Abstract
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.
Highlights
Drug repositioning has attracted considerable attention due to the potential for discovering new uses for existing drugs and for developing new drugs in pharmaceutical research and industry, due to its efficiency in saving time and cost over the traditional de novo drug development approaches [1, 2]
Current and prospective drug repositioning applications As a result of reviewing a number of computational drug repositioning studies and zooming in into their findings, we have identified a set of disease areas and related therapeutics that have benefited from drug repositioning applications
After surveying various avenues in which computational drug repositioning strategies have been adopted, and models have been introduced to identify novel therapeutic interactions, we can conclude that each strategy and approach has its advantages and limitations and that combining different strategies and approaches often achieve a higher success rate
Summary
Drug repositioning has attracted considerable attention due to the potential for discovering new uses for existing drugs and for developing new drugs in pharmaceutical research and industry, due to its efficiency in saving time and cost over the traditional de novo drug development approaches [1, 2].
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