posted on 2021-09-27, 07:14authored byVictor
O. Gawriljuk, Phyo Phyo Kyaw Zin, Ana C. Puhl, Kimberley M. Zorn, Daniel H. Foil, Thomas R. Lane, Brett Hurst, Tatyana Almeida Tavella, Fabio Trindade
Maranhão Costa, Premkumar Lakshmanane, Jean Bernatchez, Andre S. Godoy, Glaucius Oliva, Jair L. Siqueira-Neto, Peter B. Madrid, Sean Ekins
With the rapidly
evolving SARS-CoV-2 variants of concern, there
is an urgent need for the discovery of further treatments for the
coronavirus disease (COVID-19). Drug repurposing is one of the most
rapid strategies for addressing this need, and numerous compounds
have already been selected for in vitro testing by
several groups. These have led to a growing database of molecules
with in vitro activity against the virus. Machine
learning models can assist drug discovery through prediction of the
best compounds based on previously published data. Herein, we have
implemented several machine learning methods to develop predictive
models from recent SARS-CoV-2 in vitro inhibition
data and used them to prioritize additional FDA-approved compounds
for in vitro testing selected from our in-house compound
library. From the compounds predicted with a Bayesian machine learning
model, lumefantrine, an antimalarial was selected for testing and
showed limited antiviral activity in cell-based assays while demonstrating
binding (Kd 259 nM) to the spike protein
using microscale thermophoresis. Several other compounds which we
prioritized have since been tested by others and were also found to
be active in vitro. This combined machine learning
and in vitro testing approach can be expanded to
virtually screen available molecules with predicted activity against
SARS-CoV-2 reference WIV04 strain and circulating variants of concern.
In the process of this work, we have created multiple iterations of
machine learning models that can be used as a prioritization tool
for SARS-CoV-2 antiviral drug discovery programs. The very latest
model for SARS-CoV-2 with over 500 compounds is now freely available
at www.assaycentral.org.