Targeting SARS-CoV-2 main protease: a pharmacophore and molecular modeling approach

Area of Science:

• Computational drug discovery
• Virology
• Medicinal Chemistry

Background:

• COVID-19 pandemic necessitates novel therapeutics.
• SARS-CoV-2 main protease (Mpro) is crucial for viral replication and a key drug target.
• Existing treatments like nirmatrelvir highlight the need for alternative inhibitors.

Purpose of the Study:

• To identify novel inhibitors of SARS-CoV-2 Mpro using computational methods.
• To evaluate the binding affinity and stability of potential drug candidates.
• To discover alternative or complementary therapeutics to nirmatrelvir.

Main Methods:

• Virtual screening of 89,200 compounds from the ChemDiv database.
• Pharmacophore modeling and drug-like property predictions.
• Molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations (MM/GBSA).

Main Results:

• Identified E912-0363 and G740-1003 as promising Mpro inhibitors with affinities comparable to nirmatrelvir.
• Extended 500-ns MD simulations confirmed E912-0363's stability and potent Mpro inhibition.
• Computational screening successfully narrowed down candidates for further drug development.

Conclusions:

• E912-0363 is a highly promising SARS-CoV-2 Mpro inhibitor.
• This compound demonstrates potential as a complementary or alternative therapeutic agent for COVID-19.
• Integrated computational approaches are effective for identifying novel antiviral drug candidates.