XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

Understanding
how xenobiotic molecules are metabolized is important
because it influences the safety, efficacy, and dose of medicines
and how they can be modified to improve these properties. The cytochrome
P450s (CYPs) are proteins responsible for metabolizing 90% of drugs
on the market, and many computational methods can predict which atomic
sites of a moleculesites of metabolism (SOMs)are modified
during CYP-mediated metabolism. This study improves on prior methods
of predicting CYP-mediated SOMs by using new descriptors and machine
learning based on neural networks. The new method, XenoSite, is faster
to train and more accurate by as much as 4% or 5% for some isozymes.
Furthermore, some “incorrect” predictions made by XenoSite
were subsequently validated as correct predictions by revaluation
of the source literature. Moreover, XenoSite output is interpretable
as a probability, which reflects both the confidence of the model
that a particular atom is metabolized and the statistical likelihood
that its prediction for that atom is correct.