| from __future__ import annotations |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
|
|
| def _engine_from_args(args: argparse.Namespace, validation: bool = False) -> RDockEngine: |
| from .rdock import RDockEngine, RDockRunConfig |
|
|
| return RDockEngine( |
| RDockRunConfig( |
| n_runs=int(getattr(args, "n_runs", 100 if validation else 50)), |
| jobs=getattr(args, "jobs", "auto"), |
| cpu_fraction=float(getattr(args, "cpu_fraction", 0.85)), |
| dock_prm=getattr(args, "dock_prm", None), |
| rbt_root=getattr(args, "rbt_root", None), |
| require_reporting_tools=validation, |
| ) |
| ) |
|
|
|
|
| def main() -> int: |
| parser = argparse.ArgumentParser(prog="python -m docking_pipeline", description="Strict rDock-first docking pipeline") |
| sub = parser.add_subparsers(dest="cmd", required=True) |
|
|
| p = sub.add_parser("prepare-target") |
| p.add_argument("--receptor", required=True) |
| p.add_argument("--ref-ligand", required=True) |
| p.add_argument("--out", required=True) |
| p.add_argument("--rbt-root") |
| p.add_argument("--dock-prm") |
|
|
| p = sub.add_parser("dock-rdock") |
| p.add_argument("--target-config", required=True) |
| p.add_argument("--ligands", required=True) |
| p.add_argument("--out", required=True) |
| p.add_argument("--n-runs", type=int, default=50) |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--dock-prm") |
| p.add_argument("--rbt-root") |
| p.add_argument("--dry-run", action="store_true") |
| p.add_argument("--plan-only", action="store_true") |
|
|
| p = sub.add_parser("validate-rdock") |
| p.add_argument("--set", choices=["astex", "dud", "rna"], required=True) |
| p.add_argument("--system") |
| p.add_argument("--system-list") |
| p.add_argument("--max-systems", type=int) |
| p.add_argument("--list-systems", action="store_true") |
| p.add_argument("--data-dir", required=True) |
| p.add_argument("--out", default="results/rdock_validation") |
| p.add_argument("--n-runs", type=int, default=100) |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--download-url") |
| p.add_argument("--download-if-missing", action="store_true") |
| p.add_argument("--keep-work", action="store_true") |
| p.add_argument("--force", action="store_true") |
| p.add_argument("--dry-run", action="store_true") |
| p.add_argument("--plan-only", action="store_true") |
|
|
| p = sub.add_parser("run-adaptive") |
| p.add_argument("--target-config", required=True) |
| p.add_argument("--ligands", required=True, help="CSV with ligand_id,smiles and optional model_score") |
| p.add_argument("--out", required=True) |
| p.add_argument("--budget", type=int, required=True) |
| p.add_argument("--batch-size", type=int, default=16) |
| p.add_argument("--n-runs", type=int, default=50) |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--dock-prm") |
| p.add_argument("--rbt-root") |
| p.add_argument("--dry-run", action="store_true") |
| p.add_argument("--plan-only", action="store_true") |
|
|
| p = sub.add_parser("benchmark-adaptive") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--strategy", default="multifidelity_adaptive_rdock") |
| p.add_argument("--fidelity-levels", default="5,10,15,30,50") |
| p.add_argument("--cost-budget-runs", type=int, required=True) |
| p.add_argument("--adaptive-budget-ligands", type=int) |
| p.add_argument("--promotion-fraction", type=float, default=0.5) |
| p.add_argument("--min-per-cluster", type=int, default=1) |
| p.add_argument("--max-per-cluster", type=int, default=50) |
| p.add_argument("--outlier-intra-z-threshold", type=float, default=3.0) |
| p.add_argument("--score-component-filter", default="warn") |
| p.add_argument("--final-fidelity-only-hits", default="true") |
| p.add_argument("--reference-mode", default="full") |
| p.add_argument("--evaluation-pool-mode", default="same_pool") |
| p.add_argument("--reference-sample-size", type=int, default=5000) |
| p.add_argument("--reference-sample-seed", type=int, default=42) |
| p.add_argument("--balanced-baselines", default="true") |
| p.add_argument("--posthoc-score-selected-hits", default="false") |
| p.add_argument("--posthoc-final-runs", type=int, default=50) |
