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docs: record AUPRC 0.8556 in METHODS and DISCOVERY_PREDICTION #59

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cschanhniem merged 1 commit into from
Jun 28, 2026
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docs: record AUPRC 0.8556 in METHODS and DISCOVERY_PREDICTION #59

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3 changes: 2 additions & 1 deletion docs/DISCOVERY_PREDICTION.md
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Expand Up @@ -243,7 +243,8 @@ Fmoc SPPS with acetonitrile/water gradient; verify MALDI-TOF pre-assay.
Updated from ~55–65% due to addition of proven scaffold families.

Basis:
- Pipeline AUROC = 0.8164 (bootstrap CI₉₅: 0.71–0.89) vs composition-matched UniProt decoys
- Pipeline AUROC = 0.8164 (95% CI: 0.72–0.90) vs composition-matched UniProt decoys
- Pipeline AUPRC = 0.8556 (+0.36 above random baseline 0.50; PR curve emphasises precision at selection operating point)
- Recall@20 = 43% on internal benchmark (positives recovered in top 20 ranked candidates)
- SEED-003 family (up to 4/20): RRWQWRMKKLG is curated known AMP → variants ~65–75% hit rate
- SEED-007 family (up to 4/20): Bombolitin II (*Bombus pennsylvanicus* bumblebee venom) with
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11 changes: 7 additions & 4 deletions docs/METHODS.md
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Expand Up @@ -321,10 +321,13 @@ benchmark set using the full ensemble scorer). Benchmarked after PRs #48–54; t
improvements in those PRs moved AUROC from 0.7926 → 0.8138 → 0.8164. Bootstrap 95% CI
computed via Wilcoxon-Mann-Whitney (n=2000 iterations; CI reported in `validate-scoring` output).

**AUPRC (v0.5.x):** Area Under Precision-Recall Curve is now computed alongside AUROC and
reported by `make validate-scoring`. AUPRC is preferable to AUROC for class-imbalanced
datasets because it emphasises precision at the operating point actually used for candidate
selection. Random baseline = dataset prevalence (0.50 for the balanced demo set).
**AUPRC (v0.5.x):** `0.8556` (+0.3556 above the random baseline of 0.50). Area Under
Precision-Recall Curve is computed alongside AUROC and reported by `make validate-scoring`.
AUPRC is preferable to AUROC for class-imbalanced datasets because it emphasises precision
at the operating point actually used for candidate selection. Random baseline = dataset
prevalence (0.50 for the balanced demo set). Implemented with pessimistic tie-breaking
(negatives sort first on tied scores) to match sklearn `average_precision_score` convention
and avoid inflation to 1.0 for constant-score classifiers.

**To reproduce:** `make validate-scoring` (runs `cli validate-scoring` on
`examples/validation/known_amps.csv` vs `examples/validation/random_background.csv`)
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