MRDDA:一种多关系图形神经网络,用于药物疾病关联预测和预测
Congzhou Chen1, Yaozheng Zhou1, Yinghong Li1
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Journal of translational medicine
|July 8, 2025
概括
这项研究介绍了MRDDA,一种用于药物重新定位的新型图形神经网络模型. MRDDA准确地预测了药物与疾病的关联,确定了现有药物的新治疗用途,并加速了药物开发.
科学领域:
- 计算生物学是一种计算生物学.
- 网络药理学 网络药理学
- 药物发现 药物发现
背景情况:
- 药物重新定位加速开发并降低成本.
- 计算方法越来越多地用于药物疾病关联 (DDA) 预测.
- 由于复杂的生物网络相互作用,预测DDA具有挑战性.
研究的目的:
- 引入MRDDA,一种用于药物重新定位的新型图形神经网络模型.
- 使用多关联数据准确预测药物疾病关联.
- 提高现有药物的新治疗用途识别的效率.
主要方法:
- 开发了一种混合图形卷积框架,用于药物和疾病表示.
- 在高阶拓表示中采用基于元路径的方法.
- 集成的多层嵌入使用层级注意力机制.
主要成果:
- 在三个基准数据集中,MRDDA在DDA预测方面取得了卓越的表现.
- 确定了阿尔茨海默病和乳腺癌的有前途的候选药物.
- 分子对接实验验证药物向相互作用,支持实验研究.
结论:
- 通过图形神经网络,MRDDA提供了用于药物重新定位的创新框架.
- 该模型有效地整合了多关系数据,以准确预测DDA.
- 这种方法加速了药物开发,并降低了制药行业的成本.
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