In the next step, feature extraction was conducted with feature importance algorithms derived from the Random Forest algorithm. Then, 10 different machine learning models were applied for all molecular features, including logistic regression (Logic), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra tree (ExT), Adaboost (ADA), Gradient Boosting (Grad), XGBoost (XGB), CatBoost (Catbst), and Multilayer Perceptron (MLP). The Wilcoxon signed-rank test conducted a meta-analysis to select the optimal type of feature set. This was based on 10-fold internal cross-validation repeated three times (3x10 RepeatedStratifiedKFold) with the evaluation metric being F1-score. The outcome yielded the optimal feature set that best suited the model.