After establishing our cross-validation splits, each training and validation fold was individually rebalanced by calculating the current ratio of inactive to active ligands for each kinase, determining the number of additional inactive ligands needed to achieve a 1:1 ratio, and, if necessary, randomly selecting the appropriate number of ligands from the ChEMBL pool. These selected ligands were then labeled as 'inactive' and added to the dataset, aiming to rigorously test the models' ability to generalize and perform under more balanced class distributions, thereby potentially reducing bias and enhancing the reliability of bioactivity predictions.