Molecular modeling of some commercially available antiviral drugs and their derivatives against SARS-CoV-2 infection

Abstract

Numerous prior studies have identified therapeutic targets that could effectively combat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, including the angiotensin-converting enzyme 2 (ACE2) receptor, RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro). In parallel, antiviral compounds like abacavir, acyclovir, adefovir, amantadine, amprenavir, darunavir, didanosine, oseltamivir, penciclovir, and tenofovir are under investigation for their potential in drug repurposing to address this infection. The aim of the study was to determine the effect of modifying the functional groups of the aforementioned antivirals in silico. Using the genetic optimization for ligand docking algorithm on software Maestro (version 11.1), the modified antivirals were docked onto ACE2 receptor, RdRp, and Mpro. Using QuickProp (Maestro v11.1), PASS (prediction of activity spectra for the substances), and altogether with SwissADME, the ADMET (absorption, distribution, metabolism, excretion, and toxicity) of the modified antivirals, as well as their bioavailability and the predicted activity spectra, were determined.  Discovery studio software was used to undertake post-docking analysis. Among the 10 antivirals, N(CH₃)₂ derivative of darunavir, N(CH₃)₂ derivative of amprenavir and NCH₃ derivative of darunavir exhibited best binding affinities with ACE2 receptor (docking scores: -10.333, -9.527 and -9.695 kJ/mol, respectively). Moreover, NCH₃ derivative of abacavir (-6.506 kJ/mol), NO₂ derivative of didanosine (-6.877 kJ/mol), NCH₃ derivative of darunavir (-7.618 kJ/mol) exerted promising affinity to Mpro. In conclusion, the results of the in silico screenings can serve as a useful information for future experimental works.