RESUMO

Lipophilicity is a physicochemical property with wide relevance in drug design, computational biology, food, environmental and medicinal chemistry. Lipophilicity is commonly expressed as the partition coefficient for neutral molecules, whereas for molecules with ionizable groups, the distribution coefficient (D) at a given pH is used. The logDpH is usually predicted using a pH correction over the logPN using the pKa of ionizable molecules, while often ignoring the apparent ion pair partitioning ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ . In this work, we studied the impact of ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ on the prediction of both the experimental lipophilicity of small molecules and experimental lipophilicity-based applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products, and pH-dependent partition of water contaminants in synthetic passive samples such as silicones. Our findings show that better predictions are obtained by considering the apparent ion pair partitioning. In this context, we developed machine learning algorithms to determine the cases that P I app ${P_{\rm{I}}^{{\rm{app}}} }$ should be considered. The results indicate that small, rigid, and unsaturated molecules with logPN close to zero, which present a significant proportion of ionic species in the aqueous phase, were better modeled using the apparent ion pair partitioning ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ . Finally, our findings can serve as guidance to the scientific community working in early-stage drug design, food, and environmental chemistry.