The outbreak of COVID-19 caused by SARS-CoV-2 virus continually led to infect a large population worldwide. Currently, there is no specific viral protein-targeted therapeutics. The Nucleocapsid (N) protein of the SARS-CoV-2 virus is necessary for viral RNA replication and transcription. The C-terminal domain of N protein (CTD) involves in the self-assembly of N protein into a filament that is packaged into new virions. In this study, the CTD (PDB ID: 6WJI) was targeted for the identification of possible inhibitors of oligomerization of N protein. Herein, multiple computational approaches were employed to explore the potential mechanisms of binding and inhibitor activity of five antiviral drugs toward CTD. The five anti-N drugs studied in this work are 4E1RCat, Silmitasertib, TMCB, Sapanisertib, and Rapamycin. Among the five drugs, 4E1RCat displayed highest binding affinity (-10.95 kcal/mol), followed by rapamycin (-8.91 kcal/mol), silmitasertib (-7.89 kcal/mol), TMCB (-7.05 kcal/mol), and sapanisertib (-6.14 kcal/mol). Subsequently, stability and dynamics of the protein-drug complex were examined with molecular dynamics (MD) simulations. Overall, drug binding increases the stability of the complex with maximum stability observed in the case of 4E1RCat. The CTD-drug complex systems behave differently in terms of the free energy landscape and showed differences in population distribution. Overall, the MD simulation parameters like RMSD, RMSF, Rg, hydrogen bonds analysis, PCA, FEL, and DCCM analysis indicated that 4E1RCat and TMCB complexes were more stable as compared to silmitasertib and sapanisertib and thus could act as effective drug compounds against CTD. Communicated by Ramaswamy H. Sarma

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.