Computational Methods and Tools to Predict Cytochrome P450 Metabolism for Drug Discovery

This review describes important recent developments in the computational prediction of cytochrome P450 (CYP) metabolism. In silico models to predict CYP inhibitors, substrates, sites of metabolism and metabolite structures are discussed with a particular focus on freely available software for use in drug discovery.

Abstract

In this review we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e. metabolically labile sites within potential substrates) and metabolite structure predictors. We summarise the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

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