Background:
The outbreak of COVID-19 caused by severe acute respiratory syndrome
coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved
the repositioning of antiviral remedies and other medications. Several therapies, including
oral antiviral treatments, represent some approaches to adapting to the long existence of the
COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and
development in compliance with global efforts to overcome the pandemic. The main protease is
an essential target in the viral cycle. Computer-aided drug design accelerates the identification
of potential treatments, including oral therapy.
Aim:
This work aims to identify potential SARS-CoV-2 main protease inhibitors using different
aspects of in silico approaches.
Method:
In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease
inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor
PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores,
were derived. A drug-likeness filter filtered the compounds retrieved from similarity
search and pharmacophore modeling before being subjected to molecular docking. The candidate
molecules that showed higher affinity to the main protease than the reference inhibitor were
further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters.
Result:
According to binding affinity and ADME analysis, four molecules (CHEMBL218022,
PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as
promising hits. The compounds above were not reported before; no previous experimental studies
and bioactive assays are available.
Conclusion:
Our time-saving approach represents a strategy for discovering novel SARS-CoV-
2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel
SARS-CoV-2 main protease inhibitors.