After establishing a distinct division between the two populations and applying an appropriate feature selection method, Models A and B (see Figure ) were trained using the logistic regression (LR), random forest classification (RFC), and support vector machine (SVM) algorithms. A training set for acidic and basic molecules was used for each model and was evaluated using a test set consisting of 20% of the population. In addition, the two external sets were validated using the experimental data obtained by Disdier et al. (External Set 1) and Fauchère and Pliška (External Set 2). 126 Predictions were made to determine which formalism best modeled the lipophilicity of the inputs, and the results were collected in confusion matrices. The performance of each marker was evaluated by calculating its accuracy, specificity, and sensitivity. It is observed that most of the calculated accuracies for our test set have high values (between 0.8 and 0.95), denoting that these classification models manage to distinguish relatively well which molecules best fit with logDEq.1 and logDEq.2. However, the sensitivity decreased in the test set of acidic molecules, indicating that the models had difficulties in detecting molecules that fit logDEq.2 (Figure ). External Set 1 exhibited good performance, with all models showing similar accuracies, sensitivities, and specificities to those evaluated in the Test Set (Figure ).