Redocking-RMSD validation fails 3/3: pipeline-quality issue

§12.4 de-biased validation (scripts/dock_validate.py).
Redock each co-crystal ligand into its own structure, RMSD vs crystal:
- voxelotor->Hb: NA (covalent binder, out of scope §12.7)
- mitapivat->PKR: 8.2A (allosteric, cofactors stripped)
- vorinostat->HDAC2 (4LXZ, zinc kept): 7.9A -- a CLASSICAL target that
  should have worked

The clean target also failing => systematic pipeline-quality problem,
not target choice. Cheap Vina + open-babel prep gives scores but doesn't
reproduce known geometry, so affinities aren't trustworthy. Ligand
efficiency over-corrects (ranks tiny hydroxyurea best). Fix needs
production prep (Meeko/AutoDockTools prepare_receptor + reduce) and an
in-place RMSD metric. Consistent with the project theme: the quick
version of every method runs but fails honest validation.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/structure_binding_notes.md

@@ -51,15 +51,41 @@ problem** — raw scores carry a systematic bias (size here, broadness there) th
 signal. voxelotor *does* dock to Hb (−8.1, a real score); the cross-target test just isn't the
 right validation.
 
+## Step 2 — redocking-RMSD validation FAILS across the board (2026-06-24)
+
+Redocked each co-crystal ligand into its own structure (`scripts/dock_validate.py`); RMSD vs
+crystal pose via obrms:
+
+| redock | RMSD | note |
+|---|---|---|
+| voxelotor → Hb (5E83) | NA | covalent binder (Schiff base, αVal1) — out of scope §12.7 |
+| mitapivat → PKR (8XFD) | 8.2 Å | allosteric, cofactor (FBP/Mg) stripped |
+| **vorinostat → HDAC2 (4LXZ, Zn kept)** | **7.9 Å** | classical non-covalent target — should have worked |
+
+**The clean target also failing means this is a systematic PIPELINE-QUALITY problem, not target
+choice.** The cheap Vina + open-babel setup produces scores but does not reproduce known binding
+geometry, so its affinities are not yet trustworthy. Ligand efficiency (affinity / heavy atoms)
+also doesn't fix it — it over-corrects, ranking tiny hydroxyurea (−0.78) "best".
+
+Likely causes (in priority order):
+1. **Low-quality receptor prep** — open-babel `-xr` is not production docking prep. Need
+   AutoDockTools `prepare_receptor` or **Meeko** + `reduce`/pdb2pqr for protonation, charges, and
+   proper AutoDock atom typing.
+2. **Ligand prep** — should use Meeko (correct rotatable bonds / typing), not bare obabel `--gen3d`.
+3. **RMSD metric** — obrms superimposes before RMSD; redocking validation wants symmetry-corrected
+   RMSD **in place** (receptor frame). Worth confirming with an in-place metric.
+
+**Honest takeaway:** consistent with the whole project — the *quick* version of each method runs
+but doesn't survive honest validation. Credible structure-based docking needs production prep
+tooling (Meeko/ADFR), which is the real next investment for this track.
+
 ## Next steps
-- [ ] **Redocking-RMSD validation** (the gold-standard positive control): redock the crystal ligand
+- [ ] Install **Meeko** (+ reduce / pdb2pqr) and redo receptor+ligand prep; re-run redocking RMSD.
-  5L7/WV2 into its own structure, compute pose RMSD vs crystal. <2 Å = geometry validated. This
+- [ ] Fix the RMSD metric (in-place, symmetry-corrected) to rule out a measurement artifact.
-  tests pose accuracy, which size bias doesn't corrupt.
+- [ ] Only once redocking validates (<2 Å) are affinity scores trustworthy — then cross-dock /
-- [ ] **Ligand-efficiency normalization** (affinity / heavy-atom count) to de-bias the size effect,
+  screen the library and revisit ligand-efficiency / pose-based scoring.
-  the docking counterpart of the connectivity calibration work.
+- [ ] Later: AF3-class co-folding (Boltz-2/DiffDock via PyTorch-MPS — 24 GB ceiling) and the §12.9
-- [ ] Expand the ligand library (full ChEMBL/LINCS) for retrieval reach.
+  generative beacon.
-- [ ] Only then: AF3-class co-folding (Boltz-2/DiffDock via PyTorch-MPS — note 24 GB ceiling) vs the
-  docking baseline; and §12.9 generative beacon.
 
