Phytochemicals as potential inhibitors for novel coronavirus 2019-nCoV/SARS-CoV-2: a graph-based computational analysis

Abstract

Corona viruses (CoVs) are a group of infectious viruses that causes the regular cold to more extreme illnesses like Middle East Respiratory Syndrome (MERS-CoV), Severe Acute Respiratory Syndrome (SARS-CoV) and epic Covid (nCoV) is another strain that has been recently recognized in people. The formulation of effective drugs and treatment strategies are desperately required for 2019-nCoV/SARS-CoV-2 outbreak. Reducing the clinical trial period of existing as well as new drugs, the phytochemicals present in natural products would be helpful to get a quick treatment solution for this pandemic. Here, computationally some of the effective phytochemicals are identified for treating Covid. Publicly available databases have been used for collecting the phytochemicals and their associated genes that also interact with Corona viruses. Then a bipartite graph has been built with two sets of inputs; one set is the set of phytochemicals and the second set is the set of viruses. Thereafter, the eigen vector centrality which is the measure of most influential node in a graph has been calculated for each phytochemical. We found four such phytochemicals which have the top four eigen vector score. Then again, all possible cliques from the bipartite graph have been calculated and it has been seen that the same top four phytochemicals are present in almost all the bicliques. Finally, these top four phytochemicals have been investigated for their molecular and drug likeliness properties. Also the ADMET profile of the top phytochemicals are explored and analyzed.