Seyedeh Shaghayegh Sadeghi and Mohammad Reza Keyvanpour* Pages 354 - 364 ( 11 )
Background: Drug repurposing has grown significantly in recent years. Research and innovation in drug repurposing are extremely popular due to its practical and explicit advantages. However, its adoption into practice is slow because researchers and industries have to face various challenges.
Objective: As this field, there is a lack of a comprehensive platform for systematic identification for removing development limitations. This paper deals with a comprehensive classification of challenges in drug repurposing.
Methods: Initially, a classification of various existing repurposing models is propounded. Next, the benefits of drug repurposing are summarized. Further, a categorization for computational drug repurposing shortcomings is presented. Finally, the methods are evaluated based on their strength to addressing the drawbacks.
Results: This work can offer a desirable platform for comparing the computational repurposing methods by measuring the methods in light of these challenges.
Conclusion: A proper comparison could prepare guidance for a genuine understanding of methods. Accordingly, this comprehension of the methods will help researchers eliminate the barriers thereby developing and improving methods. Furthermore, in this study, we conclude why despite all the benefits of drug repurposing, it is not being done anymore.
Drug repurposing, data mining, machine learning, challenges, benefit, computation repurposing.
Data Mining Laboratory, Department of Computer Engineering, Alzahra University, Vanak, Tehran, Department of Computer Engineering, Alzahra University, Vanak, Tehran