Sensitivity determines the ability of the model to detect events of the positive class; that is, it indicates the predictive performance of the molecules of the logDEq.2 population (Equation ). On the other hand, the specificity indicates the performance of the model in detecting the negative class, which in this case are the molecules of the logDEq.1 population (Equation ). The accuracy indicates the overall performance in detecting false positives and false negatives (Equation ). 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 [10] 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑇𝑁 𝐹𝑃 + 𝑇𝑁 [11] 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁 [12] Models A and B were tested further using an external set. The experimental lipophilicity measurements made by Disdier et al. consisted of 69 data entries of small molecules with 38 acids, 16 bases, and 15 zwitterions, the latter being discarded for our analysis. To further check the robustness of our models, a second external set of amino acid analogs was evaluated 126 , consisting of 8 entries of histidine (basic amino acid) and 10 entries of tyrosine (acidic amino acid).