The development of in silico method to predict the inhibitory activity of drug-metabolizing enzyme

Abstract

In this study, we sought to develop in silico models for predicting the inhibitory activity of chemical substances against cytochrome P450 (CYP) based on chemical structure information. We used in vitro experimental values of 326 substances for rat CYPs and 215 substances for human CYPs from the HESS as learning data. The models established by XGboost showed ROC-AUC of 0.8 or more for rat and 0.75 or more for human. In this study, we have developed high-performance and reliable classification models to predict the inhibitory activity of chemical substances against CYPs by in silico methods.