MDL-HTI:一种多模式深度学习方法,用于预测草药-目标相互作用
Lianzhong Zhang1, Xiumin Shi2, Xiaohong Deng3
1School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
Interdisciplinary sciences, computational life sciences
|October 28, 2025
概括
我们开发了MDL-HTI,这是中国传统医学 (TCM) 的新计算框架,集成图形学习和生物数据来预测草药向相互作用 (HTIs). 这种方法增强了对TCM药理学的理解,并加速了药物发现.
科学领域:
- 计算药理学是一种计算药理学.
- 生物信息学是一种生物信息学.
- 传统中医 (TCM) 研究的研究.
背景情况:
- 传统中医 (TCM) 提供了独特的治疗原则和庞大的药用资源,吸引了全球医疗的兴趣.
- 了解草药向相互作用 (HTIs) 对于阐明TCM的药理机制和治疗效果至关重要.
- 目前用于识别HTIs的方法有限,并没有充分利用现有的生物数据.
研究的目的:
- 开发一种新的计算框架,MDL-HTI,以更有效地预测草药点相互作用 (HTIs).
- 将异质图形学习与多式生物数据相结合,以加强HTI识别.
- 克服当前方法在利用全面的生物信息中对TCM研究的局限性.
主要方法:
- 拟议的MDL-HTI,一个框架,结合异构图形学习 (使用多视图异构关系嵌入 - MV-HRE) 和生物多式联络信息网络.
- MV-HRE从图表,元路径和社区中提取结构模式.
- 一个具有自我注意力的关系预测网络动态地融合了用于HTI识别的特征.
主要成果:
- 在预测HTIs方面,MDL-HTI显著超过了最先进的基线方法.
- 案例研究验证证实了MDL-HTI的有效性,作为识别潜在的草药向相互作用的工具.
- 该模型展示了复杂的草药-目标关系的强大预测能力.
结论:
- 通过整合拓学习和多式生物数据,MDL-HTI为TCM药理学建立了一个新的计算范式.
- 该框架为阐明TCM机制和发现多目标草药提供了一个强大的平台.
- 在精密医学中,MDL-HTI具有潜在的应用,可以降低实验成本并改善治疗结果.
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