Improved (DFT) Generalized k-nearest information systems on Molecular QSAR-QMMM Cryptographic Mining and Chern-Simons Weighted ℓ neuron( ι ):= φ฀ D฀R2฀S฀R1 Topologies for the generation of the Roccustyrna Ligand Targeting SARS-COV-2 D614G Binding Sites.

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, Illustration1. 3D Docking representations of (i) GisitorviffirnaTM, (ii) Roccustyrna_gs1_TM, and (ii) Roccustyrna_fr1_TM fragments when targeted onto the PDB:6WZU SARS-Cov-2 protein targets. the structure and functions of SARS-CoV-2 as linear functional on the algebra of free energy docking observables. (37,38,40) Hybrid quantum repeater via a robust creation of entanglement between remote memory qubits was implemented for predicting drug targets and for multi-target and multi-site-based virtual screening against COVID-19. To demonstrate its flexibility, we tackle a hugely different objective issued from the organic molecular materials domain. We show that our method can generate sets of optimized critical molecules having high energy or low energy, starting only from penicillamine derivatives. We can also set constraints on a synthesizability score and structural features. The basic idea developed in Levine (2004) incorporates crit ical components of what is to be expected from a ―real‖ signature. (41,42) Flexible Topology Euclidean Geometric was used to fragment molecules automatically in this molecular modeling and drug designing project on several parameters while keeping the definition of the groups as simple as possible. of the macrodomain (NSP3) in complex with of sequence of V-S-VAL-155, V-M-PHE-156, V-S-PHE-156, V-M-ASP-157, V-M-LEU-160, V-S-LEU-160, V-M-GLU-120 amino acids respectively when compared with Amprenavir, Asunaprevir, Atazanavir, Boceprevir, Cytarabine, Darunavir, Ritonavir, Sorivudine, Taribavirin, Tenofovir, Valganciclovir, Merimepodib,

Density Field Theories (DFT) (23) has become a common approach as a quantum-many body premium technique and semi-empirical computational scheme in place (24) of the quantum processor and docking energy used for studying molecule structure under QM simulated sampling error among other quantitative understanding observables. Density Field Theories (DFT) is continuously increasing for more systematic and less expensive methods (25) when compared to traditional drug development approaches to repositioning drugs and physical extracts and represent the similarities (26) and dissimilarities (27) between drugs and repurposed viral proteins, respectively. (28) However, the Schrödinger equations in Markovian and (27) non-Markovian scenarios cannot be solved for any but a one-data-driven (29) electron system method (the hydrogen atom), to construct a family of solutions of (28) equation (30) and approximations need to be made. According to QM, (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)23) and during the construction of stochastic Schrödinger (29) equations, an electron bound that converges quickly and reliably by acknowledging the conditional Bohmian wavefunction to an atom cannot possess any (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)21,22,23,24,25,26,2728) arbitrary energy to produce the desired distribution or occupy any position in space using statistical and machine (23,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)38) learning concepts. Molecular Pairs (MMP), Lindenbaum-Tarski logical spaces, and Adaptive Weighted KNN Positioning Matched Bemis and Murko (BM) driven eigenvalue statements were incorporated in this project when analyzing pharmacological data allowing a well-defined superposition for each fragmented pharmacophore. This shows that the application to quantum computing as orthogonally applied for the design of small molecules may allow pure mechanical computations both for re-generating Lipinski rules and quantum inferences to bridge the gap between practical in vitro testing implementations and theoretical docking scalability predictions. (25,27,28) Since it has been shown that Path selection into a nonlinear Riemann-Hilbert simple problem of any metal formula φ for quantum repeater networks towards the determination of the exact interpolating function of h(λ) can be geometrically represented by Chern-Simons logical spaces and subspaces I decided to cryptographically implement supersymmetric solutions and Borel Singularities for N=2 allowing a quantum repeater based vectorial Supersymmetric representation in this drug design project. (20,26,27,28,29,30,31) In general, the notions of Lindenbaum matrix and associated axiomatic formulations (AQFT) for Lindenbaum-Tarski guided Adaptive Weighted KNN Positioning and its relative development to the product topology continuing to shape the field of algebraic logic introducing topology on a set to define the (31) cartesian product of topological spaces. As subbase supersymmetric solutions have paved the way until this day, to further algebraization of topology products, which had been begun by George Boole in the 19th century, as well as to an innovative language of