SynDRep:基于知识图表的协同合作伙伴预测工具,用于药物重用
Karim S Shalaby1,2, Sathvik Guru Rao1, Bruce Schultz3
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin 53757, Germany.
Bioinformatics advances
|June 10, 2025
概括
通过将药物组合效应整合到知识图表中,SynDRep增强了药物重定向. 这种新的方法识别了协同作用的药物合作伙伴,使单个药物的疾病重用成为可能,从而提高了成本效益和患者的治疗结果.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 药物再利用为开发新疗法提供了一种具有成本效益和低风险的战略.
- 现有的药物重新定位方法往往忽视了药物组合数据的潜力,而是专注于单个药物 - 疾病 - 目标关系.
研究的目的:
- 介绍SynDRep,一种用于药物重定位的新型计算工具.
- 利用与药物组合效应丰富的知识图来预测协同作用的药物合作伙伴.
- 将已识别的伙伴药物重新利用为针对目标疾病的单一药物.
主要方法:
- 用药物组合效应丰富知识图 (KG).
- 应用图形嵌入技术 (例如,HolE) 进行关系预测.
- 使用机器学习 (ML) 模型 (例如随机森林) 进行药物重定向预测.
主要成果:
- HolE嵌入模型在预测关系方面实现了84.58%的准确性.
- 随机森林模型表现出强的性能,ROC-AUC为0.796.
- 确定了一些潜在的候选药物,如阿尔茨海默病的miconazole和albendazole,并得到了一些文献验证.
结论:
- 通过结合药物组合的洞察力,SynDRep为药物重新利用提供了一个新的框架.
- 该工具在识别现有药物的新疗法应用方面表现有前途.
- 通过专门的KG和额外的数据进行进一步的改进,可以提高SynDRep在成本有效的药物发现方面的有效性.
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