HDN-DDI:使用层次分子图形和增强的双视图表示学习预测药物相互作用的新框架
Jinchen Sun1,2, Haoran Zheng3,4
1School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
BMC bioinformatics
|January 25, 2025
概括
本研究介绍了HDN-DDI,这是一种用于使用可解释的子结构和层次分子图来预测药物相互作用 (DDI) 的新框架. HDN-DDI实现了最先进的准确性,并展示了强大的概括性,以提高患者安全.
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.
- 人工智能在药物发现中的作用
背景情况:
- 药物相互作用 (DDI) 对患者安全构成重大风险,需要可靠的预测方法.
- 基于子结构的DDI预测是非常重要的,因为化学子结构对药物特性的影响.
- 现有的方法难以解释和隔离相关的化学子结构.
研究的目的:
- 开发一种新的框架,HDN-DDI,用于准确和可解释的药物相互作用预测.
- 提高编码效率和药物分子的表现,使用层次分子图表.
- 通过增强的双视图学习方法,加强对交互机制的捕获.
主要方法:
- HDN-DDI使用可解释的基结构提取模块来分解药物分子.
- 药物分子使用创新的层次分子图表来表示.
- 一种增强的双视图学习方法捕获了层次结构和交互信息.
主要成果:
- 在两个基准数据集上,HDN-DDI实现了97.90%和99.38%的精度,实现了最先进的性能.
- 在冷启动设置中观察到显著改善,精度提高了4.96%,F1得分提高了7.08%.
- 该框架在现实世界的应用中展示了对新批准的药物的强有力的概括能力.
结论:
- HDN-DDI提供了准确的DDI预测和强大的概括,有望提高药物安全性和有效性.
- 未来的工作重点是完善分解规则和整合外部知识.
- 该模型的概括能力将在未来的研究中得到保留.
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