COVID-19 therapeutics(Arctoris/InSilico Medicine)

A review.We proposed to cover the theor. studies related to computational drug discovery, drug repurposing and related therapeutic aspects of COVID-19, such as computational techniques for detecting COVID-19, vaccination and treatment strategies, and novel forecasting models for SARS-COV-2 providing a deeper understanding of issues related to co - morbidity etc.After a thorough review, we accepted eight papers in this special issue covering most of the targeted areas mentioned above.We group together three works on the socio- demog. anal. of the spread of the COVID-19 pandemic.Mutation points as single nucleotide polymorphisms (SNPs) are identified in each virus clade detected through this phylogenetic anal.Host-pathogen interaction networks of COVID-19 have been exploited in two works by Barman et al. [4] and Saha et al. [5] since these networks are always considered one of the significant resources for identifying potential drug targets.As a result, four human proteins, viz. , PRKACA, RHOA, CDK5RAP2, and CEP250, have emerged as the best therapeutic targets, of which another group also found PRKACA and CEP250 as potential candidates for drug targets in COVID-19.The results of these computational experiments highlight that the most favorable COVID-19 inhibitors are zafirlukast, pranlukast, cande - sartan, cilexetil, saquinavir, and simeprevir.

COVID-19, the pandemic disease recently discovered in Wuhan (China), severely spread and affected both social and economic activity all over the world. Attempts to find an effective vaccine are challenging, time-consuming though interminable. Hence, re-proposing effective drugs is reliable and effective alternative. Taking into account the genome similarity of COVID-19 with SARS-CoV, drugs with safety profiles could be fast solution. Clinical trials encouraged the use of Chloroquine and Hydroxychloroquine for COVID-19 inhibition. One of the possible inhibition pathways is the competitive binding with the angiotension-converting enzyme-2 (ACE-2), in particular with the cellular Sialic acid (Neu5Ac). Here, we investigate the possible binding mechanism of ClQ and ClQOH with sialic acid both in the gas phase and in water using density functional theory (DFT). We investigated the binding of the neutral, monoprotonated and diprotonated ClQs and ClQOHs to sialic acid to simulate the pH effect on the cellular receptor binding. DFT results reveals that monoprotonated ClQ⁺ and ClQOH⁺, which account for more than 66% in the solution, possess high reactivity and binding towards sialic acid. The Neu5Ac-ClQ and the analogues Neu5Ac-ClQOH adducts were stabilized in water than in the gas phase. The molecular complexes stabilize by strong hydrogen bonding and π - π stacking forces. In addition, proton-transfer in Neu5Ac-ClQOH⁺ provides more stabilizing power and cellular recognition binding forces. These results shed light on possible recognition mechanism and help future breakthroughs for COVID-19 inhibitors.