Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
Process of drug repurposing in Covid-19. The data related to drugs, diseases, proteins, and genes etc. are extracted from different data sources (databases) followed use of different repurposing methods. Repurposing methods are used to find out the potential drugs which are then validated in in-vitro (2D or 3D) cell culture and in-vivo animal models. Finally, the shortlisted drugs are forwarded to clinical trials and successful drugs are repurposed [Display omitted]
•Drug repurposing is an effective and preferable alternative to de-novo drug discovery.•A systematic review of different strategies to drug repurposing for Covid-19 is given.•Limitations of repurposing approaches with recommendations are presented for Covid-19.•Network-based drug repurposing for Covid-19 is successful, easy to explain and interpret.