For each dataset, we constructed a QSAR model using the gradient boosting method for both the balanced and imbalanced training sets. Model performance was evaluated using several metrics including balanced accuracy (BA), positive predictive value (PPV, also known as precision), area under the receiver operator curve (AUC), and the Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC). These metrics were computed using the same external validation set for all models, ensuring no overlap between training and validation data points. To assess the robustness and variability of these metrics, we iteratively repeated this process 10 times per dataset, each time with a unique random split between training and test sets. All compounds were featurized using 2048-bit, radius 4 Extended Connectivity Fingerprints (ECPF) , computed via RDKit . The gradient boosting models were developed with the XGBoost package 25 using default parameters.