In Fig. , a heatmap given that shows the Pearson correlation coefficient r between some MD for this data set. It can be seen that MD, which at first glance are almost unrelated to each other, are often correlated. For example, the number of methoxy groups (fr_methoxy) and a topological MD characterizing the contribution of polar atoms to the total surface of the molecule (VSA_EState9) are strongly correlated. For each pair of correlated MD, we can arbitrarily remove one of the two. But if we do this, then our qualitative conclusions based on the set of MD may also change depending on which of them we remove. However, we considered such a reduction of the MD set. We removed from the original set all MD having r > 0.8 with any of the others. There were 49.9 ± 2.0 MD left (confidence interval, p = 0.95, N = 200). With this approach to preselection of the MD, we conducted the same experiments in order to evaluate the reproducibility. excluded each time). Reproducibility was slightly improved, as expected, but this approach includes a virtually random removal of half of the MD (from a pair of correlated ones, we randomly choose which one to remove). We do this in a reproducible way (for the same pair of correlated MD, the same MD is removed each time). However, the reproducibility is still not very good and it is clear that as the number of molecules removed from the data set increases, the reproducibility also decreases. Thus, the problem of the selected MD set not being reproducible across the data set changes cannot be explained solely by the presence of highly correlated MD.