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

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

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 μM). 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.