Mutation-resolved ABL1 inhibitor resistance prediction under mutation-disjoint and temporal cross-study validation

Computational prediction of ABL1 tyrosine-kinase inhibitor (TKI) resistance is difficult because available mutation–drug datasets are small, imbalanced, and heterogeneous. This study evaluates structure-aware resistance prediction under mutation-disjoint internal validation and temporal cross-study outcome validation. The development benchmark comprised 144 ABL1 mutation–TKI records (19 resistant and 125 susceptible) labelled by a ten-fold affinity-loss criterion. A fixed panel of 11 documented structural and energetic predictors was used to minimize data-adaptive feature selection within the validation framework. A calibrated ensemble combining Elastic-Net logistic regression, radial-basis-function support-vector machines, random forests, and XGBoost was evaluated using mutation-disjoint five-fold grouped out-of-fold prediction repeated ten times. Temporal validation used a later prime-editing resistance map, yielding 84 exact mutation–TKI matches across imatinib, nilotinib, bosutinib, and ponatinib; 61 had binary Sensitive/Resistant outcomes and 23 were retained as Intermediate. The 11-feature stack achieved grouped internal AUROC 0.645 (95% CI 0.429–0.830) and PR-AUC 0.230, with mean ± SD AUROC 0.583 ± 0.101 across repeated grouped splits. On the 61 binary later-study outcomes, AUROC was 0.605 (95% CI 0.431–0.874) and PR-AUC 0.835, modestly above the resistance-prevalence baseline of 0.770. External calibration was poor (Brier score 0.583; calibration slope 0.441). Elastic-Net showed somewhat higher temporal discrimination (AUROC 0.643; PR-AUC 0.877). These results indicate limited transportability across mutation identities and experimental platforms and support use of the framework for hypothesis-generating resistance screening rather than clinically calibrated treatment selection.