Efficient Identification of Anti-SARS-CoV‑2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling

Identification
of anti-SARS-CoV-2 compounds through traditional
high-throughput screening (HTS) assays is limited by high costs and
low hit rates. To address these challenges, we developed machine learning
models to identify compounds acting via inhibition of the entry of
SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like
(3CL) protease. The optimal classification models achieved good performance
with area under the receiver operating characteristic curve (AUC-ROC)
values of >0.78. Experimental validation showed that the best performing
models increased the assay hit rate by 2.1-fold for viral entry inhibitors
and 10.4-fold for 3CL protease inhibitors compared to those of the
original drug repurposing screens. Twenty-two compounds showed potent
(<5 μM) antiviral activities in a SARS-CoV-2 live virus assay.
In conclusion, machine learning models can be developed and used as
a complementary approach to HTS to expand compound screening capacities
and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.