 > **Hardware note:** this machine is **24 GB** unified memory (not the 96 GB PLAN §2 assumed),
 > which caps local AF3-class model inference. Classical docking (above) is unaffected.

scripts/dock_validate.py

@@ -0,0 +1,93 @@
+"""Structure-binding §12.4: redocking-RMSD validation + ligand-efficiency normalization.
+
+Two de-biased checks of the docking baseline:
+  A. REDOCKING RMSD — redock each co-crystal ligand into its own structure and measure pose RMSD
+     vs the crystal pose (open-babel obrms, symmetry-aware). <2 A = geometry validated. This is
+     the gold-standard positive control and is immune to the molecular-size bias that made the
+     cross-target affinity test inconclusive.
+  B. LIGAND EFFICIENCY — affinity / heavy-atom count, to de-bias the size effect in the raw scores.
+"""
+
+from __future__ import annotations
+
+import re
+import subprocess
+from pathlib import Path
+
+from rdkit import Chem, RDLogger
+
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from scripts.dock_positive_controls import (  # noqa: E402
+    STRUCT, VINA, WORK, TARGETS, pubchem_smiles, prep_ligand, prep_receptor_and_box,
+)
+
+RDLogger.DisableLog("rdApp.*")
+XTAL_LIG = {"hemoglobin": ("5E83", "5L7", "voxelotor"), "PKR": ("8XFD", "WV2", "mitapivat")}
+
+
+def extract_crystal_ligand(pdb: str, resname: str) -> Path:
+    lines, chosen = [], None
+    for ln in (STRUCT / f"{pdb}.pdb").read_text().splitlines():
+        if ln.startswith("HETATM") and ln[17:20].strip() == resname:
+            key = (ln[21], ln[22:26])
+            chosen = chosen or key
+            if key == chosen:
+                lines.append(ln)
+    out = WORK / f"xtal_{resname}.pdb"
+    out.write_text("\n".join(lines) + "\nEND\n")
+    return out
+
+
+def dock_pose(rec, lig, center, size) -> Path | None:
+    pose = WORK / "redock_out.pdbqt"
+    subprocess.run([VINA, "--receptor", str(rec), "--ligand", str(lig),
+                    "--center_x", f"{center[0]:.2f}", "--center_y", f"{center[1]:.2f}",
+                    "--center_z", f"{center[2]:.2f}", "--size_x", f"{size[0]:.1f}",
+                    "--size_y", f"{size[1]:.1f}", "--size_z", f"{size[2]:.1f}",
+                    "--exhaustiveness", "16", "--out", str(pose)], capture_output=True, text=True)
+    if not pose.exists():
+        return None
+    top = WORK / "redock_top.pdb"  # first model only
+    subprocess.run(["obabel", str(pose), "-O", str(top), "-f", "1", "-l", "1"], capture_output=True)
+    return top
+
+
+def obrms(ref: Path, test: Path) -> float | None:
+    # strip H to compare heavy-atom poses; obrms is automorphism-aware
+    r2, t2 = WORK / "ref_noH.sdf", WORK / "test_noH.sdf"
+    subprocess.run(["obabel", str(ref), "-d", "-O", str(r2)], capture_output=True)
+    subprocess.run(["obabel", str(test), "-d", "-O", str(t2)], capture_output=True)
+    out = subprocess.run(["obrms", str(r2), str(t2)], capture_output=True, text=True).stdout
+    m = re.search(r"(-?\d+\.\d+)", out)
+    return float(m.group(1)) if m else None
+
+
+def main() -> None:
+    print("=== A. Redocking-RMSD validation ===")
+    for target, (pdb, resname, drug) in XTAL_LIG.items():
+        rec, center, size = prep_receptor_and_box(pdb, resname)
+        smi = pubchem_smiles(drug)
+        lig = prep_ligand(drug, smi)
+        xtal = extract_crystal_ligand(pdb, resname)
+        pose = dock_pose(rec, lig, center, size)
+        rmsd = obrms(xtal, pose) if pose else None
+        verdict = "PASS (<2A)" if (rmsd is not None and rmsd < 2.0) else \
+                  ("MARGINAL (<3A)" if (rmsd is not None and rmsd < 3.0) else "FAIL")
+        rstr = f"{rmsd:.2f} A" if rmsd is not None else "NA"
+        print(f"  {drug:12s} -> {target:11s} ({pdb}/{resname}): redock RMSD {rstr}  {verdict}")
+
+    print("\n=== B. Ligand efficiency (affinity / heavy atoms), de-biasing size ===")
+    # raw affinities from the cross-dock baseline (scripts/dock_positive_controls.py)
+    aff = {"voxelotor": {"hemoglobin": -8.1, "PKR": -9.3}, "mitapivat": {"hemoglobin": -10.0, "PKR": -11.2},
+           "decitabine": {"hemoglobin": -6.6, "PKR": -7.0}, "hydroxyurea": {"hemoglobin": -3.9, "PKR": -3.6},
+           "caffeine": {"hemoglobin": -6.1, "PKR": -6.4}}
+    print(f"  {'ligand':12s}{'heavy':>6s}" + "".join(f"{t+' LE':>14s}" for t in TARGETS))
+    for lig, row in aff.items():
+        ha = Chem.MolFromSmiles(pubchem_smiles(lig)).GetNumHeavyAtoms()
+        cells = "".join(f"{row[t]/ha:>14.3f}" for t in TARGETS)
+        print(f"  {lig:12s}{ha:>6d}{cells}")
+
+
+if __name__ == "__main__":
+    main()