| p.add_argument("--force-resume-stale", action="store_true") |
| p.add_argument("--outlier-policy", default="downrank") |
| p.add_argument("--intra-z-threshold", type=float, default=4.0) |
| p.add_argument("--score-z-threshold", type=float, default=5.0) |
| p.add_argument("--max-intra-fraction", type=float, default=0.75) |
| p.add_argument("--max-intra-fraction-soft", type=float, default=0.75) |
| p.add_argument("--max-intra-fraction-hard", type=float, default=0.9) |
| p.add_argument("--exploration-fraction", type=float, default=0.35) |
| p.add_argument("--diversity-weight", type=float, default=0.75) |
| p.add_argument("--uncertainty-weight", type=float, default=0.35) |
| p.add_argument("--outlier-risk-weight", type=float, default=2.0) |
| p.add_argument("--cluster-min-coverage", type=int, default=1) |
| p.add_argument("--use-reference-features", default="false") |
| p.add_argument("--production-reference-free", default="false") |
| p.add_argument("--calibration-size", type=int, default=0) |
| p.add_argument("--calibration-fraction", type=float, default=0.2) |
| p.add_argument("--min-clusters-covered", type=int, default=8) |
| p.add_argument("--calibration-random-fraction", type=float, default=0.15) |
| p.add_argument("--calibration-diversity-weight", type=float, default=1.0) |
| p.add_argument("--fidelity-validation-size", type=int, default=50) |
| p.add_argument("--fidelity-validation-policy", default="cluster_stratified") |
| p.add_argument("--promotion-policy", default="conservative") |
| p.add_argument("--min-final-ligands", type=int, default=20) |
| p.add_argument("--min-promotion-per-level", type=int, default=8) |
| p.add_argument("--promotion-fraction-by-level", default="") |
| p.add_argument("--triage-retain-fraction", type=float, default=0.05) |
| p.add_argument("--triage-target-recall", type=float, default=0.98) |
| p.add_argument("--triage-min-survivors", type=int, default=50) |
| p.add_argument("--triage-max-survivors", type=int, default=0) |
| p.add_argument("--cluster-min-survivors", type=int, default=1) |
| p.add_argument("--cluster-max-survivors", type=int, default=0) |
| p.add_argument("--rescue-fraction", type=float, default=0.05) |
| p.add_argument("--rare-cluster-rescue", type=int, default=20) |
| p.add_argument("--uncertainty-rescue", type=int, default=20) |
| p.add_argument("--allow-low-confidence-triage", action="store_true") |
| p.add_argument("--top-good-fraction", type=float, default=0.1) |
| p.add_argument("--minimum-training-ligands", type=int, default=50) |
| p.add_argument("--triage-controller", default="auto_recall", choices=["auto_recall", "fixed"]) |
| p.add_argument("--max-retain-fraction-before-not-useful", type=float, default=0.5) |
| p.add_argument("--classifier-top-percentile", type=float, default=0.1) |
| p.add_argument("--triage-model", default="classifier") |
| p.add_argument("--classifier-threshold-mode", default="recall_target") |
| p.add_argument("--classifier-min-positives", type=int, default=10) |
| p.add_argument("--classifier-holdout-fraction", type=float, default=0.25) |
| p.add_argument("--classifier-fallback", default="cluster_only") |
| p.add_argument("--model-fallback-if-worse", default="none") |
| p.add_argument("--survivor-combination-policy", default="model_only") |
| p.add_argument("--final-survivor-enumerate-variants", default="false") |
| p.add_argument("--variant-stage", default="none") |
| p.add_argument("--enumerate-stereoisomers", default="none") |
| p.add_argument("--max-stereoisomers-per-parent", type=int, default=2) |
| p.add_argument("--enumerate-tautomers", default="none") |
| p.add_argument("--max-tautomers-per-parent", type=int, default=1) |
| p.add_argument("--enumerate-protonation", default="none") |
| p.add_argument("--ph", type=float, default=7.4) |
| p.add_argument("--max-protomer-states-per-parent", type=int, default=1) |
| p.add_argument("--max-conformers-per-variant", type=int, default=1) |
| p.add_argument("--max-total-variants-per-parent", type=int, default=1) |
| p.add_argument("--posthoc-top-parents", type=int, default=100) |
| p.add_argument("--posthoc-max-total-variants-per-parent", type=int, default=20) |
| p.add_argument("--variant-fairness-policy", default="cap") |
| p.add_argument("--allow-no-rdkit-parent-only", action="store_true") |
| p.add_argument("--chunk-size", type=int, default=50) |
| p.add_argument("--resume", action="store_true") |
| p.add_argument("--checkpoint-every", type=int, default=1) |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--out", required=True) |
| p.add_argument("--dry-run", action="store_true") |
| p.add_argument("--plan-only", action="store_true") |
|
|
| p = sub.add_parser("screen-reference-free") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--strategy", default="reference_free_triage_bandit_v1") |
| p.add_argument("--fidelity-levels", default="5,10,15,30,50") |
| p.add_argument("--cost-budget-runs", type=int, required=True) |
| p.add_argument("--triage-retain-fraction", type=float, default=0.02) |
| p.add_argument("--triage-target-recall", type=float, default=0.98) |
| p.add_argument("--calibration-size", type=int, default=1000) |
| p.add_argument("--calibration-fraction", type=float, default=0.2) |
| p.add_argument("--min-clusters-covered", type=int, default=8) |
| p.add_argument("--calibration-random-fraction", type=float, default=0.15) |
| p.add_argument("--calibration-diversity-weight", type=float, default=1.0) |
| p.add_argument("--fidelity-validation-size", type=int, default=200) |
| p.add_argument("--fidelity-validation-policy", default="cluster_stratified") |
| p.add_argument("--promotion-policy", default="conservative") |
| p.add_argument("--min-final-ligands", type=int, default=20) |
| p.add_argument("--min-promotion-per-level", type=int, default=8) |
| p.add_argument("--promotion-fraction-by-level", default="") |
| p.add_argument("--cluster-min-survivors", type=int, default=1) |
| p.add_argument("--cluster-max-survivors", type=int, default=0) |
| p.add_argument("--rescue-fraction", type=float, default=0.05) |
| p.add_argument("--rare-cluster-rescue", type=int, default=20) |
| p.add_argument("--uncertainty-rescue", type=int, default=20) |
| p.add_argument("--allow-low-confidence-triage", action="store_true") |
| p.add_argument("--top-good-fraction", type=float, default=0.1) |
| p.add_argument("--minimum-training-ligands", type=int, default=50) |
| p.add_argument("--triage-controller", default="auto_recall", choices=["auto_recall", "fixed"]) |
| p.add_argument("--max-retain-fraction-before-not-useful", type=float, default=0.5) |
| p.add_argument("--classifier-top-percentile", type=float, default=0.1) |
| p.add_argument("--triage-model", default="classifier") |
| p.add_argument("--classifier-threshold-mode", default="recall_target") |
| p.add_argument("--classifier-min-positives", type=int, default=10) |
| p.add_argument("--classifier-holdout-fraction", type=float, default=0.25) |
| p.add_argument("--classifier-fallback", default="cluster_only") |
| p.add_argument("--model-fallback-if-worse", default="none") |
| p.add_argument("--survivor-combination-policy", default="model_only") |
| p.add_argument("--final-survivor-enumerate-variants", default="false") |
| p.add_argument("--variant-stage", default="none") |
| p.add_argument("--enumerate-stereoisomers", default="none") |
| p.add_argument("--max-stereoisomers-per-parent", type=int, default=2) |
| p.add_argument("--enumerate-tautomers", default="none") |
| p.add_argument("--max-tautomers-per-parent", type=int, default=1) |
| p.add_argument("--enumerate-protonation", default="none") |
| p.add_argument("--ph", type=float, default=7.4) |
| p.add_argument("--max-protomer-states-per-parent", type=int, default=1) |
| p.add_argument("--max-conformers-per-variant", type=int, default=1) |
| p.add_argument("--max-total-variants-per-parent", type=int, default=1) |
| p.add_argument("--posthoc-top-parents", type=int, default=100) |
| p.add_argument("--posthoc-max-total-variants-per-parent", type=int, default=20) |
| p.add_argument("--variant-fairness-policy", default="cap") |
| p.add_argument("--allow-no-rdkit-parent-only", action="store_true") |
