.QSAR-QMMM Cryptographic Mining on Chern-Simons Topologies for the generation of a Ligand Targeting SARS-COV-2 D614G Binding Sites
Abstract
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. 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 of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands with Preferred IUPAC Names of (7aR)‐5‐amino‐N‐[(S)‐ {2‐[(S)‐[(E)‐(amino methylidene)amino](cyano)methyl]hydrazin‐1‐yl} (aziridin‐1‐yl)phosphoryl]‐ 1‐[(2E)‐2‐ [(fluoromethanimidoyl) imino]acetyl]‐7‐oxo‐1H,7H,7aH‐pyrazolo[4,3‐d]pyrimidine‐3‐carboxamide; N‐{[(2‐ amino‐6‐oxo‐6,9‐dihydro‐1H‐purin‐9‐yl)amino]({1‐[5‐({[cyano({1‐[(diamino methylidene)amino]…
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