College of Information Science and Engineering, Hunan University, Changsha, 410086, China.
College of Information Science and Engineering, Hunan University, Changsha, 410086, China.
Curr Opin Struct Biol. 2022 Feb;72:114-126. doi: 10.1016/j.sbi.2021.09.003. Epub 2021 Oct 11.
Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.
药物发现是新药鉴定的过程。这个过程受到来自现有化学库和数据库的不断增加的数据的推动。知识图谱被引入药物发现领域，为整合异构生物医学数据施加了明确的结构。该图可以提供多个实体之间的结构化关系和与实体相关的非结构化语义关系。在这篇综述中，我们总结了基于知识图谱的药物再利用和药物不良反应预测工作，用于药物发现。由于知识表示学习是探索知识图谱进行预测问题的常用方法，我们介绍了几种有代表性的嵌入模型，以提供对知识表示学习的全面理解。
