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

Computational approaches for drug discovery, such as quantitative structure-activity relationship, rely on structural similarities of small molecules to infer biological activity but are often limited to identifying new drug candidates in the chemical spaces close to known ligands. Here we report a biological activity-based modeling (BABM) approach, in which compound activity profiles established across multiple assays are used as signatures to predict compound activity in other assays or against a new target. This approach was validated by identifying candidate antivirals for Zika and Ebola viruses based on high-throughput screening data. BABM models were then applied to predict 311 compounds with potential activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Of the predicted compounds, 32% had antiviral activity in a cell culture live virus assay, the most potent compounds showing a half-maximal inhibitory concentration in the nanomolar range. Most of the confirmed anti-SARS-CoV-2 compounds were found to be viral entry inhibitors and/or autophagy modulators. The confirmed compounds have the potential to be further developed into anti-SARS-CoV-2 therapies. Activity profiles generated from quantitative high-throughput screening improve drug candidate prediction.

Automated summary

The biological activity-based modeling approach uses compound activity profiles to predict activity across different assays or targets, aiding in the discovery of potential antiviral drug candidates. This method successfully identified compounds with potential activity against Zika, Ebola, and SARS-CoV-2 viruses, demonstrating its effectiveness in predicting antiviral compounds. The confirmed anti-SARS-CoV-2 compounds show promise as potential therapies, with a focus on viral entry inhibition and autophagy modulation.