A Review of Recent Developments and Progress in Computational Drug Repositioning

被引:6
|
作者
Shi, Wanwan [1 ]
Chen, Xuegong [1 ]
Deng, Lei [1 ,2 ]
机构
[1] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China
[2] Xinjiang Univ, Sch Software, Urumqi 830008, Peoples R China
基金
中国国家自然科学基金;
关键词
Computational drug repositioning; drug-disease association; indication; biological network; machine learning; sparse matrix; text mining; HETEROGENEOUS NETWORK; DISEASE ASSOCIATIONS; SIMILARITY; INFORMATION; IDENTIFICATION; INHIBITORS;
D O I
10.2174/1381612826666200116145559
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Computational drug repositioning is an efficient approach towards discovering new indications for existing drugs. In recent years, with the accumulation of online health-related information and the extensive use of biomedical databases, computational drug repositioning approaches have achieved significant progress in drug discovery. In this review, we summarize recent advancements in drug repositioning. Firstly, we explicitly demonstrated the available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of the commonly used computing approaches. For each method, we briefly described techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of the existing computing approaches.
引用
收藏
页码:3059 / 3068
页数:10
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