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@@ -95,6 +95,7 @@ RUN wget -q https://github.com/iqtree/iqtree2/releases/download/v2.3.6/iqtree-2.
 
 # Download pre-compiled AutoDock Vina binary from GitHub releases
 RUN wget -q "https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_1.2.7_linux_x86_64" -O /usr/local/bin/vina && \
+    echo "f31f774f723bba7bbe6e9d1c47577020eea9a8da16424284c043d22593570644  /usr/local/bin/vina" | sha256sum -c - && \
     chmod +x /usr/local/bin/vina
 
 WORKDIR /app

Dockerfile.worker

@@ -8,6 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     rm -rf /var/lib/apt/lists/*
 
 RUN wget -q "https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_1.2.7_linux_x86_64" -O /usr/local/bin/vina && \
+    echo "f31f774f723bba7bbe6e9d1c47577020eea9a8da16424284c043d22593570644  /usr/local/bin/vina" | sha256sum -c - && \
     chmod +x /usr/local/bin/vina
 
 # Gnina (CNN-based rescoring) — OFF BY DEFAULT, and the reason is not image size.

app/benchmarking/docking_redock.py

@@ -1,7 +1,7 @@
 """BBS-1 docking redocking benchmark helpers.
 
 The benchmark compares a predicted ligand pose with the crystallographic
-reference using RDKit's symmetry-aware best RMSD. This is intentionally kept
+reference using RDKit's symmetry-aware RMSD in the receptor frame. This is intentionally kept
 outside the production docking score path: docking affinity and pose RMSD are
 separate quantities and must not be conflated.
 """
@@ -10,6 +10,7 @@ from __future__ import annotations
 
 from copy import deepcopy
 from dataclasses import asdict, dataclass
+import math
 from typing import Any
 
 
@@ -49,7 +50,7 @@ class RedockingResult:
     threshold_angstrom: float
     passed: bool
     atom_count: int
-    metric: str = "RDKit GetBestRMS symmetry-aware heavy-atom RMSD"
+    metric: str = "RDKit CalcRMS symmetry-aware heavy-atom RMSD in receptor frame (no alignment)"
 
     def to_dict(self) -> dict[str, Any]:
         return asdict(self)
@@ -67,8 +68,8 @@ def symmetry_aware_pose_rmsd(reference_mol, predicted_mol) -> float:
     """Return symmetry-aware heavy-atom RMSD in angstrom.
 
     Both molecules must represent the same ligand graph and contain 3D
-    coordinates. RDKit's GetBestRMS searches symmetry-equivalent atom maps,
-    avoiding inflated RMSD from arbitrary numbering of equivalent atoms.
+    coordinates in the same receptor frame. CalcRMS searches equivalent atom
+    maps without moving the predicted pose onto the reference ligand.
     """
     from rdkit.Chem import rdMolAlign
 
@@ -81,12 +82,20 @@ def symmetry_aware_pose_rmsd(reference_mol, predicted_mol) -> float:
         )
     if ref.GetNumConformers() == 0 or pred.GetNumConformers() == 0:
         raise ValueError("Both ligands must contain 3D coordinates")
-    return float(rdMolAlign.GetBestRMS(pred, ref))
+    if ref.GetNumAtoms() == 0:
+        raise ValueError("Both ligands must contain heavy atoms")
+    for mol in (ref, pred):
+        if not all(math.isfinite(float(v)) for xyz in mol.GetConformer().GetPositions() for v in xyz):
+            raise ValueError("Ligand coordinates must be finite")
+    rmsd = float(rdMolAlign.CalcRMS(pred, ref))
+    if not math.isfinite(rmsd):
+        raise ValueError("Pose RMSD must be finite")
+    return rmsd
 
 
 def evaluate_redocking(reference_mol, predicted_mol, threshold_angstrom: float = 2.0) -> RedockingResult:
-    if threshold_angstrom <= 0:
-        raise ValueError("threshold_angstrom must be > 0")
+    if not math.isfinite(threshold_angstrom) or threshold_angstrom <= 0:
+        raise ValueError("threshold_angstrom must be > 0 and finite")
     rmsd = symmetry_aware_pose_rmsd(reference_mol, predicted_mol)
     atom_count = _remove_hydrogens(reference_mol).GetNumAtoms()
     return RedockingResult(

app/engines/docking_engine.py

@@ -21,6 +21,7 @@ Canonical input (a docking result object, e.g. from /api/docking):
 from __future__ import annotations
 
 from typing import Any
+import math
 
 from app.engines.base import BaseEngine, EngineResult, ValidationReport
 from app.figure.engine import bar_chart_panel, esc
@@ -107,8 +108,16 @@ class DockingEngine(BaseEngine):
             (_fnum(p.get("rmsd_lb"), 0.0) >= 0) and (_fnum(p.get("rmsd_ub"), 0.0) >= 0)
             for p in poses if isinstance(p, dict)
         )
-        box = result.evidence.get("box_size") or {}
-        box_ok = all(_fnum(box.get(k)) > 0 for k in ("x", "y", "z")) if box else True
+        def grid_values(value):
+            if isinstance(value, dict):
+                return [_fnum(value.get(k), float("nan")) for k in ("x", "y", "z")]
+            if isinstance(value, (list, tuple)) and len(value) == 3:
+                return [_fnum(v, float("nan")) for v in value]
+            return [float("nan")]*3
+
+        box = grid_values(result.evidence.get("box_size"))
+        center = grid_values(result.evidence.get("box_center"))
+        box_ok = all(math.isfinite(v) and v > 0 for v in box) and all(math.isfinite(v) for v in center)
 
         checks.extend([
             {"name": "poses_present", "passed": s["pose_count"] >= 1, "detail": f"{s['pose_count']} poses"},
@@ -119,7 +128,7 @@ class DockingEngine(BaseEngine):
             {"name": "rmsd_nonnegative", "passed": rmsd_ok, "detail": "all RMSD values >= 0"},
             {"name": "interactions_nonnegative", "passed": all(v >= 0 for v in
              (s["h_bonds"], s["hydrophobic"], s["pi_stacking"], s["salt_bridges"])), "detail": "interaction counts"},
-            {"name": "grid_box_valid", "passed": box_ok, "detail": "positive box sizes"},
+            {"name": "grid_box_valid", "passed": box_ok, "detail": "finite center and finite positive box sizes"},
         ])
         return ValidationReport(checks, self.name)
 
@@ -156,4 +165,4 @@ class DockingEngine(BaseEngine):
         )
 
 
-docking_engine = DockingEngine()
+docking_engine = DockingEngine()