PHARMACEUTICALS · Published 2026-07-27 · DOI 10.3390/ph19081173
Sukrit Kashyap, Barlina Konwar, Ji Young Lee, Kwang-sun Kim
<b>Background/Objectives:</b> Antibiotic-resistant <i>Pseudomonas aeruginosa</i> and limited experimental validation capacity motivate efficient prioritization of antibacterial candidates from large chemical libraries. Quantitative structure–activity relationship (QSAR) benchmarks often binarize disk diffusion inhibition-zone (IZ) measurements, obscuring activity gradients and imposing threshold dependence. We examined whether continuous-IZ modeling provides complementary retrospective prioritization relative to calibrated binary classification in a highly imbalanced dataset. <b>Methods:</b> In this retrospective matched-data evaluation, we revisited a published ChEMBL-derived <i>P. aeruginosa</i> disk diffusion dataset using preserved training and locked external validation partitions. A calibrated support vector classifier using Molecular ACCess System keys (SVC/MACCS) provided conservative binary active calls. RegressionStack combined source-descriptor extreme gradient boosting (XGBoost) and ElasticNet regressors, Morgan-fingerprint random forest and gradient-boosting regressors, and a MACCS-key XGBoost regressor through an XGBoost meta-regressor to predict continuous IZ. <b>Results:</b> On the locked external set (<i>n</i> = 1130; 87 actives), SVC/MACCS achieved a positive predictive value (PPV) = 0.619, receiver operating characteristic area under the curve (ROC-AUC) = 0.857, precision–recall area under the curve (PR-AUC) = 0.479, and enrichment factor at 1% (EF@1%) = 7.58. RegressionStack achieved a mean absolute error (MAE) = 3.20 mm, ROC-AUC = 0.896, PR-AUC = 0.545, and EF@1% = 9.74. Neither paired permutation tests (ROC-AUC, <i>p</i> = 0.501; PR-AUC, <i>p</i> = 0.442) nor paired bootstrap confidence intervals resolved these differences. The y-randomization analyses supported non-random signals; scaffold-grouped validation retained early enrichment but showed reduced broader performance. The consensus-positive tier contained 27 actives among 35 nominations (PPV = 0.771). At measured IZ ≥ 30 mm, MAE increased to 9.46 mm, and all 30 compounds were underpredicted. <b>Conclusions:</b> Continuous-target modeling retained the IZ scale during training and generated a threshold-flexible predicted-IZ prioritization coordinate complementary to, but not statistically superior to, calibrated binary classification. The methodological contribution is a matched-data evaluation of complete workflows and their nomination behavior under the same partitions, yielding a retrospective compound-tiering scheme. Because the pipelines differed in architecture and molecular representation, their differences cannot be attributed solely to endpoint formulation. The workflows were not prospectively evaluated on compounds lacking pre-existing IZ measurements, and whether retrospective enrichment improves experimental hit discovery or reduces screening workload remains to be established.
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Kashyap, S., Konwar, B., Lee, J., et al. (2026). Continuous Inhibition-Zone Modeling and Binary Classification for Pseudomonas aeruginosa Hit Prioritization: A Retrospective QSAR Evaluation. PHARMACEUTICALS. https://doi.org/10.3390/ph19081173