DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19
This work addresses the problem of accelerating drug discovery for viral diseases like COVID-19 by repositioning existing antivirals, though it is incremental as it builds on existing graph-regularized matrix completion techniques.
The authors tackled antiviral repositioning by formulating it as a matrix completion problem with graph Laplacian regularization and deep matrix factorization, achieving superior performance over state-of-the-art methods on a curated RNA drug-virus association dataset and successfully predicting antivirals for COVID-19 that are in use or under trial.
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.