In this stage, our main objective was to select the optimal subset of RDK7 fingerprint, building the prediction model based on two criteria. Firstly, the model used for selection had to achieve the highest average F1-score in cross-validation with significant differences based on the Wilcoxon signed-rank test. Secondly, the model used for feature selection should yield the result with the minimum number of fingerprints to accelerate the optimization step. On the other hand, the XGBoost and the Logistic Regression achieved the highest average F1-score among all the models, except for the baseline model. However, according to pairwise assessment of these two models (S3 Figure Supporting information), there was no statistically significant difference (p > 0.05). The second criterion, aimed at reducing computational resources by minimizing the number of features, was taken into consideration. The XGBoost algorithm had 533 bits, a lower number of features than the Logistic Regression algorithm, which had 744 bits. Therefore, the XGBoost algorithm was selected to reduce the dimension of the RDK7 dataset. The results of the features selection comparison are illustrated in S4 Table (Supporting information).