学习多种类型的邻近节点属性和语义通过异质图形变压器和多视图注意力与药物相关的副作用预测
Ping Xuan1,2, Peiru Li1, Hui Cui3
1School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China.
Molecules (Basel, Switzerland)
|September 28, 2023
概括
使用新型异质图形变压器和囊网络方法 (TCSD) 预测药物副作用可以降低药物开发成本. TCSD有效地整合了多视图信息,以准确预测副作用与药物相关性.
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:
- 药物的副作用是临床试验失败的主要原因,增加了开发成本.
- 准确预测药物副作用对于有效的药物发现和开发至关重要.
研究的目的:
- 提出一种新的方法,TCSD,用于预测药物副作用的关联.
- 通过提高副作用预测的准确性来降低药物开发成本.
主要方法:
- 开发了一个基于异质图形变压器的上下文表示学习模块,用于编码多个节点类型之间的拓和上下文关系.
- 实施了一种策略来衡量邻近节点的重要性,并纳入多样化的连接语义.
- 利用囊网络从异质图表进行双向多视图特征学习.
主要成果:
- 与公共数据集上最先进的方法相比,TCSD模型显示出更高的性能.
- 除研究证实了异质图形变压器,对对属性学习和注意力机制的有效性.
- 案例研究表明,TCSD有能力识别许多药物的潜在药物相关副作用.
结论:
- TCSD提供了一种有效的方法来预测药物副作用的相关性.
- 该方法推断候选副作用的能力可以帮助药物的重新用途和安全性评估.
- 这种方法有可能显著降低药物开发成本和时间表.
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