=f"{target_root.name}_adaptive_batch_{batch_index:03d}", resume=resume)
batch_docking_seconds = time.time() - batch_start
docking_seconds += batch_docking_seconds
batch_best = _read_rows(batch_dir / "tables" / "best_per_ligand.csv")
selection_meta = json.loads(selection_path.read_text(encoding="utf-8"))
selection_lookup = {str(item["ligand_id"]): item for item in selection_meta.get("selection_state", [])}
batch_rows: list[dict[str, Any]] = []
for local_index, row in enumerate(batch_best, start=1):
ligand_id = str(row["ligand_id"])
selection_item = selection_lookup.get(ligand_id, {})
item: dict[str, Any] = dict(row)
item["batch"] = batch_index
item["selection_order"] = (batch_index * batch_size) + local_index
item["model_score"] = selection_item.get("model_score", scheduler.by_id[ligand_id]["model_score"])
item["adaptive_priority"] = selection_item.get("adaptive_priority", scheduler.by_id[ligand_id]["adaptive_priority"])
item["strategy"] = "adaptive"
batch_rows.append(item)
adaptive_rows.extend(batch_rows)
update_seconds = scheduler.update(batch_ids, batch_best)
model_seconds += update_seconds
batch_metrics.append(
{
"batch": batch_index,
"selected_count": len(batch_ids),
"successful_count": len(batch_best),
"failed_count": max(0, len(batch_ids) - len(batch_best)),
"docking_seconds": batch_docking_seconds,
"model_seconds": update_seconds,
"best_score_after_batch": min(_float(row.get("SCORE"), float("inf")) for row in adaptive_rows) if adaptive_rows else "",
}
)
batch_index += 1
write_rows_csv(adaptive_rows, target_root / "tables" / "adaptive_scores.csv")
write_rows_csv(batch_metrics, target_root / "tables" / "adaptive_batches.csv")
return adaptive_rows, batch_metrics, docking_seconds, model_seconds
def _build_metrics(
target_id: str,
library_size: int,
full_rows: list[dict[str, Any]],
adaptive_rows: list[dict[str, Any]],
random_rows: list[dict[str, Any]],
full_seconds: float,
adaptive_docking_seconds: float,
adaptive_model_seconds: float,
random_seconds: float,
) -> dict[str, Any]:
full_rank_map = {str(row["ligand_id"]): int(row["full_rank"]) for row in full_rows}
adaptive_ranked = _append_rank_metrics(adaptive_rows, full_rank_map, len(full_rows))
random_ranked = _append_rank_metrics(random_rows, full_rank_map, len(full_rows))
adaptive_best = min(adaptive_ranked, key=lambda row: _float(row.get("SCORE"), float("inf"))) if adaptive_ranked else None
random_best = min(random_ranked, key=lambda row: _float(row.get("SCORE"), float("inf"))) if random_ranked else None
full_best = full_rows[0] if full_rows else None
failed = max(0, library_size - len(full_rows))
return {
"target_id": target_id,
"total_candidate_library_size": library_size,
"full_success_count": len(full_rows),
"adaptive_success_count": len(adaptive_ranked),
"random_success_count": len(random_ranked),
"failed_docking_count": failed,
"success_rate": (len(full_rows) / library_size) if library_size else 0.0,
"best_full_SCORE": _float(full_best.get("SCORE")) if full_best else None,
"best_adaptive_SCORE": _float(adaptive_best.get("SCORE")) if adaptive_best else None,
"best_random_SCORE": _float(random_best.get("SCORE")) if random_best else None,
"full_best_percentile_of_full": 100.0 if full_best else None,
"adaptive_best_percentile_of_full": _float(adaptive_best.get("full_percentile")) if adaptive_best else None,
"random_best_percentile_of_full": _float(random_best.get("full_percentile")) if random_best else None,
"adaptive_over_random_best_score_delta": (_float(random_best.get("SCORE")) - _float(adaptive_best.get("SCORE"))) if adaptive_best and random_best else None,
"adaptive_over_random_percentile_delta": (_float(adaptive_best.get("full_percentile")) - _float(random_best.get("full_percentile"))) if adaptive_best and random_best else None,
"top1_overlap_with_full": _top_overlap(full_rows, adaptive_ranked, 1),
"top5_overlap_with_full": _top_overlap(full_rows, adaptive_ranked, 5),
"top10_overlap_with_full": _top_overlap(full_rows, adaptive_ranked, 10),