logic, in a symmetric model theory containing no other constants but only one connective →. Philosophical interpretations of QM Molecular Pairs (MMP) as a core part of contemporary physics (Minkowski-type, wave-edge, etc), (20,27,(32)(33) including von Neumann and Dirac formulation states as well as probabilistic transformations on Murko (BM) driven eigenvalue statements for algebraic multi-metrics (Triangle area, Bond-angle, etc) were incorporated in this project to treat Tipping-Ogilvie and Machine Learning observables as foundational according to the interaction information theory (QIT) reference frames. (20,33,34,35) In this project, we show an original strategy and demonstrate the utility and the mechanics of this (32) unified molecular formalism as a Tipping-Ogilvie and Machine Learning QMMMP application within the quantum computing context as perturbed asymptotically through the example of coupled anti-de Sitter black harmonic black-hole oscillators and brane spacetimes. We expect this Lindenbaum-Tarski driven Chern-Simons representation to generate a valid QSAR modeling, and a lead compound design formalism, in our molecular modeling and simulations in order to produce orthogonal coordinates as applied for the design of a novel multi-chemo-structure against the crystal structure of COVID-19 protein targets. (29,35,36) A meta server and a Kappa-Symmetry C algebra of local observables were incorporated for the docking of FDA-approved small molecules, peptide-mimetic, and humanized antibodies against potential targets of COVID-19 via a generalized procedure of Quantization of classical fields which were fused together with QSAR automating modeling to lead the commutation and anticommutation relations. (37,39,41,42) Dynamic niching and flexible heuristic genetic algorithmic states for automatic molecule recoring and fragmentation were applied to fragment and re-core a database of 20,000+ molecules for use with the group contribution model Universal Quasihelical Functional Group Activity Coefficients (UNIFAC) against the structure and functions of SARS-CoV-2 as linear functional on the algebra of free energy docking observables. (37,38,40) Hybrid quantum repeater via a robust creation of entanglement between remote memory qubits was implemented for predicting drug targets and for multi-target and multi-site-based virtual screening against COVID-19. To demonstrate its flexibility, we tackle a hugely different objective issued from the organic molecular materials domain. We show that our method can generate sets of optimized critical molecules having high energy or low energy, starting only from penicillamine derivatives. We can also set constraints on a synthesizability score and structural features. The basic idea developed in Levine (2004) incorporates critical components of what is to be expected from a -real‖ signature. (41,42) Flexible Topology Euclidean Geometric was used to fragment molecules automatically in this molecular modeling and drug designing project on several parameters while keeping the definition of the groups as simple as possible. Maximum Common Substructure (MCS) topologies for generalized k-nearest neighbors on Tipping-Ogilvie and Machine Learning generated Molecular Pairs (MMP), and an Adaptive Weighted KNN Positioning Matched Bemis and Murko (BM) approach for Predictive Neural Networks and Quantum-Inspired frameworks were employed for supercritical entanglements introducing an advanced quantum mechanical inverse docking algorithm providing further insight to confirm the practicality of docking energy predictions. In this protocol, tools from conventional cryptography for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 Nsp15 (NendoU), (Mpro, 3CLpro), Nsp12 (RdRp), N protein and Spike were combined as inputs and the key element functions of SARS-CoV-2 protein pathways in understanding and designing possible novel antiviral agents, from both a quantum algebraic and a cheminformatic perspective along with the principles of the regulation of computer-aided drug discovery methodologies. This paper concentrates on the unification of quantum mechanics fundamental theories into a master wave equation for a generalized k- CQ2t |ϕ(t) to be a better representation of the realistic potentials in computation of energy eigenvalues.
Pharmacophoric-ODE fragmenting, merging, and recording: Biogenetoligandorol AI-heuristic algorithm. The patterns of this Biogenetoligandorol fragmentation Scheme are sorted into the working inputs of the Galilean transformation by examining the -extended‖ Galilean transformation based on a set of heuristically determined descriptors moving with relative acceleration to a rigid system to a nonrotating geometric observer having an arbitrary time-dependent acceleration (20,21,27,28). (25,29) These descriptors as an applied nonconcurrent to both translate and rotate can be, for example, the number of atoms describing the pattern of a force system which can both translate and rotate and be determined by the substitution as a polynomial in ip (r, t) = eiJ{rt) (p (r', t).
