Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

Area of Science:

• Computational drug discovery
• Virology
• Medicinal chemistry

Background:

• COVID-19, caused by SARS-CoV-2, is a severe global health crisis.
• The pandemic has led to millions of infections and deaths worldwide.
• There is an urgent need for effective antiviral therapies targeting SARS-CoV-2.

Purpose of the Study:

• To identify novel drug candidates for treating COVID-19.
• To computationally screen millions of compounds against key SARS-CoV-2 targets.
• To accelerate the discovery of new COVID-19 treatments.

Main Methods:

• Utilized computational dynamics, molecular modeling, and structure simulation.
• Employed docking and machine learning models to predict drug efficacy.
• Screened over 6 million compounds, including FDA-approved drugs and clinical trial candidates.

Main Results:

• Generated an ensemble of structural conformations for high-quality docking.
• Identified an initial set of 350 high-value compounds with potential inhibitory activity.
• The identified compounds target essential SARS-CoV-2 proteins (S/Ace2, Tmprss2, Cathepsins L and K, Mpro).

Conclusions:

• The computational approach successfully identified promising drug candidates for COVID-19.
• These compounds target viral binding, membrane fusion, and replication.
• The findings are expected to expedite experimental screening and accelerate the development of COVID-19 treatments.