SIGNIFICANT STATEMENTS
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 𝑐0𝜁2 (1+∑𝑖) == == (A∧A’ (p)) e • ⋱⋯⊗⋱⋯ (Group
of Eqs.1-115) improver for Chern - Simons Topology Euclidean Geometrics.