. After that, the child pharmacophoric pattern is searched in an inertial repeated merged system S asip = % (ml5 r, t) + ip2im2, r, t (equation18). (25,26,37) Then assume that one fragmented pharrmacophore system can describe the same superposition in an accelerating to a larger ligand-receptor system S' that obeys (20), with § = £ (r) (equation19), £ (0) = £ (7) (equation20), so that the system S' performs a closed quantum circuit and coincides with the chemical structure system the S at times t = 0 and t=T, such that r ‗ iT) = r (7) (equation21). (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)37) To avoid q { h q; } qreg q (3) ; creg c (3) ; reset q (0) ; reset q (1) ; reset q (2) ; h q (0) ; u2 (pi/2,pi/2) q (1) ; incomplete ] cyano-lambda6-sulfanyl}) methyl]phosphorylidene} amino) -4,6-dihydro-1H-purin-6-onecyano-1-({ ((2S,4R,5R) azaphosphiridin-1-ylium chemical groups that were already screened. In case it successfully estimates a valid fragmentation, Scheme this is taken as the phase solution relating to how one clustered pharmacophoric element occupies the space normally filled by the protease‗s substrate‗s interacting main chain; would describe the same phase in an alternative XYZ coordinate smile system. (33,(35)(36)(37)(38)(39)(40)(41)(42) This Lindenbaum-Tarski algebraic algorithm was implemented as a recursive algorithm that leads to enhancement of my novel small molecule‗s catalytic activity against the 3C protease-like domains I and II and performs a complete decision tree search of all possible combinations of fragmentation, merging, and pharmacophoric re-coring systems targeting in the in SARS-CoV Mpro. (12,13,(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)) Therefore, from a mathematical perspective, a TQFT that recovers the above AI-Quantum Hilbert symmetric spaces may be called -Chern-Simon's theory. The generative model featured an embedding layer followed by three gated recurrent units (GRU) [37] with 356 dimensions, and finally a linear layer that reshaped the outputs to the size of all possible tokens. The loss function is the Negative Log-Likelihood (NLL): where "xt" is a random variable representing the probability for all possible tokens of the vocabulary at step "t" and "xt-1" is the token chosen at the previous step. The patterns of this Biogenetoligandorol fragmentation Scheme are sorted into the working inputs of the Galilean transformation by examining the -extended‖ Galilean transformation based on a set of heuristically determined descriptors moving with relative acceleration to a rigid system to a nonrotating geometric observer having an arbitrary time-dependent acceleration ∂ P̂  (25,29). These descriptors as an applied nonconcurrent to both translate and rotate can be, for example, the number of atoms describing the pattern of a force system which can both translate and rotate and be determined by the substitution as a polynomial in ip (r, t) = eiJ{rt) (p (r', t∂ P̂ ℏ201〉 Molecular scaffolds are generally used to describe the common core structures of the molecules [25]. The selected herbs are classified into structural classes using the characteristic scaffolds of each group [14]. In medicinal chemistry, a molecular scaffold is used to represent the core structure of a group of active compounds. Since the compounds with the same scaffold may influence a particular metabolic pathway, the molecular scaffolds can effectively contribute to the prediction of biological activities [16]. The scaffold of molecule groups is defined as a common sub-graph of the graphs of the molecule groups. Representatively, Maximum Common Substructure (MCS), Matched Molecular Pairs (MMP), and Bemis and Murko (BM) are the commonly used methods to produce molecular scaffolds [22][23][24][25][26][27][28][29][30]. In addition to exact carbon skeleton matching, we also evaluated a fuzzy skeleton flter. The fuzzy scaffold filter was based on the carbon skeleton but used the atom pair fingerprint [36] for similarity assessment to compare carbon skeletons instead of exact scaffold match. For each generated carbon skeleton, an atom pair fingerprint was calculated, and different carbon skeletons were compared using the Tanimoto coefficient. If the coefficient value was at least 0.6, the two scaffolds were considered similar and the corresponding compound was added to the same bucket. The scaffold, as per the MMP method, is defined as the common part among molecules that have different molecular fragments at the same single specific site [38,39]. MCS method defines a scaffold as the maximum common edge subgraph of the graphs of molecule groups [30]. Unlike the MMP and MCS methods, the scaffolds produced by the BM method reveal a hierarchical structure [31].
