通过双特征提取和交叉双域解码提高药物与疾病关联预测,用于通过双特征提取和交叉双域解码重新定位药物
Enqiang Zhu1, Xiang Li1, Chanjuan Liu2
1Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China.
Journal of chemical information and modeling
|April 25, 2025
概括
本研究介绍了双特征药物重新定位神经网络 (DFDRNN),通过分析药物疾病关系来改善药物重新定位. 这种新型模型提高了确定现有药物的新用途的准确性,优于目前的方法.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 药物重新定位通过确定现有药物的新治疗用途,为药物开发提供了一种具有成本效益的方法.
- 当前的方法往往忽略了药物和疾病特征空间之间的相互关系,导致偏见的关联信息.
- 准确编码药物和疾病特征对于有效的药物重新定位至关重要.
研究的目的:
- 提出一种新的神经网络模型,即双特征药物重定位神经网络 (DFDRNN),用于更准确的药物疾病关联预测.
- 通过将药物疾病网络的双特征提取纳入,解决现有方法的局限性.
- 通过考虑相似性和关联特征来增强药物和疾病的编码.
主要方法:
- 开发了双特征药物重定位神经网络 (DFDRNN) 模型.
- 使用自我注意机制来提取邻居的特征.
- 集成的单域 (SDDFE) 和跨域 (CDDFE) 双功能提取模块.
- 设计了一种跨双域解码器,用于预测药物与疾病的关联.
主要成果:
- 与六种最先进的方法相比,DFDRNN模型在四个基准数据集上表现出卓越的性能.
- 获得了平均AUROC的0.946和平均AUPR的0.597.
- 案例研究证实了该模型在现实世界药物重新定位场景中的适用性.
结论:
- 拟议的DFDRNN模型通过有效地挖掘药物-疾病网络的双重特征,显著提高了药物重新定位的准确性.
- DFDRNN提供了一种有前途的方法来识别现有药物的新指示.
- 该模型的性能和现实世界的适用性突显了其加速药物发现和开发的潜力.
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