For building the baseline models, we utilized the scikit-learn package and constructed 144 proteochemometric models employing both Random Forest and XGBoost techniques with 100 trees each, employing default settings for maximum tree depth (max_depth) and split quality measurement functions (criterion). Stratified 5-fold cross-validation was applied to ensure reliable model validation and to assess the generalizability of each model. The performance of these models was evaluated through stratified 5-fold cross-validation, focusing on R 2 and mean absolute error (MAE) as our primary metrics.