A short review on recent developments in the computational techniques (2021) to mitigate SARS-CoV-19 disease

Abstract

Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) or SARS Corona Virus 2019 (SARS-CoV-19) disease, which was first reported in December 2019 in China, affected nearly all countries of the world with 65,19,18,402 confirmed cases and causing 66,56,601 deaths worldwide till 12^th December 2022. No specific drug has been discovered till now because the discovery of effective and reliable drug requires prolonged research and clinical trials. The efforts to discover treatment against Corona Virus Disease 2019 (CoVID-19) have been expedited by the techniques like high-throughput screening, bioinformatics and cheminformatics revealing molecular pathogenesis of coronaviruses. The application of multidisciplinary studies like computational chemistry, drug repurposing, protein-binding, nano-QSAR, fingerprint techniques have offered invaluable information about atomic viral host receptor interaction, biochemical basis of infection, strains evolution, identification of important viral protein for host-ligand binding studies. In the present review, we discuss the role of various techniques like Quantitative Structure Activity Relationship (QSAR), Machine Learning (ML), Deep Learning (DL), Virtual Screening (VS), Drug repurposing (DR), Molecular Dynamics (MD), nano-QSAR, docking study, Artificial Intelligence (AI) and Language Models (ML) in the process of potent drug/vaccine development against coronavirus. This review is an effort to summarize the reported database and tools for computational studies by bringing together resources in the public domain with respect to structure and pathophysiology of the corona virus. The work will offer an insight in prediction of therapeutics of the coronavirus disease.