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cschanhniem merged 1 commit into from
Jul 3, 2026
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feat(scoring): add rich selectivity scorer from evidence-identified features #130

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2 changes: 1 addition & 1 deletion docs/ARCHITECTURE.md
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Expand Up @@ -36,7 +36,7 @@ The purpose of the added layers is not simulation theater. It is to improve whic
|---|---|
| `openamp_foundry.data` | loading and normalizing candidate/reference data |
| `openamp_foundry.features` | physicochemical feature extraction |
| `openamp_foundry.scoring` | activity, safety, hemolysis risk, novelty, synthesis, ensemble, expert composite scoring |
| `openamp_foundry.scoring` | activity, safety, hemolysis risk, novelty, synthesis, ensemble, expert composite, rich selectivity scoring |
| `openamp_foundry.selection` | ranking (ensemble or expert composite) and diversity selection |
| `openamp_foundry.evidence` | JSON certificate generation and validation |
| `openamp_foundry.reports` | human-readable and machine-readable batch, calibration, and wet-lab review reports |
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41 changes: 40 additions & 1 deletion docs/METRICS_CURRENT.md
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Expand Up @@ -5,7 +5,7 @@ Machine-readable snapshot: `outputs/metrics_snapshot.json` regenerated with `mak
> **Purpose:** One authoritative table of current pipeline metrics. If any doc disagrees
> with this file, this file wins. Updated whenever benchmark/benchmark config changes.
>
> **Last updated:** 2026-07-03 (feature decomposition benchmark added)
> **Last updated:** 2026-07-03 (rich selectivity scorer added)
> **Pipeline version:** v0.5.x
> **Branch:** main

Expand Down Expand Up @@ -557,6 +557,43 @@ ultimately be needed for clinically meaningful discrimination.

Run: `make bench-feature-decomp` or `python -m openamp_foundry.cli bench feature-decomp`

## Rich Selectivity Scorer (v0.5.16 — added 2026-07-03)

The feature decomposition benchmark identified 8 significant features for selective_vs_hemolytic
discrimination, but the old `selectivity_proxy` (charge + GRAVY) used only 2. The rich selectivity
scorer (`scoring/selectivity_rich.py`) combines all 8 significant features, weighted by detection
AUROC, to produce a composite selectivity score.

| Scorer | Detection AUROC | CI 95% | Significant? |
|--------|----------------|--------|-------------|
| **rich_selectivity** | **0.7138** | **0.6266-0.7951** | **YES** |
| selectivity_proxy (old) | 0.5744 | 0.4954-0.6558 | Marginal |
| hemolysis_risk | 0.5650 | 0.4664-0.6601 | NO |
| expert_composite | 0.5459 | 0.4562-0.6305 | NO |
| safety | 0.5116 | 0.4321-0.5954 | NO |
| ensemble | 0.4201 | 0.3335-0.5067 | NO (anti-signal) |

**Key finding:** The rich selectivity scorer is the **first pipeline score with statistically
significant hemolysis detection** on the expanded n=179 benchmark (CI lower bound 0.6266 > 0.5).
It outperforms the old selectivity_proxy by +0.14 AUROC and is the only scorer whose CI excludes 0.5.

**Features combined (by detection AUROC):**
`hydrophobic_fraction` (0.6745), `net_charge_proxy` (0.6394), `net_charge_ph74` (0.6332),
`helix_propensity` (0.6489), `interior_trypsin_sites` (0.6089), `selectivity_proxy` (0.6095,
protective), `longest_repeat_run` (0.5946), `length` (0.5900).

**Honest limitations:**
- The rich selectivity scorer does NOT triage AMP-vs-decoy correctly (selective_vs_decoy = 0.19).
It is designed for within-AMP ranking, not activity detection. It must be combined with an
activity gate to be useful for candidate selection.
- Individual feature AUROCs are weak (0.59-0.67); the composite's CI is wide (0.63-0.80).
- Normalisation thresholds are empirical and may not generalise beyond the reference set.
- Does not model 3D structure, oligomeric state, or membrane curvature.
- HC50 values are approximate literature values with high inter-assay variability.
- This is a triage signal, NOT a hemolysis predictor. Wet-lab hemolysis assay remains mandatory.

Run: `make bench-selectivity` (rich_selectivity is included in the selectivity benchmark output)

## Test Suite

| Metric | Value |
Expand All @@ -578,6 +615,7 @@ Run: `make bench-feature-decomp` or `python -m openamp_foundry.cli bench feature
| Benchmark at 191 sequences | Still far from 500+ target flagged in ROADMAP (v1.0+) |
| APD/DRAMP novelty (v2) | Complete — 27,234-sequence combined DB (APD6+DRAMP+UniProt); BLOSUM62 local alignment; Wave 0.5 results updated |
| No wet-lab data | All probabilities are upper bounds; true hit rate unknown |
| Rich selectivity scope | Designed for within-AMP selectivity only; does not distinguish AMPs from decoys (selective_vs_decoy=0.19) |

---

Expand All @@ -595,5 +633,6 @@ Run: `make bench-feature-decomp` or `python -m openamp_foundry.cli bench feature
| 2026-07-01 | Within-AMP selectivity benchmark added: safety scorer FAILS hemolysis detection (AUROC=0.3844); synthesis is only significant risk detector (AUROC=0.8027); expert composite better than ensemble but not significant (0.5119 vs 0.3486) | OpenAMP loop |
| 2026-07-01 | Expert composite ranking integration: `score_candidates()` now computes `expert_composite` and `hemolysis_risk`; `--ranking-mode expert` CLI flag; expert-ranked top-5 have lower mean hemolysis_risk than ensemble | OpenAMP loop |
| 2026-07-02 | **Strict triage benchmark added:** composition-matched scrambled decoys replace random background. No scorer triages correctly — standard triage "success" of selectivity_proxy (0.782 sel_vs_dec) and expert_composite (0.757) was inflated by trivially distinguishable decoys. selectivity_proxy collapses to 0.500 (purely composition-driven), ensemble drops to 0.572. Real bottleneck (selective_vs_hemolytic) unchanged. | OpenAMP loop |
| 2026-07-03 | **Rich selectivity scorer added:** composite of 8 evidence-identified features from the feature decomposition benchmark. Detection AUROC=0.7138 (CI 0.63-0.80) on n=179 — first pipeline score with statistically significant selective_vs_hemolytic discrimination. Old selectivity_proxy=0.5744 (CI 0.50-0.66). Honest limitation: does not triage AMP-vs-decoy (0.19); must be combined with activity gate. | OpenAMP loop |
| 2026-07-03 | **Feature decomposition benchmark added:** per-feature selective_vs_hemolytic AUROC for all 30 scalar physicochemical features. hydrophobic_fraction is the strongest single discriminative feature (0.6745, CI 0.58-0.77) but is NOT used by the selectivity proxy. 8/30 features have significant signal; 6 of those are unused. Provides actionable diagnostic for why composite scorers fail selective_vs_hemolytic discrimination. | OpenAMP loop |
| 2026-06-29 | Initial — expanded benchmark (PR #110) | OpenAMP CI |
29 changes: 29 additions & 0 deletions docs/ROADMAP.md
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Expand Up @@ -303,6 +303,35 @@ by the current selectivity model. This converts the v0.5.14 aggregate failure
into actionable diagnostic information: the next loop knows exactly which feature
axes to combine into a richer selectivity scorer.

## v0.5.16 — Rich Selectivity Scorer ✓ (2026-07-03)

The feature decomposition benchmark (v0.5.15) identified 8 statistically significant
features for selective_vs_hemolytic discrimination, but the old `selectivity_proxy`
(charge + GRAVY) used only 2. This version builds a richer composite selectivity
scorer from the evidence — the first pipeline score with statistically significant
hemolysis detection on the expanded n=179 benchmark.

Changes:
- `scoring/selectivity_rich.py`: `rich_selectivity_score()` — composite of 8
evidence-identified features, weighted by detection AUROC, with full component
breakdown via `rich_selectivity_breakdown()`
- `benchmark/retrospective.py`: rich selectivity added to selectivity benchmark
evaluation and verdict
- `benchmark/triage.py`: rich selectivity added to standard and strict triage
benchmarks
- `benchmark/metrics_snapshot.py`: selectivity snapshot expanded with
`per_score_auroc` and `rich_selectivity_verdict`
- `cli/commands/benchmark.py`: selectivity bench summary includes
`rich_selectivity_verdict`
- `tests/test_selectivity_rich.py`: 18 tests covering scoring, breakdown, and
benchmark integration
- `outputs/metrics_snapshot.json`: regenerated with rich selectivity results

Key finding: rich selectivity detection AUROC=0.7138 (CI 0.6266-0.7951) — first
pipeline score with CI excluding 0.5 on selective_vs_hemolytic. Old
selectivity_proxy=0.5744 (CI 0.4954-0.6558). Honest limitation: does NOT triage
AMP-vs-decoy (selective_vs_decoy=0.19); designed for within-AMP selectivity only.

## v1.0 — Validated dry-lab-to-wet-lab loop

- independently reviewed assay batch (expert_review.yml GitHub issue template);
Expand Down
152 changes: 150 additions & 2 deletions outputs/metrics_snapshot.json
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Expand Up @@ -476,8 +476,144 @@
"n_border": 59,
"n_hemolytic": 54,
"n_selective": 125,
"per_score_auroc": {
"activity": {
"auroc": 0.6063,
"ci95_hi": 0.6858,
"ci95_lo": 0.5193,
"detection_ci95_hi": 0.4807,
"detection_ci95_lo": 0.3142,
"direction": "higher_is_risk",
"hemolysis_detection_auroc": 0.3937,
"mean_hemolytic": 0.6768,
"mean_selective": 0.6447
},
"boman_activity": {
"auroc": 0.4552,
"ci95_hi": 0.5484,
"ci95_lo": 0.3633,
"detection_ci95_hi": 0.6367,
"detection_ci95_lo": 0.4516,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5448,
"mean_hemolytic": 0.424,
"mean_selective": 0.4333
},
"ensemble": {
"auroc": 0.5799,
"ci95_hi": 0.6665,
"ci95_lo": 0.4933,
"detection_ci95_hi": 0.5067,
"detection_ci95_lo": 0.3335,
"direction": "higher_is_risk",
"hemolysis_detection_auroc": 0.4201,
"mean_hemolytic": 0.8532,
"mean_selective": 0.8395
},
"expert_composite": {
"auroc": 0.4541,
"ci95_hi": 0.5438,
"ci95_lo": 0.3695,
"detection_ci95_hi": 0.6305,
"detection_ci95_lo": 0.4562,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5459,
"mean_hemolytic": 0.7646,
"mean_selective": 0.7704
},
"hemolysis_risk": {
"auroc": 0.565,
"ci95_hi": 0.6601,
"ci95_lo": 0.4664,
"detection_ci95_hi": 0.6601,
"detection_ci95_lo": 0.4664,
"direction": "higher_is_risk",
"hemolysis_detection_auroc": 0.565,
"mean_hemolytic": 0.2042,
"mean_selective": 0.1535
},
"hinge_selectivity": {
"auroc": 0.5716,
"ci95_hi": 0.6536,
"ci95_lo": 0.4899,
"detection_ci95_hi": 0.5101,
"detection_ci95_lo": 0.3464,
"direction": "higher_is_risk",
"hemolysis_detection_auroc": 0.4284,
"mean_hemolytic": 0.5667,
"mean_selective": 0.4968
},
"novelty": {
"auroc": 0.5,
"ci95_hi": 0.5,
"ci95_lo": 0.5,
"detection_ci95_hi": 0.5,
"detection_ci95_lo": 0.5,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5,
"mean_hemolytic": 1.0,
"mean_selective": 1.0
},
"rich_selectivity": {
"auroc": 0.2862,
"ci95_hi": 0.3734,
"ci95_lo": 0.2049,
"detection_ci95_hi": 0.7951,
"detection_ci95_lo": 0.6266,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.7138,
"mean_hemolytic": 0.5743,
"mean_selective": 0.636
},
"safety": {
"auroc": 0.4884,
"ci95_hi": 0.5679,
"ci95_lo": 0.4046,
"detection_ci95_hi": 0.5954,
"detection_ci95_lo": 0.4321,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5116,
"mean_hemolytic": 0.9458,
