Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications

The coronavirus disease 2019 (COVID-19) pandemic has claimed many lives since it was first reported in late December 2019. However, there is still no drug proven to be effective against the virus. In this study, a candidate host–pathogen–interactive (HPI) genome-wide genetic and epigenetic network (HPI-GWGEN) constructed via big data mining. reverse engineering method applied investigate pathogenesis of SARS-CoV-2 infection by pruning false positives HPI-GWGEN through HPI RNA-seq time profile data. Subsequently, using principal projection (PNP) annotations Kyoto Encyclopedia Genes Genomes (KEGG) pathway, we identified significant biomarkers usable as targets for destroying favorable environments replication or enhancing defense host cells it. To discover multiple-molecule drugs that target (as targets), deep neural (DNN)-based drug–target interaction (DTI) model trained DTI databases predict molecular these targets. Using DNN-based model, predicted targeting (drug targets). After screening with design specifications, finally proposed combination bosutinib, erlotinib, 17-beta-estradiol treatment amplification stage 17-beta-estradiol, sertraline saturation mild-to-moderate infection.