Experimental techniques for lipophilicity evaluation such as shake-flask, potentiometric, and chromatographic methods , can measure but do not allow direct identification of the nature of the ionic species involved in the partitioning; hence, the partition of ionic species is measured as an apparent partitioning (P I app ). This experimentally measurable apparent partition coefficient depends on the background salt and compound concentration , and may involve many more complex species, such as ion-pairs and aggregates . Some studies have simplified the P I app to the partition of only ionic organic species (PI) because these methods have been parametrized using experimental P I app values , while other theoretical studies have modeled it using the participation of ion-pairs (PIP) . Recently, an alternative model to ion-pair partitioning has been used by applying the theory of ionic transfer between two immiscible electrolyte solutions (ITIES) , obtaining excellent predictions of experimental logDpH values. Previous experimental trials have also shown the importance of the P I app of ionizable molecules in n-octanol/water systems . Recently, Disdier et al. measured the logDpH at different pH values of a set of 13 compounds via the shake-flask method , where fitted experimental values to lipophilicity formalisms for mono-and poly substituted acids, amphoteric, and zwitterionic species derived on previous theoretical studies. The relevance of P I app for small ionic molecules between aqueous and organic phases has also been studied through interphase transfer mechanisms of substances via ionic partition diagrams as a function of pH obtained through cyclic voltammetry. Despite the lack of a consensus formalism to model logDpH as a function of P I app , and considering that different theoretical approaches have shown similar trends organic chemicals , sorption mechanisms of antimicrobials in the soil , and physicochemical properties of peptides and proteins. Previous studies have evaluated predictions of logDpH using Equations 1 and 2 for a small set of 35 ionizable molecules with computed logPN and logP I app values calculated via an extension of the Miertus-Scrocco-Tomassi solvation model. It has been reported that Equation 1 tends to overestimate the hydrophobicity of the studied molecules, given that the P I app is not considered, whereas Equation 2 predicts a logDpH value closer to the experimental values. This study showed that Equation 2 provided a more exact lipophilicity profile over a wider pH range than Equation . However, no systematized study has been performed to evaluate the importance of considering the ionic partition on the logDpH prediction for large sets of small drug-like molecules at various pH values, although it has been reported that much of the poor performance of some models on blind challenges has been due to the simplification of ignoring the ionic species partition. In this study, our aim is to evaluate the impact of considering the P I app in determining pHdependent lipophilicity profiles of small molecules. We also aim to provide guidance to the scientific community working in early-stage drug design, food, and environmental chemistry, specifically those dealing with ionizable molecules. Our goal is to help researchers determine a priori which pH-dependent lipophilicity profile should be used based solely on structural features of the substance of interest. To this end, we collected the experimental values of logPN, pKa, and logP I app of different compounds at various pH values as well as experimental data of lipophilicitybased applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products and pH-dependent partition in passive samples, which were used to compute logDpH with Equations 1 and 2. The predictions using both equations were then used to compare their performances using statistical parameters. Finally, logistic regression (LR), random forest classification (RFC), and support vector machine (SVM) models were developed to define from the molecular structure which formalism is recommended for modeling pH-dependent lipophilicity profiles.