| p.add_argument("--outlier-policy", default="downrank") |
| p.add_argument("--intra-z-threshold", type=float, default=4.0) |
| p.add_argument("--score-z-threshold", type=float, default=5.0) |
| p.add_argument("--max-intra-fraction", type=float, default=0.75) |
| p.add_argument("--max-intra-fraction-soft", type=float, default=0.75) |
| p.add_argument("--max-intra-fraction-hard", type=float, default=0.9) |
| p.add_argument("--exploration-fraction", type=float, default=0.35) |
| p.add_argument("--diversity-weight", type=float, default=0.75) |
| p.add_argument("--uncertainty-weight", type=float, default=0.35) |
| p.add_argument("--outlier-risk-weight", type=float, default=2.0) |
| p.add_argument("--cluster-min-coverage", type=int, default=1) |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--chunk-size", type=int, default=50) |
| p.add_argument("--rdock-timeout-seconds", type=int, default=3600) |
| p.add_argument("--resume", action="store_true") |
| p.add_argument("--force-resume-stale", action="store_true") |
| p.add_argument("--checkpoint-every", type=int, default=1) |
| p.add_argument("--out", required=True) |
| p.add_argument("--dry-run", action="store_true") |
| p.add_argument("--plan-only", action="store_true") |
|
|
| p = sub.add_parser("screen-production-adaptive") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--fidelity-levels", default="5,10,15,30,50") |
| p.add_argument("--cost-budget-runs", type=int, required=True) |
| p.add_argument("--calibration-size", type=int, default=12000) |
| p.add_argument("--classifier-top-percentile", type=float, default=0.05) |
| p.add_argument("--triage-target-recall", type=float, default=0.95) |
| p.add_argument("--promotion-policy", default="conservative") |
| p.add_argument("--min-final-ligands", type=int, default=200) |
| p.add_argument("--min-promotion-per-level", type=int, default=8) |
| p.add_argument("--triage-retain-fraction", type=float, default=0.05) |
| p.add_argument("--cluster-min-survivors", type=int, default=1) |
| p.add_argument("--cluster-max-survivors", type=int, default=0) |
| p.add_argument("--rescue-fraction", type=float, default=0.05) |
| p.add_argument("--rare-cluster-rescue", type=int, default=20) |
| p.add_argument("--uncertainty-rescue", type=int, default=20) |
| p.add_argument("--classifier-weight", type=float, default=1.0) |
| p.add_argument("--regressor-weight", type=float, default=0.2) |
| p.add_argument("--regressor-contribution-mode", default="gate") |
| p.add_argument("--diversity-weight", type=float, default=0.75) |
| p.add_argument("--uncertainty-weight", type=float, default=0.35) |
| p.add_argument("--outlier-risk-weight", type=float, default=2.0) |
| p.add_argument("--adaptive-policy", default="cluster_bandit") |
| p.add_argument("--fixed-score-regressor-target", default="component_sane_affinity_like") |
| p.add_argument("--regressor-model-type", default="extra_trees") |
| p.add_argument("--model-validation-split", default="cluster", choices=["random", "cluster"]) |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--chunk-size", type=int, default=10) |
| p.add_argument("--rdock-timeout-seconds", type=int, default=1800) |
| p.add_argument("--resume", action="store_true") |
| p.add_argument("--force-resume-stale", action="store_true") |
| p.add_argument("--checkpoint-every", type=int, default=1) |
| p.add_argument("--out", required=True) |
| p.add_argument("--dry-run", action="store_true") |
| p.add_argument("--plan-only", action="store_true") |
|
|
| p = sub.add_parser("validate-fidelity") |
| p.add_argument("--run-dir", required=True) |
|
|
| p = sub.add_parser("benchmark-triage-repeated") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--reference-mode", default="full", choices=["full", "sampled"]) |
| p.add_argument("--reference-sample-size", type=int, default=500) |
| p.add_argument("--reference-sample-seed", type=int, default=42) |
| p.add_argument("--strategy", default="reference_free_triage_bandit_v1") |
| p.add_argument("--baselines", default="cluster_only_triage,cheap_descriptor_filter_only,diverse_random_cost_balanced") |
