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 for generalized formalisms of k - nearest neighbors as
a Tipping–Ogilvie and Machine Learning application within the quantum
computing context targeting the atomistic level of the protein apparatus
of the SARS - COV - 2 viral characteristics.
In this effort, we 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 AI - Quantum designed
molecules of GisitorviffirnaTM, Roccustyrna_gs1_TM, and
Roccustyrna_fr1_TM ligands targeting the COVID - 19 - SARS - COV - 2
SPIKE D614G mutation by unifying Eigenvalue Statements into Shannon
entropy quantities as composed on Tipping–Ogilvie driven Machine
Learning potentials for nonzero Christoffel symbols for Schwarzschild( DFT) ℓneuron ( ι) : == ==
φ∘D∘r2∘S∘r1𝑐0𝜁2 ( 1+∑𝑖) == == ( A∧A’( p)) • ⋱⋯⊗⋱⋯ •e− ρ ( rr)−−¯σ − ¯σσ¯ǫ −i_+𝑐0𝑎2 ( 1− 𝑦𝑦) 2}𝜓( 𝑦) 𝑣) improver for Chern - Simons Topology
Euclidean Geometrics. I also arrived at a new Zmatter derived finite ‐
dimensional state integral with a symplectic ω == ==( i~) −1 ( dx/x) ∧( dy/y) model for computing the analytically continued
“holomorphic blocks” on an appropriate quantum Hilbert space H that
compose physical Chern ‐ Simons partition function to put pharmacophoric
elements back together.