Identification of targets for drug repurposing to treat COVID-19 using a Deep Learning Neural Network

Abstract

The COVID-19 pandemic has resulted in a global public health crisis requiring immediate acute therapeutic solutions. To address this challenge, we developed a useful tool deep learning model using the graph-embedding convolution network (GECN) algorithm. Our approach identified COVID-19-related genes and potential druggable targets, including tyrosine kinase ABL1/2, pro-inflammatory cytokine CSF2, and pro-fibrotic cytokines IL-4 and IL-13. These target genes are implicated in critical processes related to COVID-19 pathogenesis, including endosomal membrane fusion, cytokine storm, and tissue fibrosis. Our analysis revealed that ABL kinase inhibitors, lenzilumab (anti-CSF2), and dupilumab (anti-IL4R) represent promising therapeutic solutions that can effectively block virus-host membrane fusion or attenuate hyperinflammation in COVID-19 patients. Compared to the traditional drug screening process, our GECN algorithm enables rapid analysis of disease-related human protein interaction networks and prediction of candidate drug targets from a large-scale knowledge graph in a cost-effective and efficient manner. Overall, Overall, our results suggest that the model has the potential to facilitate drug repurposing and aid in the fight against COVID-19.