转移学习增强图形神经网络用于阿尔代氧化酶代谢预测及其实验应用
Jiacheng Xiong1,2, Rongrong Cui1,2, Zhaojun Li3,4
1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Acta pharmaceutica Sinica. B
|February 7, 2024
概括
一个新的计算模型,AOMP,准确地预测了阿尔德海德氧化酶 (AOX) 代谢,通过早期识别潜在的负债来帮助药物发现. 这种工具有助于通过评估候选药物对AOX介导风险来避免临床失败.
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:
- 氧化酶 (AOX) 是一种关键的肝酶,可以代谢药物和异生菌.
- AOX活性可能导致有毒代谢物和快速药物清除,导致临床试验失败.
- 预测性计算模型对于评估早期药物发现中的AOX代谢风险至关重要.
研究的目的:
- 开发一种新的图形神经网络 (GNN) 模型,AOMP,用于预测AOX介导的新陈代谢.
- 在AOMP模型中整合基质分类和代谢部位预测.
- 通过从13C NMR数据中转移学习来提高AOMP性能.
主要方法:
- 开发一个图形神经网络 (AOMP) 用于AOX代谢预测.
- 集成基质/非基质分类和代谢部位预测任务.
- 从13C NMR数据中转移学习的应用,以提高模型准确性.
主要成果:
- 在交叉验证和外部测试方面,AOMP显著优于现有的基准方法.
- 对酶抑制剂片段的系统评估确定了四个具有AOX代谢责任的新支架.
- 实验验证证证实了确定的AOX代谢负债.
结论:
- AOMP是一种高效的工具,用于预测阿尔德海德氧化酶代谢.
- 该模型有助于在早期药物开发过程中识别和减轻药物代谢负担.
- 一个基于AOMP的可访问的在线预测服务现在可供科学界使用.
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