through the example of two coupled Chern-Simons Topology driven anti-de Sitter harmonic black-hole oscillators and brane spacetimes where p+2 are the number of bonds predicted or the number of double bonds. (20,25) The complete pharmacophoric fragmentation Scheme was analyzed to compare similar series of chemical patterns that are contained in the chemical phase structures as extracted from within the selected 10 hit compounds of the Colchicine, Raltegravir, Hexacosanol, Benzoxazolinon, Carboxy-Pentaric acid, Ursane, Antheraxanthin, RA-XIII, Crotonate and Byrsonima coccolobifolia. (Tab s1/), (20,35) Whenever searching for a specific pattern, if the group has such a parent pattern, the parent pattern is searched first eliminate the terms in V' (p, which gives i⟨ψi||ρˆ0||ψj⟩ 22vKS1(r)a4cos2θ+a2r2+r4)Σ3'⟨ψi||ρˆ0||ψj⟩Then one can choose n g{t) such as to eliminate the purely time-dependent terms, and one finally arrives at, = (2mV '2 (p through the example of two coupled Chern-Simons Topology driven anti-de Sitter harmonic black-hole oscillators and brane spacetimes where p+2 are the number of bonds predicted or the number of double bonds. (20,25) The complete pharmacophoric fragmentation Scheme was analyzed to compare similar series of chemical patterns that are contained in the chemical phase structures as exctraced from within the selected 10 hit compounds of the Colchicine, Raltegravir, Hexacosanol, Benzoxazolinon, Carboxy-Pentaric acid, Ursane, Antheraxanthin, RA-XIII, Crotonate and Byrsonima coccolobifolia. (Tab s1/), (20,35) Whenever searching for a specific pattern, if the group has such a parent pattern, the parent pattern is searched first eliminate the terms in V' (p, which gives f = -%-r'+ g (t) (equation16). Then one can choose n g{t) such as to eliminate the purely time-dependent terms, and one finally arrives at, = (2mV '2 (p + mf; ■ r' (p = ih (p,ipir, t) =ea h J (pir',t) (equation17). (20,25,26) of the strong equivalence principle in quantum theory.

Roccustyrna ligand Protein targets
The docking engine employed in this computer-aided drug design effort is the DockThor program, which generates preparations of the acceptable topology files for the Roccustyrna ligand for the protein (.in) and ligand cofactors (.top) and a specific input .pdb file containing our prototype's ligand atoms and Roccustyrna partial charges from the MMFF94S49 force field. (19,21,37,42) The .pdbqt file of the Roccustyrna new ligand was generated by the MMFFLigand software, which is based on the utilities of the OpenBabel chemical toolbox for extracting atom types and partial charges with MMFF94S applied force field, and for the identification of the rotatable bonds, and calculating the properties necessary for computing the intramolecular interactions. Subsequently, substructure analysis was performed using DataWarrior 4.2.2, on the proposed SARS-COV-2 multi-target penicillamine derivative ligands predicted by both ligand-based and structure-based techniques (considering docking scores less than or equal to the threshold of the best F measure for each docking model 1.0]hexan-6-yl}) phosphoroso 1-(3,4,5-trifluorooxolan-2yl) -1H-1,2,4-triazole-3-carboxylate in addition to various compounds consisting of the common and frequent heterocycles identified originally in the substructural analysis of the extracted ChEMBL compounds. (16,17,41) In the MMFFLigand, all hydrogen atoms were removed and the PdbThorBox software was utilized to set the protein atomic types and the partial ionization charges from the MMFF94S force field analysis considering the nonpolar atomic groups as implicit to rebuild missing residue side-chain atoms. (3-9,31) Thus, in this KNIME based GEMDOCK-DockThor-Virtual Screening platform, both the Roccustyrna small molecule, SARS-COV-2 protein targets of and cofactors were treated again with the MMFF94S force field by keeping the same set of equations and parameters that define the new molecule's molecular force field parameterizations. (2,(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) The preparations of the steps to be used for diagonal force field for modeling such as modifying the protonation state of all the keeping amino acid residues, to parameterize a simple group of knots and atoms by adding metal complexes, hydrogen atoms, and freezing rotatable bonds was done interactively for a variety of all-Roccustyrna atoms in the publicly available web servers and performed automatically by the programs cited without the need for intervention. (3,15,16,17) The search docking space to rapidly simulate the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM cluster of molecular systems and the configuration of the new molecules grid box were interactively set in the KNIME designed BiogenetoligandorolTM pipeline which was represented