基于多视图的异构图对比学习用于药物向相互作用预测
Chao Li1, Lichao Zhang1, Guoyi Sun1
1College of Electronic and Information Engineering, Shandong University of Science and Technology, Qianwangang Road No. 579, Huangdao District, Qing Dao, 266590, Shandong, China.
Journal of biomedical informatics
|June 4, 2025
概括
这项研究引入了一种新的多视图基于异质图对比学习用于药物向相互作用预测 (HGCML-DTI) 方法. HGCML-DTI有效地整合了拓和语义信息,大大提高了药物向相互作用预测的准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 的预测对于加速药物发现至关重要.
- 现有的方法往往难以整合拓和语义信息,并保持特征多样性.
- 挑战包括信息整合不足和图形卷积运算中的表示多样性减少.
研究的目的:
- 提出一个新的范式,多视图基于异构图对比学习用于药物向相互作用预测 (HGCML-DTI).
- 解决DTI预测中信息整合不足和代表性多样性减少的局限性.
- 提高学习特征的表达力和区分能力,用于DTI预测.
主要方法:
- 构建药物-蛋白质异质图,并使用加权图卷积网络 (GCN) 来导出节点表示.
- 将药物-蛋白质对 (DPP) 的拓和语义图集成到统一的公共图中.
- 采用多通道图形神经网络和多视图对比学习策略来学习DPP表示并保持多样性.
- 使用多层感知器 (MLP) 进行DTI识别.
主要成果:
- 拟议的HGCML-DTI方法在六个现实数据集中显著优于七个竞争基线.
- 在药物向相互作用 (DTI) 预测任务中表现卓越.
- 验证了结合多视角学习和对比策略的有效性.
结论:
- 通过整合多种信息来源,HGCML-DTI范式有效地解决了DTI预测的关键挑战.
- 该研究强调了多视角学习和对比策略对于推进DTI预测的重要性.
- 拟议的方法在药物发现和开发中比现有的最先进的方法有了显著的改进.
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