Quantum Phases and Chern-Simons Geometrics for the generation of a
ligand targeting COVID-19-SARS-COV-2 SPIKE 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. 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.