Consequently, the delimiter '0.2' showed an adequate balance between these two parameters and was selected as our cut-off value (see Figure ). Molecules with values of d3 > 0.2 showed an improvement in lipophilicity modeling using Eq. 2. On the other hand, entries that had negative d3 values or that fell into the range 0.2 < d3 < 0 were classified as molecules where the difference between both models was negligible, and thus were classified as better fitted using logDEq.1 due to its easy implementation (it does not depend on P I app , resulting in less computational effort and fewer experimental parameters). Higher thresholds significantly decreased the population in logDEq.2, while lower values reduced the structural divergence between molecules in logDEq.1 and logDEq.2, making it more difficult to find descriptors that can differentiate between both populations. The value '0.5' was also tested because a local maximum of descriptors with p < 0.05 was observed at this point (see Figure ). Furthermore, this value is of experimental interest, because logPN measurements of substances with different techniques tend to vary by less than 0.5 logP units (using the Shake-Flask method as a reference), which is considered as a parameter indicating that the experimental techniques are not equivalent. However, this extreme value and the descriptors selected (see Table ) showed poor performance in the ML models tested, especially with External Set 1 (see Figure ). This phenomenon can be explained because this d3 delimiter has a very small logDEq.2 population, thus the datasets are extremely unbalanced and the robustness of the models is reduced. On the other hand, the accuracy of experimental methods, even using different techniques, rounds at values less than 0.2 logP units. Therefore, we continued to train the ML models using the d3 > 0.2 cut-off value to determine tendencies among the selected descriptors via the feature selection methods and to evaluate the performance of the ML algorithms.