多知识图和多视图实体特征学习用于预测与药物相关的副作用
Ping Xuan1,2, Tianhong Cheng1, Hui Cui3
1School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
Journal of chemical information and modeling
|May 6, 2025
概括
这项研究引入了MVDSA,这是通过整合多个知识图来预测药物副作用的新模型. 通过准确识别潜在的不良事件,MVDSA提高了药物安全性,并加速了药物开发.
科学领域:
- 药理学和生物信息学 药理学和生物信息学
- 计算机化药物发现技术
- 医疗保健中的人工智能
背景情况:
- 预测药物的副作用对于患者安全和有效的药物开发至关重要.
- 现有的基于图形的方法往往无法充分利用各种知识图形拓和语义的全部潜力.
- 需要先进的计算模型,可以整合多视图信息,以准确预测药物副作用关联.
研究的目的:
- 提出MVDSA,一种用于药物副作用关联预测的新型多视图模型.
- 为了有效地整合多个关系语义,局部图表拓和多视图实体对特征.
- 提高计算药物副作用预测的准确性和可靠性.
主要方法:
- 基于药物/副作用相似性和已知的关联,构建两个知识图.
- 开发一个空间敏感的学习策略,使用关系式语义编码器来进行自适应性特征学习.
- 实现连接敏感尾部实体注意力机制和知识图层级注意力机制,用于特征融合.
- 使用多视图增强的多层感知器 (MLP) 进行最终的关联预测.
主要成果:
- 在药物副作用关联预测方面,MVDSA显著超过了10种最先进的方法.
- 废弃性研究证实了拟议组件的有效性,包括注意力机制和多视角学习.
- 案例研究表明,MVDSA能够识别特定药物的潜在不良药物反应.
结论:
- 通过整合多样化的知识图信息,MVDSA提供了一种强大而有效的方法来预测药物副作用的关联.
- 该模型能够捕捉复杂的关系和多视图功能,提高了其预测能力.
- MVDSA对推进计算药物安全性评估和帮助制药研究具有前途.
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