README.md

metadata

license: mit
tags:
  - protein
  - small-molecule
  - affinity-prediction
  - hit-to-lead
  - qsar
  - boltz
  - structure-based
  - capo
metrics:
  - pearsonr
  - spearmanr
model-index:
  - name: capo-hit-to-lead-ft-binding-boltz-20260706-1111-cd08-best
    results:
      - task:
          type: regression
          name: Hit-to-lead affinity ranking (FEP benchmark, held-out batch)
        metrics:
          - type: pearsonr
            value: 0.7396
            name: All-targets mean log-Pearson r (15 valid targets)
          - type: spearmanr
            value: 0.7
            name: All-targets mean Spearman r (15 valid targets)

CAPO · Hit-to-lead affinity head on frozen Boltz-2

A per-target-calibrated QSAR affinity head that turns Boltz-2 co-folding outputs into a calibrated potency ranking for hit-to-lead. Boltz-2 weights are frozen — this is "fine-tuning to the assay" by calibration on measured batch_1 potencies, not gradient training. That is precisely why 549 cached co-folds from a prior run could be reused at 0 GPU-hours.

Produced by CAPO (Compute-Aware Automated Protein-Model Optimization), run binding-boltz-20260706-1111-cd08. This repo contains the best checkpoint only.

What this is (and is not)

• Is: a light regression head (affinity_head.pkl) + per-target calibration that predicts log₁₀-potency from features derived from a Boltz-2 co-fold of a protein–ligand complex.
• Is not: a standalone folding/affinity model. It requires Boltz-2 to generate structural features for any new ligand, and no Boltz-2 weights are included or modified.

Features consumed by the head

Per protein–ligand complex (affinity_head_manifest.json):

• Ligand: ECFP4 fingerprint (radius 2, 1024 bits) + RDKit descriptors (MolWt, MolLogP, TPSA, NumHDonors, NumHAcceptors, NumRotatableBonds, NumAromaticRings, FractionCSP3).
• Boltz-2 signals: affinity_pred_value, affinity_probability_binary, ptm, iptm, complex_plddt, complex_pde.
• Contacts: contacts_4A, contacts_5A, contact_residues, buried_frac.

Task & data

Hit-to-lead affinity ranking on a FEP-style benchmark spanning 29 drug targets (bace, cdk2, tyk2, shp2, mcl1, eg5, p38, …). Given batch_1 measured potencies per target, rank a held-out batch_2. 644 complexes were co-folded (549 reused + 96 new). The intended headline was to show classical-ML < raw Boltz-2 << CAPO on the regime that is hard for both baselines.

Evaluation

All-target mean over the 15 targets that yielded valid ranking metrics (results/analysis/summary.json):

Metric (log₁₀ µM)	This model (CAPO)	Raw Boltz-2	Classical ML
log-Pearson r	0.7396	0.6227	0.6416
Spearman r	0.7000	0.5936	0.6162
RMSE	0.5588	1.0611	—

Calibration improves ordering and nearly halves RMSE vs raw Boltz-2 — its main win is fixing magnitude. The classical < Boltz < CAPO ordering holds on the all-target log-Pearson average.

⚠️ Important caveats

• Pre-registered headline is unresolved. The "hard-for-both" filter (raw-Boltz Spearman < 0.30 and classical LOO < 0.20, loo_min_n = 8) selected zero targets (headline_hard_for_both_targets: []). The primary claim was never testable on this benchmark — thresholds were too strict.
• 14 / 29 targets returned NaN ranking metrics from sparse batch_2 / too-few ligands; only 15 targets contribute to the average above.
• Sparse ligand counts can drive negative per-target correlations (e.g. hsp90 Spearman −0.80). Trust the pooled average over any single sparse target.

Files

• affinity_head.pkl — the calibrated QSAR affinity head (~23 MB).
• affinity_head_manifest.json — feature spec, seed, per-target detail, versions (Python 3.10.12).
• config.json — {strategy: "custom_frozen_boltz2_calibrate_qsar", model_id: "custom", n_targets: 29}.
• metrics.json — full pre-registration table + thresholds + per-target results.

How to use

import pickle
from huggingface_hub import hf_hub_download

REPO = "theoschiff-biie/capo-hit-to-lead-ft-binding-boltz-20260706-1111-cd08-best"
head = pickle.load(open(hf_hub_download(REPO, "affinity_head.pkl"), "rb"))

# 1. Co-fold each protein–ligand pair with Boltz-2 (frozen).
# 2. Build the feature vector per the ECFP4 + RDKit + Boltz-2 + contact spec in
#    affinity_head_manifest.json (feature order must match training).
# 3. head.predict(X) -> calibrated log10-potency; rank within a target to lead-rank.

Reproduce feature extraction with the CAPO run's src/ pipeline; the manifest is the authoritative feature contract.

Provenance

• CAPO run: binding-boltz-20260706-1111-cd08 (RUN_REPORT.md in the run dir).
• Model selection: bypassed — user-specified custom frozen-Boltz-2 pipeline.
• Cost gate: projected $10.61 vs $75 cap on 1× A100-SXM4 @ $1.99/h.
• Cost: infra $3.35 (1h 57m); total run $22.09. Reuse of 549 co-folds at 0 GPU-h.
• Push: checkpoints/best/ only (private). checkpoints/last/ retained locally, not pushed.

License follows the Boltz-2 base model (MIT).