VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares.

ABSTRACT:

Background

A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19.

Methods

In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random wal

SUBMITTER: Shen L 

PROVIDER: S-EPMC8664497 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature