Our study also aimed to verify if the performance of our machine learning models genuinely resulted from learning both protein and ligand features. To test this, we systematically permuted protein and ligand embeddings as well as activity labels, either during the training or testing phases, to assess their individual influence on model performance. : Performance of XGBoost models trained to predict bioactivity of kinase-ligand pairs as measured by F1-Score (mean ± s.e.) across cross-validation folds. Different cross-validation splits were created using either random split (top row), kinase split (middle row) or family split (bottom row) strategies. Models were trained with the dataset as is (left column) or after rebalancing with SNA (right column) using a ratio of 1:1 inactives to actives. Performances measured by AUROC and PFI are shown in Supp. Figure and Supp. Figure . Embeddings or labels were also permuted during training (depicted on X-axis) and compared with the baseline (no permutation; "None"). Permutations were also performed on the test set and reported separately (Supp. Figures 15