id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-214795-8jweuq50	Mongia, Aanchal	DeepVir -- Graphical Deep Matrix Factorization for"In Silico"Antiviral Repositioning: Application to COVID-19	2020-09-22		.txt	text/plain	6420	369	47	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. It shows how the matrix completion framework can be used to computationally predict the drugs that could be effective against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus responsible for the ongoing pandemic, COVID-19 (COrona VIrus Disease-2019). In this present work, we propose to solve the problem of drug-virus association prediction via graph regularized deep matrix factorization. Among all the methodologies compared in [52] , graph regularized matrix factorization based technique (GRMF) provided the best results for the validation setting where drugs are predicted for novel viruses. In our proposed technique, multi-graph regularization is incorporated in the deep matrix factorization formulation with the aim to incorporate the metadata associated with the drugs and viruses in the form of similarity information as shown below:	./cache/cord-214795-8jweuq50.txt	./txt/cord-214795-8jweuq50.txt