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. 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 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.