Interpretive Limits of Cross-Split AUPRC Differences in Deep Learning-Based Antibiotic Screening

Abstract Deep learning expands the chemical space accessible to antibiotic discovery, making interpretation of evaluation results increasingly important when only a few compounds can be prioritized from vast candidate libraries. AUPRC depends on test-set prevalence, while molecular splitting can alter training-to-test structural relatedness and test-set class composition, so when prevalence differs across splits, a cross-split AUPRC difference alone cannot quantify how much of the performance change is attributable to structural differences. Cross-split comparisons should therefore report test-set size, positive count, prevalence, splitting method, and the precision-recall summary metric, while direct model comparisons should use the same fixed independent test set whenever possible. Claims about practical screening utility should also consider top-k performance, early enrichment metrics, or prospective experimental hit rates under a prespecified budget, so that evidence remains aligned with the scientific claims it is intended to support.