通过结构和语义邻居融合进行药物重新定位的丰富邻居对比学习框架
IEEE journal of biomedical and health informatics
|January 12, 2026
概括
本研究介绍了用于药物重新定位的富邻对比学习 (RCL-DR),这是一种通过整合结构和语义数据来增强药物重新定位的新框架. RCL-DR提高了药物疾病关联的预测准确度,加速了治疗发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 医学中的人工智能
背景情况:
- 药物重新定位为发现新疗法提供了一种具有成本效益的方法.
- 现有的图形协作过方法与数据稀疏性和噪声作斗争,限制了其模拟复杂药物-疾病关系的能力.
- 整合结构和语义信息对于提高预测准确性至关重要.
研究的目的:
- 提出一个统一的框架,Rich-Neighborhood Contrastive Learning for Drug Repositioning (RCL-DR),以解决目前药物重新定位方法的局限性.
- 利用结构和语义社区的互补信息来提高对药物疾病关联的预测.
- 改进药物重新定位中的潜在高阶和语义关系的建模.
主要方法:
- 开发了RCL-DR,这是一个框架,将结构和语义社区建模集成到基于LightGCN的协作过骨干中.
- 利用预期最大化 (EM) 算法来优化语义原型.
- 在使用 10 × 10 倍交叉验证的三个公共数据集上进行了实验.
主要成果:
- 在预测药物与疾病的关联方面,RCL-DR显著优于现有的基线方法.
- 获得了0.9419的接收器操作特征曲线 (AUROC) 下面的面积和0.5126.12的精度召回曲线 (AUPR) 下面的面积.
- 确定了潜在的候选药物,阿尔茨海默病的巴斯皮龙和帕金森病的三基,并与权威数据库进行验证.
结论:
- RCL-DR提供了一个强大的和统一的对比学习框架,用于有效的药物重新定位.
- 该框架成功地缓解了数据稀疏性,并改善了在识别新药与疾病相关性方面的预测性能.
- 证明了RCL-DR在精密药理学中的潜力,并加速了复杂疾病治疗的发现.
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