Context: In the healthcare system, Artificial Intelligence (AI) is emerging as a productive tool. There are instances where AI has done marvels in the diagnosis of various health conditions and the interpretation of complex medical disorders. Although AI is far from human intelligence, it can be used as an effective tool to study the SARS-CoV-2 and its capabilities, virulence, and genome. The progress of the pandemic can be tracked, and the patients can be monitored, thereby speeding up the research for the treatment of COVID-19. In this review article, we highlighted the importance of AI and Machine learning (ML) techniques that can speed up the path to the discovery of a possible cure for COVID-19. We also deal with the interactions between viromics and AI, which can hopefully find a solution to this pandemic. Evidence Acquisition: A review of different articles was conducted using the following databases: MEDLINE/PubMed, SCOPUS, Web of Science, ScienceDirect, and Google Scholar for recent studies regarding the use of AI, seeking the spread of different infectious diseases using relevant MeSH subheadings. Results: After a thorough screening of different articles, 30 articles were considered, and key information was obtained from them. Finally, the scope was broadened to obtain more information. Our findings indicated that AI/ML is a promising approach to drug development. Conclusions:The field of AI has enormous potential to predict the changes that may take place in the environment. If this technology is applied to situations of a pandemic such as COVID-19, breakthroughs could potentially pave the way for new vaccines and antiviral drugs.

Coronavirus disease – 2019 (COVID-19) pandemic, due to severe acute respiratory syndrome–coronavirus-2 (SARS-CoV-2), is posing a severe bio threat to the entire world. Nucleocapsids of SARS-CoV-2 and the related viruses were studied for gene and amino acid sequence homologies. In this study, we established similarities and differences in nucleocapsids in SARS-CoV-2, severe acute respiratory syndrome – coronavirus-1 (SARS-CoV-1), bat coronavirus (bat-CoV) and Middle East respiratory syndrome – coronavirus (MERS-CoV). We conducted a detailed analysis of the nucleocapsid protein amino acid and gene sequence encoding it, found in various coronavirus strains. After thoroughly screening the different nucleocapsids, we observed a close molecular homology between SARS-CoV-1 and SARS-CoV-2. More than 95% sequence similarity was observed between the two SARS-CoV strains. Bat-CoV and SARS-CoV-2 showed 92% sequence similarity. MERS-CoV and SARS-CoV-2 nucleocapsid analysis indicated only 65% identity. Molecular characterization of nucleocapsids from various coronaviruses revealed that SARS-CoV 2 is more related to SARS-CoV 1 and bat-CoV. SARS-CoV 2 exhibited less resemblance with MERS-CoV. SARS-CoV 2 showed less similarity to MERS-CoV. Thus, either SARS-CoV-1 or bat-CoV may be the source of SARS-CoV-2 evolution. Moreover, the existing differences in nucleocapsid molecular structures in SARS-CoV-2 make this virus more virulent and highly infectious, which means that the non-identical SARS-CoV-2 genes (which are absent in SARS-CoV-1 and bat-CoV) are responsible for COVID-19 severity. We observed that SARS-CoV-2 nucleocapsid from different locations varied in amino acid sequences. This revealed that there are many SARS-CoV-2 subtypes/subsets currently circulating globally. This study will help to develop antiviral vaccine and drugs, study viral replication and immunopathogenesis, and synthesize monoclonal antibodies that can be used for precise COVID-19 diagnosis, without false-positive/false-negative results.