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使用深度学习方法和子结构模式识别来预测细胞染色体P450抑制

Zhaoyang Chen¹, Le Zhang¹, Pei Zhang¹

¹Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.

Journal of chemical information and modeling

|October 21, 2023

概括

新的深度学习模型预测了细胞染色体P450 (CYP) 酶的化学抑制,这对药物代谢和安全至关重要. 这些可访问的工具可以识别潜在的药物相互作用,并提供有关抑制机制的见解.

科学领域:

生物化学生化学
药理学药理学是指药理学的学科.
计算化学的计算化学

背景情况:

细胞染色体P450 (CYP) 酶对药物代谢至关重要,约有75%的代谢反应中介.
CYP1A2,CYP2C9,CYP2C19,CYP2D6和CYP3A4是参与大多数药物代谢和药物不良反应的关键异构体.
准确预测CYP抑制在药物发现和食品工业中至关重要,但现有的计算模型存在局限性.

研究的目的:

开发准确和可访问的深度学习模型,用于预测主要的细胞染色体P450异型体的化学抑制.
分析与CYP450抑制相关的结构特征,并确定结构警报.
为研究人员提供开源工具,以预测CYP抑制并了解其机制.

主要方法:

使用Python,Keras和TensorFlow开发了深度学习模型,用于预测CYP1A2,CYP2C9,CYP2C19,CYP2D6和CYP3A4.4的抑制.
在PubChem生物测试数据库中的85,715种化合物的大型数据集上训练模型.
执行外部验证并分析抑制剂的结构特征,以检测结构警报.

主要成果:

获得了高的外部验证AUC值:0.97 (CYP1A2),0.94 (CYP2C9),0.94 (CYP2C19),0.96 (CYP2D6) 和0.94 (CYP3A4).这些值在CYP1A2中是0.97 (CYP1A2),在CYP2C9中是0.94 (CYP2C19),在CYP2D6中是0.96 (CYP2D6) 和在CYP3A4中是0.94.
开发了一个网络服务器 (CYPi-DNN预测器) 以免费访问预测模型.
通过CYPi-SAdetector识别并提供结构性警报 (SAs),提供对CYP450抑制机制的见解.

结论:

开发的深度学习模型为预测CYP450抑制剂提供了强大且易于使用的工具.
已识别的结构性警报为了解CYP450抑制机制提供了有价值的信息.
这些资源通过早期预测潜在的药物相互作用和不良反应来促进更安全的药物发现和开发.

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