Computational study of novel inhibitory molecule, 1-(4-((2S,3S)-3-amino-2-hydroxy-4-phenylbutyl)piperazin-1-yl)-3-phenylurea, with high potential to competitively block ATP binding to the RNA dependent RNA polymerase of SARS-CoV-2 virus.

For coronaviruses, RNA-dependent RNA polymerase (RdRp) is an essential enzyme that catalyses the replication from RNA template and therefore remains an attractive therapeutic target for anti-COVID drug discovery. In the present study, we performed a comprehensive in silico screening for 16,776 potential molecules from recently established drug libraries based on two important pharmacophores (3-amino-4-phenylbutan-2-ol and piperazine). Based on initial assessment, 4042 molecules were obtained suitable as drug candidates, which were following Lipinski's rule. Molecular docking implemented for the analysis of molecular interactions narrowed this number of compounds down to 19. Subsequent to screening filtering criteria and considering the critical parameters viz. docking score and MM-GBSA binding free energy, 1-(4-((2S,3S)-3-amino-2-hydroxy-4-phenylbutyl)piperazin-1-yl)-3-phenylurea (compound 1) was accomplished to score highest in comparison to the remaining 18 shortlisted drug candidates. Notably, compound 1 displayed higher docking score (-8.069 kcal/mol) and MM-GBSA binding free energy (-49.56 kcal/mol) than the control drug, remdesivir triphosphate, the active form of remdesivir as well as adenosine triphosphate. Furthermore, a molecular dynamics simulation was carried out (100 ns), which substantiated the candidacy of compound 1 as better inhibitor. Overall, our systematic in silico study predicts the potential of compound 1 to exhibit a more favourable specific activity than remdesivir triphosphate. Hence, we suggest compound 1 as a novel potential drug candidate, which should be considered for further exploration and validation of its potential against SARS-CoV-2 in wet lab experimental studies.Communicated by Ramasawamy H. Sarma.

Drug repurposing against SARS-CoV-2 using computational approaches.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has generated a critical need for treatments to reduce morbidity and mortality associated with this disease. However, traditional drug development takes many years, which is not practical solution given the current pandemic. Therefore, a viable option is to repurpose existing drugs. The structural data of several proteins vital for the virus became available shortly after the start of the pandemic. In this review, we discuss the importance of these targets and their available potential inhibitors predicted by the computational approaches. Among the hits identified by computational approaches, 35 candidates were suggested for further evaluation, among which ten drugs are in clinical trials (Phase III and IV) for treating Coronavirus 2019 (COVID-19).