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HTCL-DDI:一种分层的三视图对比学习框架,用于药物相互作用预测
Ran Zhang1,2, Xuezhi Wang1,2, Pengfei Wang1,2
1Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China.
Briefings in bioinformatics
|September 24, 2023
概括
这项研究引入了一种新的等级三视图对比学习框架,用于药物相互作用 (DDI) 预测. 新方法显著提高了DDI预测的准确性,提高了药物安全性,并确定了协同作用的药物组合.
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.
- 人工智能的人工智能
背景情况:
- 药物相互作用 (DDI) 的预测对于识别药物组合的潜在风险至关重要.
- 现有的计算方法通常使用单视图范式,限制它们捕获复杂DDI模式的能力.
研究的目的:
- 为药物相互作用预测 (HTCL-DDI) 提出一个新的等级三视图对比学习框架.
- 通过整合分子,结构和语义观点来增强药物表示学习.
主要方法:
- 为分子视图开发了一种双重注意意识网络,以聚合分子内信息.
- 利用单跳邻近信息 (结构视图) 和高阶语义关系 (语义视图) 进行分子间分析.
- 实施对比学习以改善药物表示和模型稳定性.
主要成果:
- 在三个现实数据集上,HTCL-DDI显示了与最先进的方法相比的显著改进.
- 该框架有效地模拟复杂的信息,以准确预测DDI.
结论:
- HTCL-DDI为DDI预测提供了一种强大而有效的方法.
- 这些发现为确保药物安全和确定协同作用的药物组合开辟了新的途径.
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