Fig. 2.

The performance of the customized SFs built by 3 ML algorithms (SVM, XGBoost and RF) and 2 traditional SFs (Glide SP and ChemPLP) on the Dataset I and their 95% confidence intervals by 10,000 bootstrapping for 3 metrics (ROC AUC, EF at 1% level and F1 Score). For the SF labels in this figure, ‘sp’ and ‘chemplp’ represent the docking methods (Glide SP and Gold ChemPLP) used for binding pose generation, ‘free’ and ‘all’ represent the descriptor combinations, and ‘svm’, ‘xgb’ and ‘rf’ are the ML algorithms used for modelling