使用可解释的多任务深度学习模型预测细胞染色体P450基质
Jiaojiao Fang1, Yan Tang1, Changda Gong1
1Shanghai 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, Shanghai 200237, China.
Chemical research in toxicology
|August 28, 2024
概括
通过细胞P450 (CYP) 酶预测药物代谢对于药物开发至关重要. 新的多任务学习模型准确地识别CYP基质,改善早期药物安全性评估.
科学领域:
- 生物化学 生物化学
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
背景情况:
- 细胞染色体P450 (P450或CYP) 是关键的I期代谢酶,处理大约75%的治疗药物.
- 通过CYP介导的新陈代谢与有毒代谢物生成和药物相互作用有关,因此需要预测工具.
研究的目的:
- 开发和评估多任务学习模型,同时预测五种主要药物代谢P450酶 (CYP3A4,2C9,2C19,2D6,1A2) 的基质.
- 通过准确识别潜在的P450基质,增强早期药物开发.
主要方法:
- 使用指纹和图形神经网络构建多任务学习模型.
- 对多个CYP酶收集的基质数据集进行培训和验证.
- 应用沙普利的添加式解释和注意力机制用于亚结构识别.
主要成果:
- 多任务模型实现了卓越的性能,测试组的平均AUC为90.8%,优于单任务和传统机器学习模型.
- 该模型显示出强大的性能,即使对CYP1A2,2C9和2C19等酶的基质数据有限.
- 与P450基质相关的关键子结构被确定并验证.
结论:
- 多任务学习,特别是图形神经网络,为预测P450药物代谢提供了一种强大的方法.
- 开发的模型为评估药物相互作用和代谢负债在药物发现早期提供了宝贵的见解.
- 可解释性方法有助于理解P450-基质相互作用的结构基础.
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