Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (similar to 16% of predicted hits) active compounds (efficacy > 30%, IC50 <= 15 mu M). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Automated summary

By utilizing NCATS screening data and various models, active small molecules against SARS-CoV-2 were successfully predicted. Further investigation revealed three novel chemotypes with potential, along with the identification of potential host inhibitors.