Computational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method

Cytochrome P450 (CYP) is the most important drug-metabolizing enzyme in human beings. Each CYP isoform is able to metabolize a large number of compounds, and if patients take more than one drugs during the treatment, it is possible that some drugs would be metabolized by the same CYP isoform, leading to potential drug-drug interactions and side effects. Therefore, it is necessary to investigate the isoform specificity of CYP substrates. In this study, we constructed a data set consisting of 10 major CYP isoforms associated with 776 substrates, and used machine learning methods to construct the predictive models based on the features of structural and physicochemical properties of substrates. We also proposed a new method called Improved Bayesian method, which is suitable for small data sets and is able to construct more stable and accurate predictive models compared with other traditional machine learning models. Based on this method, the predictive performance of our method got the accuracy of 86% for the independent test, which was significantly better to the existing models. We believe that our proposed method will facilitate the understanding of drug metabolisms and help the large-scale analysis of drug-drug interactions.