DVMMHGNN:一个双视图多模态异构图神经网络,用于微生物信息化药物重定位的对比学习
Huan Li1, YingZhe Bai1,2, Yang Lv3
1State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China.
Journal of chemical information and modeling
|February 23, 2026
概括
这项研究介绍了DVMMHGNN,这是一种用于药物重定向的新型计算框架,它集成了微生物数据. 它准确地预测了药物与疾病的关联,增强了制药开发策略.
科学领域:
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 药物重新定位 (DR) 通过为现有药物找到新的用途来加速制药发展.
- 准确识别药物与疾病的关联是至关重要的,但由于复杂的生物相互作用,具有挑战性.
- 当前的方法往往忽视了微生物群的调节作用,并在数据集成中与语义一致性作斗争.
研究的目的:
- 提出DVMMHGNN,一个微生物知情的异构图对比学习框架,用于增强药物重定位.
- 解决现有的计算方法的局限性,特别是关于微生物群的影响和语义一致性的问题.
主要方法:
- 开发了DVMMHGNN,使用异质图对比学习框架集成结构和元路径信息.
- 采用多式联运特征融合模块用于跨式联运实体嵌入.
- 使用图形掩盖的自动编码器进行高阶表示学习.
- 在结构和元路径层面应用对比学习以增强语义连贯性.
主要成果:
- 在预测药物疾病关联方面,DVMMHGNN显著超过了九种最先进的方法.
- 取得了卓越的性能指标,包括AUC,AUPR和F1分数.
- 废弃性研究证实了单个模型组件的有效性.
结论:
- DVMMHGNN提供了一种强大的方法,用于微生物信息的药物重定位,提高预测准确度.
- 该框架有效地捕捉了复杂的生物语义,以便更好地识别药物和疾病的关联.
- DVMMHGNN有可能发现新的药物指示并指导治疗策略.
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