as a grid box and the docking potentials are stored at the best grid points for the description of the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM cluster of molecular energetics and structures through the parameters of the center of coordinates, size of the grid and discretization (i.e, the spacing between the grid points∂ P̂ (5,13,29,31) The initial population for the rotational, and translational, was randomly generated within the conformational degrees and grid box using random values of freedom of the Roccustyrna ligand. (15,16,17,38) For each SARS-CoV-2 therapeutic target, DockThor-VS default parameters were uploaded as a recommended set of parameters for the grid box (i.e, center, and grid sizes) which can be used or modified according to the objectives of this docking experiment which was specially designed to deal with highly flexible ligands such as the Roccustyrna small molecule. (15,29,31) In this strategy, a replacement ligand-based method was introduced by using a low mass phase phenotypic steady-state and crowding-based protocol and a multiple genetic parental algorithms as a Dynamic Solution Modified Restricted Tournament Selection (DSMRTS) approach, which provided us a better machine learning exploration of the energetic hypersurface for the identification of multiple quantum phase minima solutions in a single Hadamard run, preserving the population diversity of the generated structures. The default parameters of this parallel docking algorithm (named BiogenetoligandorolTM) are set in the KNIME-web server as follows: (i) 24 inverse docking runs, (ii) 1.000.000 evaluations per parallel docking run, (iii) population of the Roccustyrna individuals, (iv) maximum of 20 cluster small molecule top leaders on each parallel inverse docking run. For this sequential screening experiment, we also provided an alternative dataset of geometric parameters to improve the Euclidean space between the Roccustyrna and protein interacting chains without significantly losing binding site accuracy (named EuTHTS Euclidean Topology Virtual Screening): (i) 120 docking runs, (ii) 200.000 evaluations per docking run, (iii) population of Roccustyrna individuals, (iv) maximum of 20 cluster leaders on each docking run. The docking experiments were performed on DockThor CPU nodes of the Dumont supercomputer, each one containing two processors Intel Xeon E5-2695v2 Ivy Bridge (12c @2,4 GHz) and 64 Gb of RAM memory. We validated the docking experiments through the redocking of the non-covalent Roccustyrna ligand present in the complexes 6W63 (Mpro) using the standard configuration, successfully predicting the co-crystallized conformation of each complex. In the crystallographic structure, this moiety is exposed to the solvent and has insufficient electronic density data. The free energy scoring function applied to score the best-docked poses of the same Roccustyrna ligand was based on the sum of the following terms from the MMFF94S force field and is named -Total Energy (Etotal) ‖: (i) intermolecular interaction energy calculated as the sum of the van der Waals between the hydroxyl and cyano groups (buffering constant = 0.35) and electrostatic potentials between the protein-ligand atom pairs, (ii) intramolecular interaction energy of the van der Waals and electrostatic potentials calculated as the sum between the 1-4 atom pairs, and (iii) torsional term of the ligand.  The PK properties, such as adverse effect predictive modeling, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), of the Roccustyrna pharmacophoric scaffold, were predicted by utilizing the admerSAR v2.0 server (http://lmmd.ecust.edu.cn/admetsar2/) for the prediction of our novel combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands ADMET properties on factors such as membrane permeability [designated by colon cancer cell line (Caco-2) ], human intestinal absorption (HIA), and the status of either P-glycoprotein substrate or inhibitor. Finally, knowledge of these processes and more specifically the ability of the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM cluster of small molecules to penetrate the blood-brain barrier and its metabolism is of crucial importance to evaluate the risk of exposure to toxins and was evaluated by the MATE1, CYP, and OATP1B3 -OATP1B1testing models. The Excretion of the Roccustyrna ligand was estimated by applying the advanced matched molecular pair analysis (MMPA), based on the renal OCT substrate and the toxicity which was then predicted accordingly on the Human Ether-a-go-go-related gene inhibition, mutagenic status, carcinogenic status, and acute oral toxicity default parameters (30,31,40,41) Results

Discussions
