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MPHGCL-DDI:基于元路径的异构图对比学习,用于药物相互作用预测
Baofang Hu1, Zhenmei Yu1, Mingke Li2
1School of Data and Computer Science, Shandong Women's University, Jinan 250030, China.
Molecules (Basel, Switzerland)
|June 19, 2024
概括
预测药物相互作用 (DDI) 对患者安全至关重要. 一个新的基于元路径的异质图对比学习模型,MPHGCL-DDI,通过分析药物连接和结合蛋白质-蛋白质相互作用来改善DDI预测.
科学领域:
- 药理学和计算机生物学
- 人工智能在医学中的应用
背景情况:
- 组合药物治疗可以导致不良药物相互作用 (DDI).
- 准确的DDI预测对于提高药物安全性和减轻风险至关重要.
- 现有的深度学习模型有希望,但需要提高性能.
研究的目的:
- 提出一种基于元路径的新型异质图对比学习模型 (MPHGCL-DDI),以改进DDI事件预测.
- 利用异质图表表示学习和对比学习来提高DDI预测的准确性.
- 纳入间接药物信息,例如蛋白质与蛋白质相互作用 (PPI),以揭示潜在的药物关系.
主要方法:
- 构建了一个包含药物信息和蛋白质与蛋白质相互作用 (PPI) 的异质图.
- 开发了一种基于元路径的方法来创建两个对比的视图:平均图形视图和增强图形视图.
- 实施了多损失培训策略,结合了多关系预测损失,无监督对比损失和监督对比损失.
- 在增强图表视图中应用了三个级别的数据增强.
主要成果:
- MPHGCL-DDI在三个不同的任务和两个数据集中在DDI事件预测方面表现出卓越的表现.
- 该模型有效地利用元路径和对比学习来捕捉复杂的药物关系.
- 纳入PPI数据揭示了潜在的药物关联,进一步提高了预测的准确性.
结论:
- MPHGCL-DDI代表了DDI预测的重大进步,超过了现有的最先进的方法.
- 拟议的模型提供了一个强大的框架,通过整合异质图形学习和对比学习来预测药物相互作用.
- 这种方法有可能改善组合疗法中的药物安全性.
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