适应性多视图学习方法用于增强药物重定向,使用化学诱导的转录特征,知识图和大型语言模型
Yudong Yan1, Yinqi Yang1, Zhuohao Tong1
1Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Journal of pharmaceutical analysis
|July 18, 2025
概括
本研究引入了自适应式多视图学习 (AMVL),通过整合多种数据源来改善药物重用. AMVL提高了药物疾病关联的预测,加速了药物发现和转化医学.
科学领域:
- 计算生物学和生物信息学
- 药物的发现和开发.
- 翻译医学是一种翻译医学.
背景情况:
- 传统药物开发是昂贵和耗时的.
- 现有的药物重定向方法通常使用有限的数据和简单的假设.
- 需要先进的计算方法来整合复杂的生物数据用于药物重新利用.
研究的目的:
- 引入自适应多视角学习 (AMVL),一种用于增强药物重定向的新方法.
- 整合化学诱导的转录形状 (CTP),知识图 (KG) 嵌入和大型语言模型 (LLM) 表示.
- 提高药物疾病关联预测的准确性和效率.
主要方法:
- 适应式多视图学习 (AMVL) 框架.
- 整合CTP,KG嵌入和LLM表示.
- 类似性矩阵扩展,多视图学习 (MVL),矩阵分解和集合优化.
- 在基准数据集 (Fdataset,Cdataset,Ydataset) 和iDrug数据集上进行评估.
主要成果:
- 在预测药物与疾病的关联方面,AMVL显著优于最先进的 (SOTA) 方法.
- 在基准和大规模数据集上实现了跨多个指标的卓越准确性.
- 基于文献的验证证实了预测能力,70%的顶级预测得到了最近证据的证实.
结论:
- AMVL为加速药物发现提供了强大且可扩展的解决方案.
- 该方法有效地整合了各种数据模式,以提高药物重新用途.
- 开源数据和代码促进了翻译医学的透明度,可复制性和进一步的创新.
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