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基于知识图谱嵌入的阿尔茨海默病药物重定位研究

卢艳峰, 杨思瀚, 莫鸿仪, 侯凤贞

卢艳峰, 杨思瀚, 莫鸿仪, 侯凤贞. 基于知识图谱嵌入的阿尔茨海默病药物重定位研究[J]. 中国药科大学学报, 2023, 54(3): 344-354. DOI: 10.11665/j.issn.1000-5048.2023040305
引用本文: 卢艳峰, 杨思瀚, 莫鸿仪, 侯凤贞. 基于知识图谱嵌入的阿尔茨海默病药物重定位研究[J]. 中国药科大学学报, 2023, 54(3): 344-354. DOI: 10.11665/j.issn.1000-5048.2023040305
shu
LU Yanfeng, YANG Sihan, MO Hongyi, HOU Fengzhen. Drug repurposing for Alzheimer''s disease using knowledge graph embedding[J]. Journal of China Pharmaceutical University, 2023, 54(3): 344-354. DOI: 10.11665/j.issn.1000-5048.2023040305
Citation: LU Yanfeng, YANG Sihan, MO Hongyi, HOU Fengzhen. Drug repurposing for Alzheimer''s disease using knowledge graph embedding[J]. Journal of China Pharmaceutical University, 2023, 54(3): 344-354. DOI: 10.11665/j.issn.1000-5048.2023040305
shu

基于知识图谱嵌入的阿尔茨海默病药物重定位研究

  • 中国药科大学理学院，医药大数据与人工智能研究院，南京 211198

Drug repurposing for Alzheimer''s disease using knowledge graph embedding

  • Institute of Medical Big Data and Artificial Intelligence, School of Science, China Pharmaceutical University, Nanjing 211198, China

摘要

    摘要
  • 摘要: 阿尔茨海默病（Alzheimer''s disease，AD）给社会带来了巨大的医疗和经济负担，寻找和发现其治疗药物有着重大的研究意义。本研究采用知识图谱嵌入在公开的药物再利用知识图谱（drug repurposing knowledge graph，DRKG）上研究了AD的药物重定位。首先，利用4种知识图谱嵌入模型，即TransE、DistMult、ComplEx和RotatE在DRKG上学习实体和关系的嵌入向量；随后使用3种经典的知识图谱评估指标评估和比较了这些模型的性能和学习到的嵌入向量的质量；根据评估比较的结果，选择利用RotatE模型进行链接预测，确定了16种有可能用于AD治疗的药物，其中谷胱甘肽、氟哌啶醇、辣椒素、槲皮素、雌二醇、葡萄糖、双硫仑、腺苷、帕罗西汀、紫杉醇、格列本脲、阿米替林已被前人的研究证实对于AD有潜在的治疗作用。研究结果表明，基于知识图谱嵌入的药物重定位研究有望为AD药物发现提供新的思路和方法，RotatE模型可以有效地整合DRKG的多源信息，进而很好地完成了AD药物重定位任务。本研究的源代码可以从https：//github.com/LuYF-Lemon-love/AD-KGE获得。
    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.
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出版历程
  • 收稿日期:  2023-04-02
  • 修回日期:  2023-06-13
  • 刊出日期:  2023-06-24

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