Abstract
ROR- NF-B/RelA-STAT3-FURIN-SARS-COV-2 Quantum Deep Learning Functional Similarities on Remdesivir, Ursolic Acid and Colchicine Drug Synergies to treat COVID19 in Practice
Author(s): Grigoriadis IoannisSARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae is an enveloped, positive- sense, single -stranded RNA betacoronavirus encoding a SARSCOV-2 (2019-NCOV, Coronavirus Disease 2019, that infect humans historically. Remdesivir, or GS-5734, is an adenosine triphosphate analog first described in the literature in 2016 as a potential treatment for Ebola. In 2017, its activity against the coronavirus family of viruses was also demonstrated. Remdesivir is also being researched as a potential treatment to SARS-CoV-2, the coronavirus responsible for COVID-19. Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. In this research paper, we estimated the druggable similarity by applying an inverse docking multitask machine learning approach to basal gene expression in acute respiratory distress syndrome and response to single drugs. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID19 (2019- NCOV), which is associated with 1,000,000 deaths worldwide, to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection and the associated NOS3- COVID-19 pathology. We anticipate that our approaches can be used for prioritization of drug combinations in large scale screenings, and to maximize the efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy, ultimately enabling COVID19 patient stratification.