Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

Unexpected metabolism in modification
and conjugation
phases can
lead to the failure of many late-stage drug candidates or even withdrawal
of approved drugs. Thus, it is critical to predict the sites of metabolism
(SoM) for enzymes, which interact with drug-like molecules, in the
early stages of the research. This study presents methods for predicting
the isoform-specific metabolism for human AOs, FMOs, and UGTs and
general CYP metabolism for preclinical species. The models use semi-empirical
quantum mechanical simulations, validated using experimentally obtained
data and DFT calculations, to estimate the reactivity of each SoM
in the context of the whole molecule. Ligand-based models, trained
and tested using high-quality regioselectivity data, combine the reactivity
of the potential SoM with the orientation and steric effects of the
binding pockets of the different enzyme isoforms. The resulting models
achieve κ values of up to 0.94 and AUC of up to 0.92.