"random_top1_overlap_with_full": _top_overlap(full_rows, random_ranked, 1),
"random_top5_overlap_with_full": _top_overlap(full_rows, random_ranked, 5),
"random_top10_overlap_with_full": _top_overlap(full_rows, random_ranked, 10),
"full_docking_seconds": full_seconds,
"adaptive_docking_seconds": adaptive_docking_seconds,
"adaptive_model_seconds": adaptive_model_seconds,
"random_docking_seconds": random_seconds,
"ligands_docked_by_adaptive": len(adaptive_ranked),
"ligands_docked_by_random": len(random_ranked),
}
def _merge_commands(target_root: Path) -> None:
dst = target_root / "commands.log"
existing = dst.read_text(encoding="utf-8", errors="ignore") if dst.exists() else ""
with dst.open("w", encoding="utf-8") as out:
if existing:
out.write(existing)
for candidate in [
target_root / "prep_commands.log",
target_root / "adaptive_run" / "commands.log",
target_root / "random_run" / "commands.log",
]:
if candidate.exists():
out.write(candidate.read_text(encoding="utf-8", errors="ignore"))
if not dst.exists():
dst.write_text("", encoding="utf-8")
def _write_target_report(target_root: Path, target: dict[str, str], metrics: dict[str, Any], plots: list[str], notes: list[str]) -> None:
report = [
f"# Adaptive + rDock Benchmark: {target['target_id']}",
"",
"## Input Summary",
f"- PDB: `{target['pdb_id']}`",
f"- Receptor chain: `{target['receptor_chain']}`",
f"- Reference ligand: `{target['reference_ligand_resname']}` chain `{target['reference_ligand_chain']}`",
f"- Ligand CSV: `{target['ligands_csv']}`",
f"- Library size: `{metrics['total_candidate_library_size']}`",
"",
"## Metrics",
]
for key in [
"best_full_SCORE",
"best_adaptive_SCORE",
"best_random_SCORE",
"adaptive_best_percentile_of_full",
"random_best_percentile_of_full",
"adaptive_over_random_best_score_delta",
"adaptive_over_random_percentile_delta",
"full_docking_seconds",
"adaptive_docking_seconds",
"adaptive_model_seconds",
"random_docking_seconds",
"ligands_docked_by_adaptive",
"ligands_docked_by_random",
"failed_docking_count",
"success_rate",
]:
report.append(f"- {key}: `{metrics.get(key)}`")
report.extend(["", "## Plots"])
report.extend([f"- `{path}`" for path in plots] or ["- No plots generated"])
report.extend(["", "## Notes"])
report.extend([f"- {note}" for note in notes] or ["- No extra notes"])
(target_root / "report.md").write_text("\n".join(report) + "\n", encoding="utf-8")
def _sanity_checks(target_root: Path, ligand_count: int, budget: int, n_runs: int) -> list[str]:
checks: list[str] = []
all_ligands = target_root / "ligands" / "all_ligands.sdf"
if _count_sdf(all_ligands) != ligand_count:
raise RDockPipelineError(f"{all_ligands} does not contain {ligand_count} ligands")
checks.append(f"all_ligands.sdf has {ligand_count} ligands")
full_rows = _read_rows(target_root / "tables" / "full_docking_scores.csv")
if len(full_rows) != ligand_count:
checks.append(f"full docking deviation: expected {ligand_count}, got {len(full_rows)}")
else:
checks.append("full docking returned expected ligand count")
best_ligand = require_file(target_root / "poses" / f"best_ligand_{n_runs}.sdf", f"best_ligand_{n_runs}.sdf")
if _count_sdf(best_ligand) != n_runs:
checks.append(f"best_ligand_{n_runs}.sdf deviation: expected {n_runs}, got {_count_sdf(best_ligand)}")
else:
checks.append(f"best_ligand_{n_runs}.sdf contains {n_runs} poses")
adaptive_rows = _read_rows(target_root / "tables" / "adaptive_scores.csv")
random_rows = _read_rows(target_root / "tables" / "random_baseline_scores.csv")
if len(adaptive_rows) < budget:
checks.append(f"adaptive deviation: budget {budget}, successful {len(adaptive_rows)}")
if len(random_rows) < budget:
checks.append(f"random deviation: budget {budget}, successful {len(random_rows)}")
if len(adaptive_rows) >= budget and len(random_rows) >= budget:
checks.append("adaptive and random produced budget-sized result tables")
return checks
