Hybrid <i>In Silico</i> Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants
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 D. Hall, Anton Simeonov, Alexey Zakharov
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 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 (∼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 investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown 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.