LiProS: FAIR simulation workflow to predict accurate lipophilicity profiles for small molecules

Abstract

The consideration of the ionic partition coefficient in estimating pH-dependent lipophilicity profiles for small molecules has been previously emphasized through classification Machine Learning protocols. In alignment with the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data to enhance data management and sharing, we introduce LiProS: a FAIR workflow accessible via Google Colab. LiProS assists researchers in efficiently determining the appropriate pH-dependent lipophilicity profile based on the SMILES code of their molecules of interest. LiProS demonstrated its applicability in discerning the most suitable lipophilicity formalism based on small structural variations in potential cases of structure-based drug design.