An iterative feature selection method was also tested using the RFE model. The algorithm achieved better performance when the 14 most important variables for acids and the nine most important variables for bases were maintained. The importance of each descriptor posed by the RFE is shown in Figure . Good results were obtained when these descriptors were implemented during the training of the Machine Learning models. However, the accuracy decreased significantly when the Test and External Set 1 were evaluated (see Figure ), indicating that these descriptors did not generate a sufficiently robust model, or that the large number of chosen descriptors (see Table ) may overfit the data. Therefore for the delta and Alogp2 descriptors (Table ). The descriptor delta was calculated at the respective pH of each entry for acids and bases. As expected, this descriptor proved to be the most important in every test carried out in this regard (see Figures ), as it correlates with the prominence of ionic species in both phases. Therefore, the apparent ionic partition becomes more significant for entries with higher delta values (Figures and). This result is very promising, because despite being an experimental descriptor, there are computational methods to determine pKa that include first-principles models 133-136 as well as machine learning tools 137,138 .Thus, the descriptor delta can be automated and easily used to classify molecules according to the lipophilicity formalisms analyzed here. In fact, the root-mean-square error (RMSE) between predicted pKa values using the software ChemAxon and experimental data in our database is just 0.58 log units and the squared coefficient of determination (R 2 ) of 0.95 (see Figure )