在中国传统医学中用可解释图形神经网络量化兼容性机制
Jingqi Zeng1, Xiaobin Jia1,2
1School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
Journal of pharmaceutical analysis
|September 2, 2025
概括
这项研究引入了GraphAI框架,以了解传统中医药 (TCM) 的配方机制. 它确定了Radix Astragali-Rhizoma Phragmitis作为长期COVID-19管理的一个有希望的对.
科学领域:
- 计算生物学
- 药理学
- 医学中的人工智能
背景情况:
- 传统中国医学 (TCM) 涉及草药配方中的复杂相互作用.
- 现有的方法难以完全量化多组件,多目标和多路径机制.
- 需要可解释的框架来分析TCM的兼容性.
研究的目的:
- 开发和验证可解释的图形人工智能 (GraphAI) 框架,用于量化TCM草药配方机制.
- 构建一个多维的TCM知识图 (TCM-MKG) 集成不同的数据模块.
- 确定潜在的治疗草药对和生物活性化合物治疗COVID-19等疾病.
主要方法:
- 构建一个具有七个模块的多维TCM知识图 (TCM-MKG).
- 应用邻居扩散策略来增强复合目标关联.
- 使用带有注意力机制的图形神经网络 (GNN) 和虚拟节点进行可解释的中药公式 (CHF) 建模.
主要成果:
- 该框架成功量化了经典的TCM兼容性作用和发现的病因类型.
- 一个邻居传播策略将复合目标覆盖率从12.0%提高到98.7%.
- 雷迪克斯·阿斯特拉加利-瑞佐玛·弗拉格米蒂斯被确定为COVID-19管理的高度关注的草药对,具有长期COVID-19的潜力.
结论:
- 开发的GraphAI框架为TCM研究提供了一个可扩展和可解释的平台.
- 来自Radix Astragali-Rhizoma Phragmitis的活性化合物显示出神经免疫和解毒作用的潜力.
- 这种方法有助于发现生物活性植物成分及其治疗用途.
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