A Review of Recent Developments and Progress in Computational Drug Repositioning

Wanwan       Shi; Xuegong       Chen; Lei       Deng
doi:10.2174/1381612826666200116145559
Abstract

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.
Keywords: Computational drug repositioning, drug-disease association, indication, biological network, machine learning, sparse matrix, text mining.
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DOI https://dx.doi.org/10.2174/1381612826666200116145559	Print ISSN 1381-6128
Publisher Name Bentham Science Publisher	Online ISSN 1873-4286
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