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深度学习模型用于预测人类细胞染色体P450的抑制和诱导

Zhaodi Xiao¹, Hajime Hirao¹

¹School of Medicine, and Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P. R. China.

Journal of chemical information and modeling

|September 18, 2025

概括

这项研究引入了先进的深度学习模型,以预测与细胞染色体P450酶 (CYPs) 的药物相互作用. 这些计算工具通过有效识别潜在的CYP抑制剂和诱导剂来改善药物安全性评估.

科学领域:

药理学和毒理学药理学和毒理学
计算化学计算化学
药物开发药物开发

背景情况:

细胞染色体P450酶 (CYPs) 对药物代谢至关重要,因此对其抑制和诱导的预测对于药物安全至关重要.
识别CYP调节器的实验方法耗时且昂贵,需要高效的in silico方法.

研究的目的:

开发先进的深度学习模型来预测CYP抑制和诱导.
为了确定与CYP调节相关的结构特征.
预测人类孕妇X受体 (hPXR) 激活,这是CYP诱导的关键机制.

主要方法:

深度神经网络与主要成分分析 (PCA) 和合成少数超样本技术 (SMOTE) 的整合,用于CYP抑制预测.
开发一种新的分类模型来分类CYP抑制强度 (强度,中度,非抑制剂).
应用统计分析来识别CYP抑制和诱导的结构性警报 (SAs).
创建一个深度学习模型来预测hPXR激活.

主要成果:

深度学习模型显示了对CYP抑制的优异预测性能.
该分类模型准确地区分了关键CYP的强度,中度和非抑制剂.
确定了与CYP抑制和CYP3A4诱导相关的特定结构性警报.
该hPXR激活预测模型取得了良好的表现.

结论:

先进的深度学习模型提供了有效和准确的CYP抑制和诱导的in silico预测.
识别的结构性警报为药物设计和安全评估提供了宝贵的见解.
预测hPXR激活有助于理解和减轻CYP诱导风险.

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