  This work formulates antiviral repositioning as a matrix completion problem
where the antiviral drugs are along the rows and the viruses along the columns.
The input matrix is partially filled, with ones in positions where the
antiviral has been known to be effective against a virus. The curated metadata
for antivirals (chemical structure and pathways) and viruses (genomic structure
and symptoms) is encoded into our matrix completion framework as graph
Laplacian regularization. We then frame the resulting multiple graph
regularized matrix completion problem as deep matrix factorization. This is
solved by using a novel optimization method called HyPALM (Hybrid Proximal
Alternating Linearized Minimization). Results on our curated RNA drug virus
association (DVA) dataset shows that the proposed approach excels over
state-of-the-art graph regularized matrix completion techniques. When applied
to "in silico" prediction of antivirals for COVID-19, our approach returns
antivirals that are either used for treating patients or are under for trials
for the same.