def run_target(target: dict[str, str], args: argparse.Namespace, out_root: Path) -> dict[str, Any]:
target_root = out_root / target["target_id"]
if args.resume and _target_complete(target_root, args.ligands_per_target, args.adaptive_budget, args.n_runs):
metrics = json.loads((target_root / "metrics" / "adaptive_benchmark_metrics.json").read_text(encoding="utf-8"))
return {"target_id": target["target_id"], "status": "resumed_complete", "metrics": metrics, "run_dir": str(target_root)}
if target_root.exists() and args.force and not args.resume:
shutil.rmtree(target_root)
target_root.mkdir(parents=True, exist_ok=True)
runner = CommandRunner(target_root / "prep_commands.log")
pdb = require_file(target["pdb_path"], f"PDB file for {target['target_id']}")
receptor, ligand_pdb = _extract_receptor_and_ligand(
pdb,
target["receptor_chain"],
target["reference_ligand_resname"],
target["reference_ligand_chain"],
target_root / "target_inputs",
)
reference_ligand_sdf = target_root / "target_inputs" / "reference_ligand.sdf"
if not (args.resume and reference_ligand_sdf.exists()):
_obabel_convert(runner, "reference_ligand_to_sdf", ligand_pdb, reference_ligand_sdf, [], target_root)
engine = RDockEngine(
RDockRunConfig(
n_runs=args.n_runs,
jobs=args.jobs,
cpu_fraction=args.cpu_fraction,
timeout_seconds=3600,
)
)
prepared_root = target_root / "target_prepared"
if args.resume and (prepared_root / "target_config.yaml").exists():
from docking_pipeline.rdock import load_target_config
target_config = load_target_config(prepared_root / "target_config.yaml")
else:
target_config = engine.prepare_target(receptor, reference_ligand_sdf, prepared_root)
rows, all_ligands_sdf = _prepare_library(runner, Path(target["ligands_csv"]), args.ligands_per_target, target_root, args.resume)
all_block_map = _load_input_block_map(all_ligands_sdf)
model_rows = _build_model_rows(rows)
rdock_metrics, full_rows, full_seconds = _run_full_docking(engine, target_config, all_ligands_sdf, target_root, args.n_runs, args.jobs, args.resume)
_copy_full_aliases(target_root, target_config, full_rows, args.n_runs)
adaptive_rows, batch_rows, adaptive_docking_seconds, adaptive_model_seconds = _run_adaptive_benchmark(
engine,
target_config,
target_root,
all_block_map,
model_rows,
args.adaptive_budget,
args.batch_size,
args.n_runs,
args.jobs,
args.resume,
)
random_rows, random_seconds = _run_random_baseline(
engine,
target_config,
target_root,
all_block_map,
model_rows,
args.adaptive_budget,
args.n_runs,
args.jobs,
args.resume,
)
full_rank_map = {str(row["ligand_id"]): int(row["full_rank"]) for row in full_rows}
adaptive_ranked = _append_rank_metrics(adaptive_rows, full_rank_map, len(full_rows))
random_ranked = _append_rank_metrics(random_rows, full_rank_map, len(full_rows))
write_rows_csv(adaptive_ranked, target_root / "tables" / "adaptive_scores.csv")
write_rows_csv(random_ranked, target_root / "tables" / "random_baseline_scores.csv")
write_rows_csv(batch_rows, target_root / "tables" / "adaptive_batches.csv")
metrics = _build_metrics(
target["target_id"],
args.ligands_per_target,
full_rows,
adaptive_ranked,
random_ranked,
full_seconds,
adaptive_docking_seconds,
adaptive_model_seconds,
random_seconds,
)
_write_json(target_root / "metrics" / "adaptive_benchmark_metrics.json", metrics)
_write_json(target_root / "metrics" / "validation_metrics.json", metrics)
plots = []
plots.extend(plot_score_outputs(target_root / "tables" / "full_docking_scores.csv", target_root / "plots", title_prefix=f"{target['target_id']} full"))
plots.extend(
plot_adaptive_benchmark_outputs(
target_root / "tables" / "full_docking_scores.csv",
target_root / "tables" / "adaptive_scores.csv",
target_root / "tables" / "random_baseline_scores.csv",
target_root / "metrics" / "adaptive_benchmark_metrics.json",
target_root / "plots",
)
)
checks = _sanity_checks(target_root, args.ligands_per_target, args.adaptive_budget, args.n_runs)
_merge_commands(target_root)
manifest = {
"target_id": target["target_id"],
