A Review of Recent Developments and Progress in Computational Drug Repositioning

Author(s): Wanwan Shi, Xuegong Chen, Lei Deng*

Journal Name: Current Pharmaceutical Design

Volume 26 , Issue 26 , 2020

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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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Article Details

VOLUME: 26
ISSUE: 26
Year: 2020
Page: [3059 - 3068]
Pages: 10
DOI: 10.2174/1381612826666200116145559
Price: $65

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