A hybrid in silico approach reveals novel inhibitors of multiple SARS-CoV-2 variants

Jain, S.; Talley, D. C.; Baljinnyam, B.; Choe, J.; Hanson, Q.; Zhu, W.; Xu, M.; Chen, C. Z.; Zheng, W.; Hu, X.; Shen, M.; Rai, G.; Hall, M.; Simeonov, A.; Zakharov, A. V.

doi:10.1101/2021.06.04.447130

bioRxiv · 10.1101/2021.06.04.447130

A hybrid in silico approach reveals novel inhibitors of multiple SARS-CoV-2 variants

Jain, S.·Talley, D. C.·Baljinnyam, B.·Choe, J.·Hanson, Q.·Zhu, W.·Xu, M.·Chen, C. Z.·Zheng, W.·Hu, X.·Shen, M.·Rai, G.·Hall, M.·Simeonov, A.·Zakharov, A. V.

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Abstract

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 cytophatic 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 (~16% of predicted hits) active compounds (Efficacy > 30%, IC50 [≤] 15 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 analysis identified allosteric binders to host receptor angiotensin-converting enzyme 2, which were able to inhibit the entry of 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.

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Jain, S., Talley, D. C., Baljinnyam, B., Choe, J., Hanson, Q., Zhu, W., Xu, M., Chen, C. Z., Zheng, W., Hu, X., Shen, M., Rai, G., Hall, M., Simeonov, A., Zakharov, A. V.. 2021-06-04. A hybrid in silico approach reveals novel inhibitors of multiple SARS-CoV-2 variants. https://doi.org/10.1101/2021.06.04.447130

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