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\begin{document}

\title{DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral
Repositioning: Application to COVID-19}



\author[1]{Angshul Majumdar }%
\author[2]{Aanchal Mongia }%
\author[2]{Emilie Chouzenoux }%
\author[2]{Stuti Jain }%
\affil[1]{Indraprastha Institute of Information Technology}%
\affil[2]{Affiliation not available}%


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  \date{June 15, 2024}


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\begin{abstract}
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.\\%
\end{abstract}%



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