The data splitting strategy had the most profound effect on the model performance metrics (see Table ). Post-hoc pairwise comparisons revealed that models trained using a random split consistently outperformed those using kinase-specific or family-specific splits in terms of F1-score, as shown in Figure , and AUROC, as illustrated in Supp. Figure . This also held true with or without SNA (Supp. Figure ), except for the F1-Score (Figure ) and PFI (Supp. Figure ) which were not significantly different between kinase and family splits in the baseline (no rebalancing and no permutation) conditions. After rebalancing, however, the same trend was observed as the overall result (discussed further below). This confirmed that greater data leakage across training and testing folds contributes to model performance and undermines generalizability to unseen targets.