A Structure-Based Computational Pipeline for Broad-Spectrum Antiviral Discovery

Area of Science:

• Computational virology
• Drug discovery
• Infectious diseases

Background:

• Emerging viruses with pandemic potential necessitate rapid antiviral development.
• Traditional drug discovery pipelines are lengthy, highlighting the need for accelerated strategies.
• Broad-spectrum antivirals targeting multiple viral families and variants are essential.

Purpose of the Study:

• To present a structure-based computational pipeline for identifying and evaluating broad-spectrum antiviral inhibitors.
• To support spectrum breadth assessment and prioritization in lead optimization.
• To accelerate the discovery of antiviral therapies for current and emerging viruses.

Main Methods:

• An automated search for viral sequences related to a target construct.
• Pose prediction using available structural data.
• Scoring of protein-ligand complexes to estimate antiviral activity breadth using the drugforge package.

Main Results:

• Retrospective evaluation on SARS-CoV-2 and MERS-CoV main protease (Mpro) showed predictive power for binding affinities.
• Screening of SARS-CoV-2 Mpro inhibitors against various coronaviruses demonstrated potential for broad-spectrum activity assessment.
• The pipeline successfully identified and evaluated broad-spectrum inhibitors.

Conclusions:

• The developed computational pipeline accelerates the identification of broad-spectrum antiviral therapies.
• This approach aids in global preparedness for future viral outbreaks.
• Structure-based drug design is a viable strategy for rapid antiviral discovery.