图表生成和对抗策略增强节点特征学习和自我校准的对对属性编码,用于预测与药物相关的副作用
1Department of Computer Science, School of Engineering, Shantou University, Shantou, China.
Frontiers in pharmacology
|September 21, 2023
概括
我们开发了GGSC,这是一种通过整合各种图形数据和属性来预测与药物相关的副作用的新方法. 这种方法提高了识别潜在不良药物反应的准确性,有助于药物开发.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 预测药物诱导的副作用对于有效的药物开发至关重要.
- 现有的计算方法经常使用异质图形,但未能充分利用多样化的拓和属性.
- 需要先进的方法来整合多图形数据,以改善药物副作用关联预测.
研究的目的:
- 提出GGSC,一种用于药物副作用关联预测的新方法.
- 从多个异质图表中深入整合多样化的拓和属性信息.
- 通过利用药物副作用结节对的自我校准属性来提高预测准确度.
主要方法:
- 构建了两个异质图,其中包含药物,副作用节点以及它们的相似性/关联关系.
- 采用了图形生成对抗网络 (GGSC) 与图形卷积自编码器和多层感知特征区分器用于节点特征学习.
- 集成多个图表表示,使用表示级别的注意力和精细的对对属性,使用基于卷积神经网络的自我校准模块.
主要成果:
- 与最先进的方法相比,GGSC表现出优越的预测性能.
- 除研究证实了拓增强,基于注意力的融合和自我校准学习的有效性.
- 案例研究成功地确定了五种药物的潜在药物相关副作用候选者.
结论:
- 拟议的GGSC方法有效预测药物副作用的关联.
- GGSC有助于选可靠的关联候选人,支持生物发现.
- 这种方法为减少药物开发成本和时间表提供了有价值的工具.
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