SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses infected ACE2-expressing cells pseudotyped with SG614 that is presently affecting a growing number of countries markedly more efficiently than those with SD614. The availability of newer powerful computational resources, molecular modeling techniques, and cheminformatics quality data have made it feasible to generate reliable algebraic calculations to design new chemical entities, merging chemicals, recoring natural products, and a lot of other substances fuelling further development and growth of this AI-quantum based drug design field to balance the trade-off between the structural complexity and the quality of such biophysics predictions that cannot be obtained by any other method. In this paper, we strongly combine topology geometric methods targeting at the atomistic level the protein apparatus of the SARS-COV-2 virus that are simple in machine learning anti-viral characteristics, to propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of the AI-Quantum designed molecule the RoccustyrnaTM small molecule, a multi-targeting druggable scaffold (1S,2R,3S)‐2‐({[(1S,2S,4S,5R)‐4‐ethenyl‐4‐sulfonylbicyclo[3.2.0]heptan‐2‐yl]oxy}amino)‐3‐[(2R,5R)‐5‐(2‐methyl‐6‐methylidene‐6,9‐dihydro‐3H‐purin‐9‐yl)‐3‐methylideneoxolan‐2‐yl]phosphirane‐1‐carbonitrile targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.
Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern 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. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection. We anticipate that our geometry hashing driven quantum deep learing similarity approaches which is based on separated pairs of short consecutive matching fragments, can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.