DeepMetab:一个全面的和机理上知情的图形学习框架,用于端到端的药物代谢预测
Yiling Zhou1, Dejun Jiang1, Xiao Wei1
1Xiangya School of Pharmaceutical Sciences, Central South University Changsha 410013 Hunan P.R. China oriental-cds@163.com.
Chemical science
|September 18, 2025
概括
DeepMetab是一个新的AI框架,通过整合多个任务,准确地预测药物代谢. 这促进了药物开发和研究的药理动力学预测.
科学领域:
- 计算化学是一种计算化学.
- 药理动力学 药理动力学
- 人工智能在药物发现中的作用
背景情况:
- 药物代谢预测是复杂的,因为酶机制.
- 当前的计算工具是分散的,缺乏整体的整合性和可解释性.
- 现有的模型专注于孤立的任务,如基质识别或代谢物识别.
研究的目的:
- 介绍DeepMetab,这是一个全面的深度图形学习框架,用于端到端预测CYP450介导药物代谢.
- 将基质分析,代谢部位定位和代谢物生成集成到一个统一的多任务架构中.
- 在药物代谢预测中增强机械忠实性和化学解释性.
主要方法:
- 开发了DeepMetab,这是一个使用多任务架构的深度图形学习框架.
- 采用双标记策略来预测原子和债券级别的反应性.
- 在图形神经网络 (GNN) 骨干中集成多尺度特征 (量子信息,拓) .
- 纳入了专家衍生反应规则,用于代谢物合成的机械一致性.
主要成果:
- 在9种CYP异型中,DeepMetab的表现优于现有的模型,用于基质分析,SOM定位和代谢物生成.
- 在18种FDA批准的药物上实现了100%的TOP-2准确性,用于SOM预测.
- 成功恢复了在训练数据中不存在的经验证实代谢物.
- 通过可视化学习的表示表现来证明可解释性,显示化学特征的辨别能力.
结论:
- DeepMetab提供准确,可解释和端到端的药物代谢预测.
- 该框架将象征性反应规则和深度图形推理连接起来,以实现增强的预测.
- 为临床前研究和药物开发中的监管应用提供了重要的价值.
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