Calibrated stacked learning for source-aware screening of ABL1/BCR–ABL1 mutation–TKI resistance

Predicting ABL1/BCR–ABL1 mutation resistance to tyrosine kinase inhibitors (TKIs) remains challenging because available mutation–drug evidence is limited, heterogeneous, and distributed across public bioactivity resources. This study presents an optimized calibrated stacked-learning framework for computational screening of ABL1/BCR–ABL1 mutation–TKI resistance. Public bioactivity records from BindingDB and ChEMBL were harmonized into a conservative binary mutation–drug pair-level dataset, with activity-derived variables excluded from model predictors to prevent target leakage. The proposed model combined automatic base-learner selection, sparse logistic stacking, probability calibration, and recall-prioritized threshold optimization. In the random stratified holdout experiment, the model achieved strong discrimination, with AUROC = 0.970 and PR-AUC = 0.922. Repeated stratified cross-validation confirmed stable performance, with mean AUROC = 0.932 ± 0.054 and mean PR-AUC = 0.869 ± 0.095. In the source-aware BindingDB-to-ChEMBL robustness experiment, performance decreased to AUROC = 0.795 and PR-AUC = 0.581, indicating sensitivity to database shift and assay heterogeneity. Benchmark analysis showed that simpler probabilistic and regularized models generalized better under cross-source evaluation. Overall, the framework provides a leakage-controlled and probability-calibrated screening approach for prioritizing resistant-candidate mutation–TKI pairs, but its outputs should be interpreted as hypothesis-generating predictions rather than clinical treatment recommendations.