Sungkyunkwan University, South Korea.
Dalian University, China.
Health Informatics J. 2020 Dec;26(4):2737-2750. doi: 10.1177/1460458220937101. Epub 2020 Jul 17.
Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.
由于新药发现和开发的巨大成本，药物重定位已成为传统从头开始方法的重要补充。随着公共数据库数量的增加和分析方法的快速发展，计算方法在药物重定位领域获得了巨大的动力。在这项研究中，我们介绍了一种基于全面药物知识图的知识驱动药物重定位方法。我们通过系统地整合多个药物知识库来设计和开发药物知识图。我们描述了基于路径和嵌入的数据表示方法，将药物知识图中的信息转化为有价值的输入，以使机器学习模型能够预测药物重定位候选物。评估表明，该知识驱动方法仅使用其他疾病的治疗信息，就可以为已知的糖尿病治疗方法产生高预测结果。此外，该方法通过审查连接药物和疾病的元路径，支持探索性研究。这种知识驱动的方法是一种有效的药物重定位策略，支持大规模预测和案例研究的调查。
