To Pass or Not To Pass: Predicting the Blood–Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory

Predicting
the ability of chemical species to cross the blood–brain
barrier (BBB) is an active field of research for development and mechanistic
understanding in the pharmaceutical industry. Here, we report the
BBB permeability of a large data set of compounds by incorporating
molecular solvation energy descriptors computed by the 3D-RISM-KH
molecular solvation theory. We have been able to show, for the first
time, that the computed excess chemical potential in different solvents
can be successfully used to predict permeability of compounds in a
binary manner (yes/no) via a minimum-descriptor-based model. Our findings
successfully combine the molecular solvation theory with the machine
learning approach to address one of the most daunting challenges in
predictive structure–activity relationship modeling. The workflow
presented in this work is simple enough to be used by nonexperts with
ease.