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.