Xu Minjie, Lu Zhou, Wu Zengrui, Gui Minyan, Liu Guixia, Tang Yun, Li Weihua
Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai200237, China.
Mol Pharm. 2023 Jan 2;20(1):194-205. doi: 10.1021/acs.molpharmaceut.2c00571. Epub 2022 Dec 2.
Cytochrome P450 3A4 (CYP3A4) is one of the major drug metabolizing enzymes in the human body and metabolizes ∼30-50% of clinically used drugs. Inhibition of CYP3A4 must always be considered in the development of new drugs. Time-dependent inhibition (TDI) is an important P450 inhibition type that could cause undesired drug-drug interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient way is of great importance to any new drug discovery effort. Here, we report the development of in silico classification models for prediction of potential CYP3A4 time-dependent inhibitors. On the basis of the CYP3A4 TDI data set that we manually collected from literature and databases, both conventional machine learning and deep learning models were constructed. The comparisons of different sampling strategies, molecular representations, and machine-learning algorithms showed the benefits of a balanced data set and the deep-learning model featured by GraphConv. The generalization ability of the best model was tested by screening an external data set, and the prediction results were validated by biological experiments. In addition, several structural alerts that are relevant to CYP3A4 time-dependent inhibitors were identified via information gain and frequency analysis. We anticipate that our effort would be useful for identification of potential CYP3A4 time-dependent inhibitors in drug discovery and design.
细胞色素P450 3A4（CYP3A4）是人体主要的药物代谢酶之一，可代谢约30%-50%的临床使用药物。在新药研发过程中，必须始终考虑对CYP3A4的抑制作用。时间依赖性抑制（TDI）是一种重要的P450抑制类型，可能导致不良的药物相互作用。因此，通过快速便捷的方法鉴定CYP3A4 TDI对任何新药研发工作都至关重要。在此，我们报告了用于预测潜在CYP3A4时间依赖性抑制剂的计算机分类模型的开发。基于我们从文献和数据库中手动收集的CYP3A4 TDI数据集，构建了传统机器学习模型和深度学习模型。不同采样策略、分子表示和机器学习算法的比较显示了平衡数据集和以GraphConv为特征的深度学习模型的优势。通过筛选外部数据集测试了最佳模型的泛化能力，并通过生物学实验验证了预测结果。此外,通过信息增益和频率分析确定了几个与CYP3A4时间依赖性抑制剂相关的结构警报。我们预计，我们的工作将有助于在药物发现和设计中识别潜在的CYP3A4时间依赖性抑制剂。
