Leakage-aware audit shows tumor-homing peptide predictors may be overstated by redundancy and motif shortcuts
- The authors present a reproducible framework that audits tumor-homing peptide predictors for leakage, motif-driven scoring, chemical redundancy and sensitivity to hard negatives, rather than judging them on score alone.
- It combines motif-rule comparisons, Morgan/ECFP chemical-space analysis, strict external-recognition filtering and a conservative scheme for ranking peptide analogs.
- Evaluation is retrospective on a curated public benchmark of a few thousand peptides, using a simple k-mer logistic-regression baseline under standard stratified cross-validation; the abstract is truncated, so stricter-split results are not stated.
💡 Points Based on the abstract
The audit is worth more than the predictor it audits. A k-mer logistic regression under stratified CV says little about novel scaffolds, so headline scores likely overstate real performance, though without stricter-split numbers or wet-lab follow-up I can't say by how much. I'd borrow the checklist for any peptide model I inherit.