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In-silico strategies to combat COVID-19: A comprehensive review
Soumya Basu1, Sudha Ramaiah1, Anand Anbarasu1
1Medical & Biological Computing Laboratory, School of Bio-Sciences & Technology, Vellore Institute of Technology, Vellore, India.
Abstract:
The novel coronavirus SARS-CoV-2 since its emergence at Wuhan, China in December 2019 has been creating global health turmoil despite extensive containment measures and has resulted in the present pandemic COVID-19. Although the virus and its interaction with the host have been thoroughly characterized, effective treatment regimens beyond symptom-based care and repurposed therapeutics could not be identified. Various countries have successfully developed vaccines to curb the disease-transmission and prevent future outbreaks. Vaccination-drives are being conducted on a war-footing, but the process is time-consuming, especially in the densely populated regions of the world. Bioinformaticians and computational biologists have been playing an efficient role in this state of emergency to escalate clinical research and therapeutic development. However, there are not many reviews available in the literature concerning COVID-19 and its management. Hence, we have focused on designing a comprehensive review on in-silico approaches concerning COVID-19 to discuss the relevant bioinformatics and computational resources, tools, patterns of research, outcomes generated so far and their future implications to efficiently model data based on epidemiology; identify drug targets to design new drugs; predict epitopes for vaccine design and conceptualize diagnostic models. Artificial intelligence/machine learning can be employed to accelerate the research programs encompassing all the above urgent needs to counter COVID-19 and similar outbreaks.
Insights
Bioinformatics and computational tools accelerate COVID-19 research, aiding drug discovery, vaccine design, and diagnostics. These in-silico approaches are crucial for managing the pandemic and future outbreaks.
Area of Science:
- Computational Biology
- Bioinformatics
- Epidemiology
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, presents global health challenges.
- Effective treatments beyond supportive care remain limited, despite vaccine development.
- Bioinformatics and computational biology are vital for accelerating research and therapeutic development.
Purpose of the Study:
- To provide a comprehensive review of in-silico approaches for COVID-19 management.
- To discuss bioinformatics resources, tools, research trends, and outcomes.
- To explore future implications for epidemiological modeling, drug discovery, vaccine design, and diagnostics.
Main Methods:
- Review of existing literature on in-silico methods applied to COVID-19.
- Analysis of bioinformatics and computational tools relevant to the pandemic.
- Discussion of artificial intelligence and machine learning applications in COVID-19 research.
Main Results:
- In-silico approaches are instrumental in identifying drug targets and designing novel therapeutics.
- Computational methods aid in predicting epitopes for vaccine development.
- Bioinformatics tools facilitate epidemiological data modeling and diagnostic model conceptualization.
Conclusions:
- In-silico strategies, including AI/ML, significantly accelerate COVID-19 research and development.
- These approaches are essential for efficient pandemic response and preparedness for future outbreaks.
- Continued development and application of computational tools are critical for public health advancements.
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