Table : Statistical analysis of XGBoost models after cross-validation across metrics and with or without rebalancing the dataset. Baseline ANOVA refers to the analysis performed without taking into account permutations of embeddings. ANOVA was also performed with all permutation conditions with and without rebalancing. Significant p-values are indicated with asterisks. *p<0.05, **p<0.01, ***p<0.001. Significant conditions were followed up with post-hoc Tukey's HSD tests for pairwise comparisons (Supp. Tables ). Values with †indicate that post-hoc comparisons validated the significance (or lack thereof) of a variable. Protein permutation showed minimal impact on F1-scores and AUROC, suggesting that models may not heavily rely on protein embeddings for making predictions (Figure and Supp. Figure ). However, a noticeable decline in PFI was observed when protein embeddings were shuffled (Supp. Figure ), indicating reduced dependency on protein data. In contrast, permuting ligand embeddings led to significant decreases in both F1-scores and AU-ROC (Figure and Supp. Figure ), which underscores the ligands' critical role in model predictions, likely due to their greater diversity compared to proteins.