Title: Predicting possible antiviral drugs against COVID-19 based on Laplacian regularised least squares and similarity kernel fusion

Authors: Xiaojun Zhang; Lan Yang; Hongbo Zhou

Addresses: School of Software, Quanzhou University of Information Engineering, Quanzhou 362000, China ' School of Software, Quanzhou University of Information Engineering, Quanzhou 362000, China ' School of Software, Quanzhou University of Information Engineering, Quanzhou 362000, China

Abstract: COVID-19 has produced a severe impact on global health and wealth. Drug repurposing strategies provide an effective way for inhibiting COVID-19. In this manuscript, a drug repositioning-based virus-drug association prediction method, VDA-LRLSSKF, was developed to screen potential antiviral compounds against COVID-19. First, association profile similarity matrices of viruses and drugs are computed. Second, similarity kernel fusion model is presented to combine biological similarity and association profile similarity from viruses and drugs. Finally, a Laplacian regularised least square method is used to compute the association probability for each virus-drug pair. We compare VDA-LRLSSKF with four the best VDA prediction methods. The experimental results and analyses demonstrate that VDA-LRLSSKF calculates better AUCs of 0.8286, 0.8404, 0.8882 on three datasets, respectively. VDA-LRLSSKF predicted that ribavirin and remdesivir could be underlying therapeutic clues for inhibiting COVID-19 and need to further experimental validation.

Keywords: SARS-CoV-2; VDA-LRLSSKF; drug repurposing; Laplacian regularised least squares; LRLS; similarity kernel fusion; SKF.

DOI: 10.1504/IJCSE.2023.132149

International Journal of Computational Science and Engineering, 2023 Vol.26 No.4, pp.470 - 478

Received: 05 Mar 2022
Received in revised form: 16 Apr 2022
Accepted: 19 Apr 2022
Published online: 12 Jul 2023 *

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