Consequently, one of the aims of this study was to develop a classification algorithm that can differentiate whether the lipophilicity profile of a molecule can be better predicted with logDEq.1 or logDEq.2. However, a significant number of entries indicate that both formalisms compute a similar result compared to their experimental values by yielding d3 values close to 0 (see Figure ). Let us note that we focus on the specific cases with a significant improvement when the P I app of molecules is considered. Therefore, we decided to delimit the conditional d3, indicating that if a molecule exceeds a certain value of d3, it is important to consider its apparent ionic partitioning to predict its lipophilicity. We tested d3 values between 0.1-1 and picked the optimal value based on two main parameters. First, considering that our set was small because we used strictly experimental values in our database, we seek that the population of molecules that best fit with logDEq.2 should be at least 10 %. Then, there should be a sufficient number of descriptors that have statistically proven divergence by WTT (p < 0.05). Thus, Machine Learning algorithms have a larger number of parameters to create predictive models with higher accuracy.