Deep Learning Models for Predicting Human Cytochrome P450 Inhibition and Induction

Given the critical roles played by human cytochrome P450 enzymes (CYPs) in drug metabolism, accurately predicting their potential inhibition and induction by drugs and drug candidates is a key objective for improving drug development and safety assessment. Traditional experimental methods for identifying CYP modulators are labor-intensive and costly, underscoring the need for efficient in silico prediction models. In this study, we present an advanced deep learning model for predicting CYP inhibition, with a primary focus on key enzymes involved in drug metabolism: CYP3A4, CYP2D6, CYP1A2, CYP2C9, and CYP2C19. This model integrates deep neural networks with principal component analysis (PCA) and the synthetic minority oversampling technique (SMOTE), and it demonstrates excellent predictive performance. Furthermore, we developed a novel classification model capable of accurately distinguishing compounds as strong inhibitors, moderate inhibitors, or noninhibitors for these CYPs, achieving robust and reliable overall performance. Through statistical analysis, we also identified structural alerts (SAs) associated with CYP inhibition and strong CYP3A4 induction, providing a more precise characterization than previous approaches. Finally, we introduced a novel deep learning-based method specifically designed to predict human pregnane X receptor (hPXR) activation, a major mechanism responsible for CYP induction, which also achieved good performance.