  Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic
situation. Repurposing drugs, already shown to be free of harmful side effects,
for the treatment of COVID-19 patients is an important option in launching
novel therapeutic strategies. Therefore, reliable molecule interaction data are
a crucial basis, where drug-/protein-protein interaction networks establish
invaluable, year-long carefully curated data resources. However, these
resources have not yet been systematically exploited using high-performance
artificial intelligence approaches. Here, we combine three networks, two of
which are year-long curated, and one of which, on SARS-CoV-2-human host-virus
protein interactions, was published only most recently (30th of April 2020),
raising a novel network that puts drugs, human and virus proteins into mutual
context. We apply Variational Graph AutoEncoders (VGAEs), representing most
advanced deep learning based methodology for the analysis of data that are
subject to network constraints. Reliable simulations confirm that we operate at
utmost accuracy in terms of predicting missing links. We then predict hitherto
unknown links between drugs and human proteins against which virus proteins
preferably bind. The corresponding therapeutic agents present splendid starting
points for exploring novel host-directed therapy (HDT) options.
