In silico approaches to identify therapeutic drug targets against COVID-19: a detailed review with a case study

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, primarily affects the respiratory system, has impacted millions worldwide and resulted in significant morbidity and mortality. The development of effective therapies for treatment is crucial to reduce the burden of the disease, as the current treatments are mainly supportive. Therefore, identifying therapeutic drug targets for COVID-19 is of utmost importance. Overall, identifying and validating drug targets for COVID-19 is an active area of research. Advances in understanding the molecular mechanisms underlying SARS-CoV-2 infection and the host response to the virus will continue to inform the development of effective therapies for COVID-19. Computational biology has played a crucial role in developing therapeutics for COVID-19, such as drug discovery, vaccine development, understanding viral evolution, predicting drug resistance, and repurposing existing drugs. In this review, we will discuss the details of the different drug targets and their mode of action. Computational biology has been an essential tool in the fight against COVID-19, helping researchers develop new treatments and vaccines and understand the behaviour and evolution of the virus. We demonstrate a case study on the in-silico identification of natural compounds as potential IL-6 inhibitors, highlighting their relevance in managing COVID-19-associated cytokine storms.