"mean_selective": 0.9427
},
"selectivity_proxy": {
"auroc": 0.4256,
"ci95_hi": 0.5046,
"ci95_lo": 0.3442,
"detection_ci95_hi": 0.6558,
"detection_ci95_lo": 0.4954,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5744,
"mean_hemolytic": 0.8345,
"mean_selective": 0.8449
},
"serum_stability": {
"auroc": 0.4007,
"ci95_hi": 0.4918,
"ci95_lo": 0.3137,
"detection_ci95_hi": 0.6863,
"detection_ci95_lo": 0.5082,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5993,
"mean_hemolytic": 0.6186,
"mean_selective": 0.6571
},
"synthesis": {
"auroc": 0.4708,
"ci95_hi": 0.5563,
"ci95_lo": 0.3876,
"detection_ci95_hi": 0.6124,
"detection_ci95_lo": 0.4437,
"direction": "higher_is_safe",
"hemolysis_detection_auroc": 0.5292,
"mean_hemolytic": 0.9733,
"mean_selective": 0.9732
}
},
"rich_selectivity_verdict": "Rich selectivity scorer detects hemolysis risk (detection AUROC=0.7138, CI=[0.6266, 0.7951]). This is the first composite selectivity score built from evidence-identified features (feature decomposition benchmark v0.5.15) that achieves statistically significant selective_vs_hemolytic discrimination. It outperforms the old selectivity_proxy (detection AUROC=0.5744). The signal comes from combining 8 significant physicochemical features that the old proxy ignored.",
"risk_detectors": [
"serum_stability"
"serum_stability",
"rich_selectivity"
],
"risk_indicators": [
"expert_composite",
Expand All @@ -502,7 +638,7 @@
"recall_at_43": 0.4211
},
"strict_triage": {
"best_scorer": "triage_score",
"best_scorer": "rich_selectivity",
"decoy_type": "composition_matched_scrambled",
"n_decoy": 125,
"n_hemolytic": 45,
Expand Down Expand Up @@ -532,6 +668,12 @@
"selective_vs_hemolytic": 0.4876,
"triages_correctly": false
},
"rich_selectivity": {
"hemolytic_vs_decoy": 0.2683,
"selective_vs_decoy": 0.5155,
"selective_vs_hemolytic": 0.7453,
"triages_correctly": false
},
"safe_weighted_ensemble": {
"hemolytic_vs_decoy": 0.6857,
"selective_vs_decoy": 0.6405,
Expand Down Expand Up @@ -612,6 +754,12 @@
"selective_vs_hemolytic": 0.4876,
"triages_correctly": false
},
"rich_selectivity": {
"hemolytic_vs_decoy": 0.0825,
"selective_vs_decoy": 0.1918,
"selective_vs_hemolytic": 0.7453,
"triages_correctly": false
},
"safe_weighted_ensemble": {
"hemolytic_vs_decoy": 0.8904,
"selective_vs_decoy": 0.849,
Expand Down
2 changes: 2 additions & 0 deletions src/openamp_foundry/benchmark/metrics_snapshot.py
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Expand Up @@ -143,9 +143,11 @@ def build_metrics_snapshot(
"n_border": selectivity["n_border"],
"safety_verdict": selectivity["safety_verdict"],
"selectivity_proxy_verdict": selectivity["selectivity_proxy_verdict"],
"rich_selectivity_verdict": selectivity["rich_selectivity_verdict"],
"expert_composite_verdict": selectivity["expert_composite_verdict"],
"risk_detectors": selectivity["risk_detectors"],
"risk_indicators": selectivity["risk_indicators"],
"per_score_auroc": selectivity["per_score_auroc"],
},
"triage": {
"best_scorer": triage["best_scorer"],
Expand Down
32 changes: 31 additions & 1 deletion src/openamp_foundry/benchmark/retrospective.py
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Expand Up @@ -816,6 +816,7 @@ def run_selectivity_benchmark(
from openamp_foundry.scoring.novelty import novelty_score
from openamp_foundry.scoring.hemolysis import hemolysis_risk_score
from openamp_foundry.scoring.safety import safety_score
from openamp_foundry.scoring.selectivity_rich import rich_selectivity_score
from openamp_foundry.scoring.stability import serum_stability_score
from openamp_foundry.scoring.synthesis import synthesis_feasibility_score

