基于图形对比学习和双视图融合的药物相互作用预测
Shanyang Ding1, Dongjiang Niu1, Mingxuan Li1
1College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China.
Computational biology and chemistry
|March 26, 2025
概括
本研究介绍了GDF-DDI,这是一种用于预测药物相互作用 (DDI) 的新型深度学习模型. 通过融合知识图和分子结构,它提高了尽管数据限制的预测准确性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 药物相互作用 (DDI) 是患者危害的重要原因之一.
- 预测DDI对于药物的安全性和有效性至关重要.
- 现有的用于DDI预测的深度学习方法面临着杂,不完整和有限数据集的挑战.
研究的目的:
- 为准确的DDI预测开发一个先进的深度学习模型.
- 为了提高药物代表性,利用知识图形网络和分子结构.
- 为了解决现有的DDI数据集中的数据限制.
主要方法:
- 提出了一个名为GDF-DDI的双视图融合模型.
- 使用知识图和药物相似性网络构建全球信息.
- 在药物嵌入中使用图形卷积运算和分层图形对比学习.
- 利用自主监督学习进行全面的药物嵌入.
- 从两种视角合嵌入,以捕获全球和本地DDI信息.
主要成果:
- 与现有的最先进的基线相比,GDF-DDI模型表现出优越的性能.
- 在两个数据集上的比较实验验证实了模型的有效性.
- 双视图融合方法成功地整合了各种各样的药物信息.
结论:
- 通过有效利用知识图和分子结构,GDF-DDI模型为DDI预测提供了强大的方法.
- 拟议的方法在克服DDI研究中的数据限制方面显示出有希望.
- 这项工作通过增强DDI预测,有助于提高药物安全性.
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