| p.add_argument("--triage-target-recall", type=float, default=0.98) |
| p.add_argument("--triage-retain-fraction", type=float, default=0.02) |
| p.add_argument("--calibration-size", type=int, default=300) |
| p.add_argument("--fidelity-validation-size", type=int, default=100) |
| p.add_argument("--triage-model", default="classifier") |
| p.add_argument("--classifier-top-percentile", type=float, default=0.05) |
| p.add_argument("--classifier-threshold-mode", default="recall_target") |
| p.add_argument("--classifier-min-positives", type=int, default=10) |
| p.add_argument("--classifier-holdout-fraction", type=float, default=0.25) |
| p.add_argument("--classifier-fallback", default="cluster_only") |
| p.add_argument("--triage-controller", default="auto_recall") |
| p.add_argument("--model-fallback-if-worse", default="none") |
| p.add_argument("--survivor-combination-policy", default="model_only") |
| p.add_argument("--seeds", default="1,2,3") |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--chunk-size", type=int, default=20) |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--out", required=True) |
| p.add_argument("--force", action="store_true") |
|
|
| p = sub.add_parser("benchmark-triage-strategies") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--reference-mode", default="sampled", choices=["full", "sampled", "none"]) |
| p.add_argument("--reference-sample-size", type=int, default=300) |
| p.add_argument("--reference-sample-seed", type=int, default=42) |
| p.add_argument("--strategies", required=True) |
| p.add_argument("--triage-target-recall", type=float, default=0.98) |
| p.add_argument("--triage-retain-fraction", type=float, default=0.02) |
| p.add_argument("--calibration-size", type=int, default=300) |
| p.add_argument("--fidelity-validation-size", type=int, default=100) |
| p.add_argument("--cost-budget-runs", type=int, default=3000) |
| p.add_argument("--fidelity-levels", default="5,10,15,30,50") |
| p.add_argument("--triage-model", default="classifier") |
| p.add_argument("--classifier-top-percentile", type=float, default=0.05) |
| p.add_argument("--classifier-threshold-mode", default="recall_target") |
| p.add_argument("--classifier-min-positives", type=int, default=10) |
| p.add_argument("--classifier-holdout-fraction", type=float, default=0.25) |
| p.add_argument("--classifier-fallback", default="cluster_only") |
| p.add_argument("--model-fallback-if-worse", default="union_with_cluster_only") |
| p.add_argument("--survivor-combination-policy", default="union") |
| p.add_argument("--adaptive-policy", default="hybrid_rank") |
| p.add_argument("--adaptive-policies", default="") |
| p.add_argument("--regressor-contribution-mode", default="linear") |
| p.add_argument("--classifier-weight", type=float, default=1.0) |
| p.add_argument("--regressor-weight", type=float, default=0.35) |
| p.add_argument("--cluster-quality-weight", type=float, default=0.5) |
| p.add_argument("--fixed-score-regressor-name", default="fixed_score_regressor_v1") |
| p.add_argument("--fixed-score-regressor-target", default="component_sane_affinity_like") |
| p.add_argument("--regressor-model-type", default="extra_trees") |
| p.add_argument("--model-validation-split", default="cluster", choices=["random", "cluster"]) |
| p.add_argument("--promotion-policy", default="balanced") |
| p.add_argument("--exploration-fraction", type=float, default=0.35) |
| p.add_argument("--diversity-weight", type=float, default=0.75) |
| p.add_argument("--uncertainty-weight", type=float, default=0.35) |
| p.add_argument("--outlier-risk-weight", type=float, default=2.0) |
| p.add_argument("--min-per-cluster", type=int, default=1) |
| p.add_argument("--max-per-cluster", type=int, default=50) |
| p.add_argument("--triage-max-survivors", type=int, default=0) |
| p.add_argument("--cluster-quota", type=int, default=0) |
| p.add_argument("--promotion-temperature", type=float, default=1.0) |
| p.add_argument("--diagnostics-level", default="standard", choices=["minimal", "standard", "full"]) |
| p.add_argument("--classifier-gate-fraction", type=float, default=0.15) |
