通过关系变换器和知识蒸增强的图形推理方法,用于药物相关副作用预测
Honglei Bai1, Siyuan Lu1, Tiangang Zhang1,2
1School of Computer Science and Technology, Heilongjiang University, Harbin, China.
iScience
|May 27, 2024
概括
本研究介绍了RKDSP,这是一种图形推理方法,可以准确预测药物的副作用. 通过早期识别潜在的不良事件,RKDSP改善了药物开发,降低了成本和失败风险.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 识别药物的副作用对于成功开发药物和降低成本至关重要.
- 目前的方法可能无法充分利用药物和副作用之间的复杂关系.
研究的目的:
- 提出一种新的图形推理方法,RKDSP,用于准确预测药物的副作用.
- 整合多样化的知识来源,包括语义关系,本地和全球图形知识以及节点属性.
主要方法:
- 构建的药物副作用异质图,包含相似性和关联关系.
- 采用多个关系转换器来从各种元路径视角学习节点特征.
- 利用知识蒸模块用于本地和全球知识获取.
- 开发了一种适应性卷积神经网络,用于编码药物副作用对属性.
主要成果:
- 与现有的最先进的预测方法相比,RKDSP方法显示出更高的性能.
- 多个知识来源和关系学习的融合显著提高了预测准确性.
结论:
- RKDSP提供了一种强大的新方法来预测药物的副作用.
- 这种方法有可能显著减少药物开发失败和相关成本.
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