The more molecules we remove from the data set each time we train, the less reproducible the set of selected MD is. Such a dependence is shown in Fig. . The dependence of the average number of the MD selected in both experiments for all pairs of experiments is shown. Even if we remove only one molecule, the MD selection will not be reproducible. In addition, a typical dependence of accuracy on the number of MD is shown in Fig. . The average of 200 repeats is shown, confidence intervals are too narrow to be shown. It can be seen that the prediction error (as expected) decreases with increasing number of MD, but with a further increase in the number it decreases very slowly. Based on this, we decided to select 10 MD. Fig. . A -dependence of the average size of the intersection between sets of selected MD for all pairs of repeats with an altered data set on the number of molecules randomly excluded from the data set during each repeat (1 -without preliminary removal of highly correlated MD; 2 -with preliminary removal of MD with the Pearson correlation coefficient r > 0.8 with any other MD); B -dependence of cross-validation accuracy on the number of MD.