| p.add_argument("--classifier-max-gate-fraction", type=float, default=0.2) |
| p.add_argument("--seeds", default="1,2,3") |
| p.add_argument("--jobs", default="10") |
| p.add_argument("--chunk-size", type=int, default=20) |
| p.add_argument("--rdock-timeout-seconds", type=int, default=14400) |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--out", required=True) |
| p.add_argument("--force", action="store_true") |
| p.add_argument("--resume", action="store_true") |
|
|
| p = sub.add_parser("benchmark-regressor-ablation") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--reference-mode", default="sampled", choices=["sampled", "none"]) |
| p.add_argument("--reference-sample-size", type=int, default=1000) |
| p.add_argument("--reference-sample-seed", type=int, default=42) |
| p.add_argument("--fidelity-levels", default="5,10,15,30,50") |
| p.add_argument("--cost-budget-runs", type=int, default=3000) |
| p.add_argument("--calibration-size", type=int, default=500) |
| p.add_argument("--fidelity-validation-size", type=int, default=150) |
| p.add_argument("--triage-target-recall", type=float, default=0.98) |
| p.add_argument("--triage-retain-fraction", type=float, default=0.02) |
| p.add_argument("--triage-model", default="classifier") |
| p.add_argument("--classifier-top-percentile", type=float, default=0.05) |
| p.add_argument("--classifier-threshold-mode", default="recall_target") |
| p.add_argument("--classifier-min-positives", type=int, default=10) |
| p.add_argument("--classifier-holdout-fraction", type=float, default=0.25) |
| p.add_argument("--classifier-fallback", default="cluster_only") |
| p.add_argument("--adaptive-policy", default="classifier_only") |
| p.add_argument("--regressor-contribution-mode", default="gate") |
| p.add_argument("--classifier-weight", type=float, default=1.0) |
| p.add_argument("--regressor-weight", type=float, default=0.35) |
| p.add_argument("--cluster-quality-weight", type=float, default=0.5) |
| p.add_argument("--uncertainty-weight", type=float, default=0.35) |
| p.add_argument("--diversity-weight", type=float, default=0.75) |
| p.add_argument("--outlier-risk-weight", type=float, default=2.0) |
| p.add_argument("--fixed-score-regressor-name", default="fixed_score_regressor_v1") |
| p.add_argument("--fixed-score-regressor-target", default="component_sane_affinity_like") |
| p.add_argument("--regressor-model-type", default="extra_trees") |
| p.add_argument("--model-validation-split", default="cluster", choices=["random", "cluster"]) |
| p.add_argument("--diagnostics-level", default="standard", choices=["minimal", "standard", "full"]) |
| p.add_argument("--classifier-gate-fraction", type=float, default=0.15) |
| p.add_argument("--classifier-max-gate-fraction", type=float, default=0.2) |
| p.add_argument("--variants", default="") |
| p.add_argument("--seeds", default="1,2,3") |
| p.add_argument("--jobs", default="10") |
| p.add_argument("--chunk-size", type=int, default=20) |
| p.add_argument("--rdock-timeout-seconds", type=int, default=14400) |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--out", required=True) |
| p.add_argument("--force", action="store_true") |
| p.add_argument("--resume", action="store_true") |
|
|
| p = sub.add_parser("benchmark-comparable-adaptive") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--evaluation-universe-size", type=int, required=True) |
| p.add_argument("--evaluation-universe-seed", type=int, default=42) |
| p.add_argument("--reference-mode", default="full", choices=["full", "sampled", "none"]) |
| p.add_argument("--reference-sample-size", type=int, default=0) |
| p.add_argument("--reference-sample-seed", type=int, default=42) |
| p.add_argument("--strategies", required=True) |
| p.add_argument("--cost-budget-runs", type=int, required=True) |
| p.add_argument("--fidelity-levels", default="5,10,15,30,50") |
| p.add_argument("--promotion-policy", default="quota_ladder") |
| p.add_argument("--min-final-ligands", type=int, default=200) |
| p.add_argument("--triage-target-recall", type=float, default=0.95) |
| p.add_argument("--triage-retain-fraction", type=float, default=0.05) |
