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 detailed demonstrate available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of commonly used computing approaches. For each method, we briefly describe techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of existing computing approaches.
Keywords: Computational drug repositioning, drug-disease association, indication, biological network, machine learning, sparse matrix,
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