Predicting possible antiviral drugs against COVID-19 based on Laplacian regularised least squares and similarity kernel fusion
by Xiaojun Zhang; Lan Yang; Hongbo Zhou
International Journal of Computational Science and Engineering (IJCSE), Vol. 26, No. 4, 2023

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.

Online publication date: Wed, 12-Jul-2023

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