| p.add_argument("--calibration-size", type=int, default=1000) |
| p.add_argument("--fidelity-validation-size", type=int, default=100) |
| p.add_argument("--adaptive-policy", default="cluster_bandit") |
| p.add_argument("--regressor-contribution-mode", default="gate") |
| p.add_argument("--classifier-weight", type=float, default=1.0) |
| p.add_argument("--regressor-weight", type=float, default=0.2) |
| p.add_argument("--cluster-quality-weight", type=float, default=0.4) |
| p.add_argument("--diversity-weight", type=float, default=0.75) |
| p.add_argument("--uncertainty-weight", type=float, default=0.2) |
| p.add_argument("--outlier-risk-weight", type=float, default=2.0) |
| p.add_argument("--classifier-top-percentile", type=float, default=0.05) |
| p.add_argument("--classifier-threshold-mode", default="recall_target") |
| p.add_argument("--classifier-min-positives", type=int, default=10) |
| p.add_argument("--classifier-holdout-fraction", type=float, default=0.25) |
| p.add_argument("--classifier-fallback", default="cluster_only") |
| p.add_argument("--model-fallback-if-worse", default="none") |
| p.add_argument("--survivor-combination-policy", default="model_only") |
| p.add_argument("--fixed-score-regressor-name", default="fixed_score_regressor_v1") |
| p.add_argument("--fixed-score-regressor-target", default="component_sane_affinity_like") |
| p.add_argument("--regressor-model-type", default="extra_trees") |
| p.add_argument("--model-validation-split", default="cluster", choices=["random", "cluster"]) |
| p.add_argument("--diagnostics-level", default="standard", choices=["minimal", "standard", "full"]) |
| p.add_argument("--classifier-gate-fraction", type=float, default=0.15) |
| p.add_argument("--classifier-max-gate-fraction", type=float, default=0.2) |
| p.add_argument("--seeds", default="1,2,3") |
| p.add_argument("--jobs", default="auto") |
| p.add_argument("--chunk-size", type=int, default=10) |
| p.add_argument("--rdock-timeout-seconds", type=int, default=1800) |
| p.add_argument("--cpu-fraction", type=float, default=0.85) |
| p.add_argument("--out", required=True) |
| p.add_argument("--resume", action="store_true") |
| p.add_argument("--force", action="store_true") |
|
|
| p = sub.add_parser("validate-dataset") |
| p.add_argument("--dataset-dir", required=True) |
| p.add_argument("--check-rdock-tools", action="store_true") |
|
|
| p = sub.add_parser("audit-benchmark") |
| p.add_argument("--run-dir", required=True) |
|
|
| p = sub.add_parser("validate-benchmark-model") |
| p.add_argument("--run-dir", required=True) |
|
|
| p = sub.add_parser("audit-strategy-benchmark") |
| p.add_argument("--run-dir", required=True) |
|
|
| p = sub.add_parser("audit-regressor-ablation") |
| p.add_argument("--run-dir", required=True) |
|
|
| p = sub.add_parser("clean-run-cache") |
| p.add_argument("--run-dir", required=True) |
|
|
| args = parser.parse_args() |
| if args.cmd == "prepare-target": |
| engine = _engine_from_args(args) |
| target = engine.prepare_target(args.receptor, args.ref_ligand, args.out) |
| print(json.dumps({"target_config": str(Path(args.out) / "target_config.yaml"), "target": target.__dict__}, indent=2)) |
| elif args.cmd == "dock-rdock": |
| from .rdock import load_target_config |
|
|
| engine = _engine_from_args(args) |
| if args.dry_run or args.plan_only: |
| plan = { |
| "target_config": args.target_config, |
| "ligands": args.ligands, |
| "out": args.out, |
| "n_runs": args.n_runs, |
| "jobs": args.jobs, |
| "cpu_fraction": args.cpu_fraction, |
| "commands": ["rbcavity was already run during prepare-target", "rbdock chunks will be launched by --jobs"], |
| } |
| Path(args.out).mkdir(parents=True, exist_ok=True) |
| (Path(args.out) / "dock_plan.json").write_text(json.dumps(plan, indent=2), encoding="utf-8") |
| print(json.dumps(plan, indent=2)) |
| return 0 |
| artifacts = engine.dock_sdf(load_target_config(args.target_config), args.ligands, args.out, n_runs=args.n_runs, jobs=args.jobs) |
| print(json.dumps(artifacts.__dict__, indent=2)) |
| elif args.cmd == "validate-rdock": |
| from .validation import discover_validation_systems, ensure_validation_data, validate_many |
|
|
| if args.set == "rna": |
| raise SystemExit("RNA rDock validation is not implemented yet; supported sets are astex and dud.") |
| data_root = ensure_validation_data(args.data_dir, args.set, args.download_url, args.download_if_missing, force=args.force) |
| if args.list_systems: |
| systems = discover_validation_systems(data_root, args.set) |
| print(json.dumps([s.__dict__ for s in systems], indent=2)) |
| return 0 |
| result = validate_many( |
| args.set, |
| data_root, |
| args.out, |
| system=args.system, |
| system_list=args.system_list, |
| max_systems=args.max_systems, |
| n_runs=args.n_runs, |
| jobs=args.jobs, |
| cpu_fraction=args.cpu_fraction, |
| download_url=args.download_url, |
| download_if_missing=args.download_if_missing, |
| force=args.force, |
| dry_run=args.dry_run, |
| plan_only=args.plan_only, |
| ) |
| print(json.dumps(result, indent=2)) |
| metrics = result.get("metrics", {}) if isinstance(result, dict) else {} |
| if metrics.get("failures"): |
| return 1 |
| elif args.cmd == "run-adaptive": |
| from .adaptive import run_adaptive |
| from .rdock import load_target_config |
|
|
| engine = _engine_from_args(args) |
| if args.dry_run or args.plan_only: |
| plan = { |
| "target_config": args.target_config, |
| "ligands": args.ligands, |
| "out": args.out, |
| "budget": args.budget, |
| "batch_size": args.batch_size, |
| "n_runs": args.n_runs, |
| "jobs": args.jobs, |
| } |
| Path(args.out).mkdir(parents=True, exist_ok=True) |
| (Path(args.out) / "adaptive_plan.json").write_text(json.dumps(plan, indent=2), encoding="utf-8") |
| print(json.dumps(plan, indent=2)) |
| return 0 |
| run_dir = run_adaptive(load_target_config(args.target_config), args.ligands, args.out, args.budget, args.batch_size, engine, n_runs=args.n_runs) |
| print(json.dumps({"run_dir": str(run_dir)}, indent=2)) |
| elif args.cmd == "benchmark-adaptive": |
| from .benchmark_adaptive import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "screen-reference-free": |
| from .benchmark_adaptive import run_reference_free_from_args |
|
|
| print(json.dumps(run_reference_free_from_args(args), indent=2)) |
| elif args.cmd == "screen-production-adaptive": |
| from .benchmark_adaptive import run_production_from_args |
|
|
| print(json.dumps(run_production_from_args(args), indent=2)) |
| elif args.cmd == "validate-dataset": |
| from .dataset import validate_dataset_dir |
|
|
| print(json.dumps(validate_dataset_dir(args.dataset_dir, check_rdock_tools=args.check_rdock_tools), indent=2)) |
| elif args.cmd == "audit-benchmark": |
| from .audit_benchmark import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "validate-fidelity": |
| from .validate_fidelity import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "benchmark-triage-repeated": |
| from .benchmark_triage_repeated import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "benchmark-triage-strategies": |
| from .benchmark_triage_strategies import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "benchmark-regressor-ablation": |
| from .benchmark_regressor_ablation import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "benchmark-comparable-adaptive": |
| from .benchmark_comparable_adaptive import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "validate-benchmark-model": |
| from .validate_benchmark_model import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "audit-strategy-benchmark": |
| from .audit_strategy_benchmark import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "audit-regressor-ablation": |
| from .audit_regressor_ablation import run_from_args |
|
|
| print(json.dumps(run_from_args(args), indent=2)) |
| elif args.cmd == "clean-run-cache": |
| from .benchmark_adaptive import clean_run_cache |
|
|
| print(json.dumps(clean_run_cache(args.run_dir), indent=2)) |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| try: |
| raise SystemExit(main()) |
| except Exception as exc: |
| from .provenance import RDockPipelineError |
|
|
| if isinstance(exc, RDockPipelineError): |
| raise SystemExit(str(exc)) |
| raise |
|
|