"engine": "adaptive-plus-rdock",
"target": target,
"rdock_metrics": rdock_metrics,
"adaptive_metrics": metrics,
"plots": plots,
"artifacts": {
"target_dir": str(target_root / "target"),
"ligands_sdf": str(target_root / "ligands" / "all_ligands.sdf"),
"all_poses_sdf": str(target_root / "poses" / "all_poses.sdf"),
"best_per_ligand_sdf": str(target_root / "poses" / "best_per_ligand.sdf"),
"best_ligand_all_poses_sdf": str(target_root / "poses" / f"best_ligand_{args.n_runs}.sdf"),
"full_scores": str(target_root / "tables" / "full_docking_scores.csv"),
"adaptive_scores": str(target_root / "tables" / "adaptive_scores.csv"),
"random_scores": str(target_root / "tables" / "random_baseline_scores.csv"),
"report": str(target_root / "report.md"),
"commands_log": str(target_root / "commands.log"),
},
"sanity_checks": checks,
"executables": {
"rbdock": probe_version(require_executable("rbdock")),
"rbcavity": probe_version(require_executable("rbcavity")),
"obabel": probe_version(require_executable("obabel")),
},
}
_write_json(target_root / "manifest.json", manifest)
notes = [
f"Prepared target from {target['pdb_path']}.",
f"Library source: {target['ligands_csv']}.",
f"Resume mode: {args.resume}.",
]
_write_target_report(target_root, target, metrics, plots, notes)
return {"target_id": target["target_id"], "status": "completed", "metrics": metrics, "run_dir": str(target_root), "sanity_checks": checks}
def _write_aggregate_report(out_root: Path, results: list[dict[str, Any]], args: argparse.Namespace) -> None:
lines = [
"# Adaptive + rDock Benchmark on 3 Targets",
"",
"## Command",
f"- targets: `{args.targets}`",
f"- ligands_per_target: `{args.ligands_per_target}`",
f"- n_runs: `{args.n_runs}`",
f"- adaptive_budget: `{args.adaptive_budget}`",
f"- batch_size: `{args.batch_size}`",
f"- jobs: `{args.jobs}`",
f"- cpu_fraction: `{args.cpu_fraction}`",
f"- resume: `{args.resume}`",
"",
"## Target Status",
]
for result in results:
metrics = result.get("metrics", {})
lines.extend(
[
f"- {result['target_id']}: `{result['status']}`",
f" best full SCORE `{metrics.get('best_full_SCORE')}`, adaptive `{metrics.get('best_adaptive_SCORE')}`, random `{metrics.get('best_random_SCORE')}`",
f" adaptive percentile `{metrics.get('adaptive_best_percentile_of_full')}`, random percentile `{metrics.get('random_best_percentile_of_full')}`",
f" full seconds `{metrics.get('full_docking_seconds')}`, adaptive docking `{metrics.get('adaptive_docking_seconds')}`, random `{metrics.get('random_docking_seconds')}`",
f" run dir `{result.get('run_dir')}`",
]
)
(out_root / "report.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
_write_json(out_root / "manifest.json", {"results": results})
def main() -> int:
parser = argparse.ArgumentParser(description="Production adaptive + rDock benchmark on 3 PDB targets with 1000 ligands each.")
parser.add_argument("--targets", required=True)
parser.add_argument("--ligands-per-target", type=int, default=1000)
parser.add_argument("--n-runs", type=int, default=50)
parser.add_argument("--adaptive-budget", type=int, default=250)
parser.add_argument("--batch-size", type=int, default=50)
parser.add_argument("--jobs", default="auto")
parser.add_argument("--cpu-fraction", type=float, default=0.85)
parser.add_argument("--out", required=True)
parser.add_argument("--resume", action="store_true")
parser.add_argument("--force", action="store_true")
args = parser.parse_args()
if args.ligands_per_target != 1000:
raise RDockPipelineError("This production benchmark is configured for 1000 ligands per target; do not reduce without explicit approval.")
out_root = Path(args.out)
out_root.mkdir(parents=True, exist_ok=True)
targets = _load_targets(Path(args.targets))
results = []
for target in targets:
results.append(run_target(target, args, out_root))
_write_aggregate_report(out_root, results, args)
print(json.dumps({"results": results, "out": str(out_root)}, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
|