Semi-empirical and machine learning-based prediction of site of metabolisms mediated by aldehyde oxidase

Abstract

Prediction of drug clearance by aldehyde oxidase (AO) catalyzed metabolism has been of great interest in drug discovery. We report a fast, accurate, and fully-automated tool that combines semi-empirical quantum mechanical (SQM) calculations and machine-learning (ML) technique to predict AO-mediated site of metabolism (SOM). We infer that calculated probability scores together with those based on approximate C(sp2)−H bond dissociation energies accomplish accurate and immediate prediction of the number and rank ordering of SOMs. Tests on a series of 150 AO substrates demonstrate that our computational strategy can be applicable to high-throughput screening of metabolically stable compounds.