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 𝑐0𝜁2(1+∑𝑖)=[A∧A’(p)] ⊗[∂vˆ∂qˆ,∂uˆ∂pˆ] − [∂vˆ⊗∂pˆ,⊗∂uˆ⊗∂qˆ]=1𝑐𝑖𝜉𝑖{−ℏ20<≡===〈ψ⁎∣∣ψ⁎〉=[A∧A’(p)] ↦(𝑐𝑜𝑠𝜙−𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜙𝑐𝑜𝑠𝜙) (𝑥𝑝),𝐷(𝛼):(𝑥𝑝)↦(𝑥+𝑅𝑒(𝛼)𝑝+𝐼𝑚(𝛼)),𝑆(𝑟): (𝑥𝑝)↦(𝑒−𝑟00𝑒𝑟)(𝑥𝑝),𝐵𝑆(𝜃)…
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.