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

By Sankalp Jain, Daniel C. Talley, Bolormaa Baljinnyam, Jun Choe, Quinlin Hanson, Wei Zhu, Miao Xu, Catherine Z Chen, Wei Zheng, Xin Hu, Min Shen, Ganesha Rai, Matthew Hall, Anton Simeonov, Alexey V. Zakharov

Posted 04 Jun 2021
bioRxiv DOI: 10.1101/2021.06.04.447130

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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