生物信息学
深度学习
人工智能
药物发现
计算机科学
细胞色素P450
计算生物学
药物重新定位
机器学习
CYP3A4型
过采样
药物开发
机制(生物学)
药品
钥匙(锁)
人工神经网络
药物靶点
光学(聚焦)
卷积神经网络
主成分分析
深层神经网络
训练集
化学
生物信息学
作者
Zhaodi Xiao,Hajime Hirao
标识
DOI:10.1021/acs.jcim.5c01192
摘要
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.
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