Expand All @@ -838,6 +839,7 @@ def run_selectivity_benchmark(
boman_act = boman_activity_score(seq)
stability = serum_stability_score(features)
sel_proxy = float(features.get("selectivity_proxy", 0.0))
rich_sel = rich_selectivity_score(features)
hemo_risk = hemolysis_risk_score(features)
raw = {
"activity": act, "safety": safe,
Expand Down Expand Up @@ -866,6 +868,7 @@ def run_selectivity_benchmark(
"serum_stability": stability,
"selectivity_proxy": sel_proxy,
"hemolysis_risk": hemo_risk,
"rich_selectivity": rich_sel,
"hinge_selectivity": exp.components["hinge_selectivity"],
})

Expand All @@ -882,7 +885,7 @@ def run_selectivity_benchmark(
score_cols = [
"ensemble", "expert_composite", "activity", "safety",
"synthesis", "novelty", "boman_activity", "serum_stability",
"selectivity_proxy", "hemolysis_risk", "hinge_selectivity",
"selectivity_proxy", "rich_selectivity", "hemolysis_risk", "hinge_selectivity",
]

per_score: dict[str, dict] = {}
Expand Down Expand Up @@ -1028,6 +1031,32 @@ def run_selectivity_benchmark(
"No pipeline score can distinguish hemolytic from selective AMPs."
)

# Rich selectivity scorer assessment (evidence-driven composite from feature decomposition)
rich_detection = per_score["rich_selectivity"]["hemolysis_detection_auroc"]
rich_ci_lo = per_score["rich_selectivity"]["detection_ci95_lo"]
rich_ci_hi = per_score["rich_selectivity"]["detection_ci95_hi"]

if rich_detection > 0.5 and rich_ci_lo > 0.5:
rich_verdict = (
f"Rich selectivity scorer detects hemolysis risk "
f"(detection AUROC={rich_detection:.4f}, CI=[{rich_ci_lo:.4f}, {rich_ci_hi:.4f}]). "
"This is the first composite selectivity score built from evidence-identified features "
"(feature decomposition benchmark v0.5.15) that achieves statistically significant "
"selective_vs_hemolytic discrimination. It outperforms the old selectivity_proxy "
f"(detection AUROC={sel_detection:.4f}). The signal comes from combining 8 significant "
"physicochemical features that the old proxy ignored."
)
elif rich_detection > 0.5:
rich_verdict = (
f"Rich selectivity scorer shows trend (detection AUROC={rich_detection:.4f}, "
f"CI lo={rich_ci_lo:.4f}). Signal is present but not statistically significant."
)
else:
rich_verdict = (
f"Rich selectivity scorer fails (detection AUROC={rich_detection:.4f}). "
"Combining significant features did not improve selective_vs_hemolytic discrimination."
)

# Rank hemolytic AMPs by safety score to find blind spots
hemolytic_by_safety = sorted(hemolytic, key=lambda r: r["safety"], reverse=True)
blind_spots = [
Expand All @@ -1052,6 +1081,7 @@ def run_selectivity_benchmark(
"selectivity_proxy_verdict": sel_verdict,
"expert_composite_verdict": expert_verdict,
"hemolysis_risk_verdict": hemo_risk_verdict,
"rich_selectivity_verdict": rich_verdict,
"blind_spots": blind_spots,
"border_zone": [
{"id": r["id"], "sequence": r["sequence"], "family": r["family"],
Expand Down
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