Improved (DFT) Generalized k-nearest information systems on Molecular
QSAR-QMMM Cryptographic Mining and Chern-Simons Weighted
ℓneuron(ι):=φ∘D∘R2∘S∘R1 Topologies for the generation of the Roccustyrna
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 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 Molecular Pairs (MMP), Lindenbaum-Tarski logical spaces and
Adaptive Weighted KNN Positioning for Matched Bemis and Murko (BM)
driven eigenvalue statements into Shannon entropy quantities as composed
by Tipping–Ogilvie driven Machine Learning potentials on a (DFT)
ℓneuron(ι):=φ∘D∘R2∘S∘R1
02(1+∑=1{−ℏ20<≡===〈ψ⁎∣∣ψ⁎〉=∑Ni,j=1⟨ψi∣∣(ˆ−ρˆ0)∣∣ψj⟩∑Ni,j=1δij−∑Ni,j=1⟨ψi∣∣ρˆ0∣∣ψj⟩N−Tr[ρˆρˆ0]−∑Ni=1,j≠i⟨ψi∣∣ρˆ0∣∣ψj⟩1−2ℜ∑Ni=1,j