MGRL-DDI:用于准确预测药物相互作用的多视图表示学习
Peng Xiong1, Hu Chen1, Jiaxu Zhou1
1College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Journal of chemical information and modeling
|September 3, 2025
概括
预测药物相互作用对于患者的安全至关重要. 一个新的多视图表示学习框架,MGRL-DDI,从多个角度有效地建模药物结构,提高DDI预测的准确性.
科学领域:
- 计算化学
- 药理学
- 生物信息学
背景情况:
- 药物相互作用 (DDI) 构成重大临床挑战,影响患者的安全性和治疗疗效.
- 目前的预测方法受到单一视图药物表示的限制,无法捕捉复杂的药物特性.
研究的目的:
- 开发一种先进的药物相互作用 (DDI) 预测框架.
- 在DDI预测中克服单视图药物表示的局限性.
主要方法:
- 提出了MGRL-DDI,一个多视图表示学习框架.
- 整合了三种互补的药物结构视图:三维分子图,图形图和分子图.
- 引入了多视图融合模块,以组合跨结构维度的信息.
主要成果:
- 与现有方法相比,MGRL-DDI在DDI预测方面表现优异.
- 在热启动和冷启动场景中实现了持续的改进.
- 突出了多视图结构建模用于DDI预测的有效性.
结论:
- 多视图表示学习为药物结构建模提供了更全面的方法.
- MGRL-DDI显著提高了药物相互作用预测的准确性和稳定性.
- 拟议的框架有望改善临床实践中的患者安全.
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