DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19

TL;DR

This paper introduces DeepVir, a deep matrix factorization method with graph regularization for antiviral repositioning, successfully predicting effective antivirals for COVID-19 using a novel optimization algorithm.

Contribution

It presents a new deep matrix completion framework with graph Laplacian regularization and a novel optimization method for antiviral repositioning, outperforming existing techniques.

Findings

Outperforms state-of-the-art matrix completion methods.

Accurately predicts antivirals for COVID-19.

Identifies drugs under trial or used for COVID-19 treatment.

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.