军事医学2025，Vol.49Issue(7)：494-503,10.DOI:10.7644/j.issn.1674-9960.2025.07.003
融合语义信息与知识图谱结构信息的抗新型冠状病毒药物发现研究
Anti-SARS-CoV-2 drugs discovery by combining semantic information with knowledge graph structural information
周佳欣 1张音1
作者信息
- 1. 军事科学院军事医学研究院,北京 100850
- 折叠
摘要
Abstract
Objective To propose a knowledge graph embedding model that can help discover potential anti-SARS-CoV-2 drugs from approved drugs by combining semantic information with knowledge graph structural information.Methods Potential therapeutic drugs were predicted by using the head entity prediction task in knowledge graph completion.Results Six potential drugs were predicted,including naratriptan,sumatriptan,colchicine,doxorubicin,diphenhydramine and hydrocortisone.Conclusion The combination of semantic information and knowledge graphstructural information can enhance the representation capability of a knowledge graph embedding model,and provide a novel approach to research on anti-SARS-CoV-2 drug discovery.关键词
知识图谱补全/知识图谱嵌入/新型冠状病毒/药物发现/链接预测Key words
knowledge graph completion/knowledge graph embedding/SARS-CoV-2/drug discovery/link prediction分类
信息技术与安全科学引用本文复制引用
周佳欣,张音..融合语义信息与知识图谱结构信息的抗新型冠状病毒药物发现研究[J].军事医学,2025,49(7):494-503,10.
