Biological activity-based modeling identifies antiviral leads against SARS-CoV-2

Area of Science:

• Computational chemistry
• Drug discovery
• Virology

Background:

• Traditional drug discovery methods like quantitative structure-activity relationship (QSAR) are limited to exploring chemical spaces near known ligands.
• Predicting novel drug candidates often requires exploring vast chemical spaces beyond known structural similarities.

Purpose of the Study:

• To introduce a novel biological activity-based modeling (BABM) approach for drug discovery.
• To leverage compound activity profiles across multiple assays for predicting activity against new targets.
• To identify potential antiviral compounds, including those targeting SARS-CoV-2.

Main Methods:

• Developed and applied a biological activity-based modeling (BABM) approach using compound activity profiles as signatures.
• Validated BABM by identifying candidate antivirals for Zika and Ebola viruses using high-throughput screening data.
• Utilized BABM to predict 311 compounds with potential activity against SARS-CoV-2.

Main Results:

• BABM successfully predicted antiviral activity for SARS-CoV-2.
• 32% of predicted compounds exhibited antiviral activity in cell culture assays.
• The most potent SARS-CoV-2 compounds demonstrated half-maximal inhibitory concentrations in the nanomolar range.
• Identified SARS-CoV-2 inhibitors acting as viral entry inhibitors and/or autophagy modulators.

Conclusions:

• The BABM approach offers a powerful strategy for identifying novel drug candidates beyond the limitations of structural similarity.
• The identified compounds show promise for further development into effective therapies against SARS-CoV-2.
• BABM has broad applicability in predicting compound activity for various diseases and targets.