Phase 1 co-folding WORKS: HDAC2/Zn validated (Boltz-2 on Modal)

First clear positive result in the project. Ran Phase 1 on Modal L4
(~$0.70). Boltz-2 P(binder), cofactors co-folded:
- HDAC2 (+Zn): vorinostat 0.9994 vs negatives ~0.1 -> PASS, decisive
- hemoglobin (+heme): voxelotor 0.46 -> PASS (weak; covalent/tetramer)
- PKR (+FBP/Mg): mitapivat 0.32 < hydroxyurea 0.40 -> FAIL (allosteric)

HDAC2/Zn is the exact case classical Vina failed (no metal term, 7.9A
redock). Co-folding handles the Zn-chelation chemistry -> the structure-
binding modality pivot (PLAN §12) is validated on its decisive test.

Engineering fixes that got it running: image needs cuequivariance kernels;
max_containers=1 so weights download once (parallel corrupted the shared-
Volume checkpoint); rank by P(binder) not affinity_pred_value (sign).

Adds docs/results/phase1_affinity.csv (committed; raw under data/ gitignored).

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

gpu/modal_app.py

@@ -27,7 +27,8 @@ app = modal.App("reverso-binding")
 image = (
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git", "wget")
-    .pip_install("boltz", "rdkit", "numpy")
+    # Boltz-2 needs NVIDIA cuequivariance kernels (cuda 12) for inference, plus rdkit/numpy.
+    .pip_install("boltz", "cuequivariance-torch", "cuequivariance-ops-torch-cu12", "rdkit", "numpy")
 )
 weights = modal.Volume.from_name("reverso-binding-weights", create_if_missing=True)
 WEIGHTS = "/weights"
@@ -119,7 +120,9 @@ def build_boltz_yaml(protein_seq: str, ligand_smiles: str, cofactor_ccds: list[s
 
 # ------------------------------------------------------------------------------- GPU function
 
-@app.function(gpu="L4", image=image, volumes={WEIGHTS: weights}, timeout=3600)
+# max_containers=1: run the inputs serially on one warm container so the weights download ONCE
+# (no concurrent-download race that corrupts the checkpoint) and are reused for the rest.
+@app.function(gpu="L4", image=image, volumes={WEIGHTS: weights}, timeout=3600, max_containers=1)
 def cofold(label: str, protein_seq: str, ligand_smiles: str, cofactor_ccds: list[str]) -> dict:
     """Co-fold one complex (protein + drug + cofactors) on the GPU; return affinity + P(binder).
 
@@ -136,12 +139,14 @@ def cofold(label: str, protein_seq: str, ligand_smiles: str, cofactor_ccds: list
     work.mkdir(parents=True, exist_ok=True)
     (work / "in.yaml").write_text(build_boltz_yaml(protein_seq, ligand_smiles, cofactor_ccds))
     out = work / "out"
-    subprocess.run(
-        ["boltz", "predict", str(work / "in.yaml"), "--use_msa_server",
-         "--cache", boltz_cache, "--out_dir", str(out), "--output_format", "pdb"],
-        check=True,
-    )
-    weights.commit()  # persist anything newly downloaded
+    try:
+        subprocess.run(
+            ["boltz", "predict", str(work / "in.yaml"), "--use_msa_server",
+             "--cache", boltz_cache, "--out_dir", str(out), "--output_format", "pdb"],
+            check=True,
+        )
+    finally:
+        weights.commit()  # persist downloaded weights/CCD even if this run fails, so retries skip it
 
     # Affinity is written to a JSON under out/predictions/<name>/; parse defensively (keys vary).
     aff = {"affinity_pred_value": None, "affinity_probability_binary": None}