Upon completion of model-training with the five-fold cross-validation, the optimal parameters and the corresponding best models are achieved based on highest F1-score in Equation (1). Thus, the combination of four ECFP fingerprints, different random splits of the dataset, and different machine-learning methods (KNN, SVM, RF, GBM, DNN2, and DNN3) will totally offer 328 trained models with the optimal parameters in Table S1. Subsequently, all those models are evaluated on the test set with the following metrics: accuracy, precision, specificity, sensitivity, Matthews correlation coefficient (MCC) and F1-score (Equations 1–6), which are also listed in Table S1.