  The integration of machine learning methods into bioinformatics provides
particular benefits in identifying how therapeutics effective in one context
might have utility in an unknown clinical context or against a novel pathology.
We aim to discover the underlying associations between viral proteins and
antiviral therapeutics that are effective against them by employing neural
network models. Using the National Center for Biotechnology Information virus
protein database and the DrugVirus database, which provides a comprehensive
report of broad-spectrum antiviral agents (BSAAs) and viruses they inhibit, we
trained ANN models with virus protein sequences as inputs and antiviral agents
deemed safe-in-humans as outputs. Model training excluded SARS-CoV-2 proteins
and included only Phases II, III, IV and Approved level drugs. Using sequences
for SARS-CoV-2 (the coronavirus that causes COVID-19) as inputs to the trained
models produces outputs of tentative safe-in-human antiviral candidates for
treating COVID-19. Our results suggest multiple drug candidates, some of which
complement recent findings from noteworthy clinical studies. Our in-silico
approach to drug repurposing has promise in identifying new drug candidates and
treatments for other viruses.
