Drug repurposing for Alzheimer''s disease using knowledge graph embedding
10.11665/j.issn.1000-5048.2023040305
- VernacularTitle:基于知识图谱嵌入的阿尔茨海默病药物重定位研究
- Author:
Yanfeng LU
1
;
Sihan YANG
;
Hongyi MO
;
Fengzhen HOU
Author Information
1. 中国药科大学理学院
- Publication Type:Journal Article
- Keywords:
drug repurposing;
Alzheimer''s disease;
knowledge graph;
knowledge graph embedding;
knowledge graph completion
- From:
Journal of China Pharmaceutical University
2023;54(3):344-354
- CountryChina
- Language:Chinese
-
Abstract:
Alzheimer''s disease (AD) has brought to us huge medical and economic burdens, and so discovery of its therapeutic drugs is of great significance.In this paper, we utilized knowledge graph embedding (KGE) models to explore drug repurposing for AD on the publicly available drug repurposing knowledge graph (DRKG).Specifically, we applied four KGE models, namely TransE, DistMult, ComplEx, and RotatE, to learn the embedding vectors of entities and relations on DRKG.By using three classical knowledge graph evaluation metrics, we then evaluated and compared the performance of these models as well as the quality of the learned embedded vectors.Based on our results, we selected the RotatE model for link prediction and identified 16 drugs that might be repurposed for the treatment of AD.Previous studies have confirmed the potential therapeutic effects of 12 drugs against AD, i.e., glutathione, haloperidol, capsaicin, quercetin, estradiol, glucose, disulfire, adenosine, paroxetine, paclitaxel, glybride and amitriptyline.Our study demonstrates that drug repurposing based on KGE may provide new ideas and methods for AD drug discovery.Moreover, the RotatE model effectively integrates multi-source information of DRKG, enabling promising AD drug repurposing.The source code of this paper is available at https://github.com/LuYF-Lemon-love/AD-KGE.