In this article, we are improving the speed and accuracy and proposing an alternative topological quantum computing optimization framework through the example of coupled anti-de Sitter black harmonic black-hole oscillators and brane spacetimes for the computation of topological invariants of knots, links, and tangles through a discrete stochastic optimization multithreading procedure that uses nonlinear finite element analysis and a ground structure approach, for quantum repeater networks, as applied to quantum homology inspired Chern-Simons topology evolutionary scalable and fast fragmentation algorithm in which the geometric concepts of proper time enter in the non-relativistic limit (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)39 (32,(35)(36)(37)(38)40) For this reason, the Roccustyrna multi-targeting pharmacophoric element for each Turing pattern was kept as simple as possible and can be geometrically represented at a growing boundary as promising potent and selective anti-viral inhibitor with rationally calculated logical atomic spaces and subatomic subspaces to Higgs branch Representation allowing a vectorial negative docking energy representation against this drug target. (20,25,26) Harmonic resonant excitation by picking up poles of the one-loop determinant and Quantum principal analysis on flow-distributed oscillation waves and few chemical space Turing patterns were applied at a growing boundary for the design of my novel multi-chemo-structure the Roccustyrna small molecule against the crystal structure of COVID-19 main protease in a Lindenbaum-Tarski in a half complex plane generated QSAR automating modeling lead compound design approach. Post-quantum cryptography procedures of QSAR modeling Quantization for generalized heuristic fields when fused together with von Neumann and Dirac formulation states on eigenvalue statements equipped with a natural "inclusion" functor T → T″ for algebraic multi-metrics hope to fix this chemical space problem by developing new cryptographic algorithms that rely on hard problems that, to the best of our knowledge, a quantum computer cannot break. A side channel of a cryptographic algorithm is a way to gather information besides looking at the encrypted data. In this hybrid drug designing approach, we have designed the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM nano-structures by improving the speed and the accuracy of the known docking protocols as represented a system of intrinsically positioned cables filtered before evaluation and triangular bars kinematically stable and structurally valid symmetric formations of connected components, holes, and voids jointed at their ends in the (Coulomb branch) localization formula for ZS3 by hinged connections to form our combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM prototype rigid chemical scaffolds with therapeutic potential against novel respiratory 2019 coronavirus in comparative effectiveness persistent homologies by combined virtual screening and supervised machine learning (19,21,24,41,42) was also observed in this parallel docking energy analysis that our QMMM designed Risitorviffirna interacted onto the (PDB:6WZU) protein targets with the highest docking energy negative score when compared to other FDA-approved and experimental drugs. GisitorviffirnaTM neoligand could also be hypothetically combined with Faldaprevir, Gemigliptin, Raltegravir, Ribavirin, Eflornithine, Cycloserine, Azathioprine, Umifenovir, Darunavir, Baricitinib, Hydroxychloroquine, Minocycline, Azithromycin, and the Linoleic acid, FDA approved drugs and the experimental EIDD2801MK4482) small molecule but not with Minocycline, Azithromycin, Remdesivir, GC376, Histrelin, Doxycycline, Cobicistat, Ritonavir, Colchicine, Bleomycin when targeted at the same (PDB:6WZU) protein targets. In this Schrödinger picture for the system minimum-energy of quantum mechanics the dynamics of quantum states for the  1.0] pentan-2-ylidene]cyano-lambda6-sulfanyl}) methyl]phosphorylidene} amino) -4,6dihydro-1H-purin-6-one[ (2R,5R) -5-(2-methyl-6-methylidene-6,9-dihydro-3H-purin-9-yl) -3methylideneoxolan-2-yl]phosphirane-1-carbonitrile-oxy} imino) -1lambda5, 2lambda5-azaphosphiridin-1ylium. αk represents the parameters of displacement D(α(k)) and φ(·) is nonlinear function. (42,43,44) Conclusions DNA-Protein-Ligand signatures in more general spacetimes enhanced by ZK-based proofs of nonlinear dynamics in an extended cryptography model to encrypt and decrypt chemical data which may be extended to hyper-symmetric equations of Chern-Simons Topology driven motion on a collection of nonlinearly coupled remerging harmonic oscillators. Of special interest here is the fusion pharmacophoric product of quantum reference frame representations from the Hopf algebra structure on Uq , when the universal R matrix may provide the emerging pharmaceutical or medical applications, including medicinal products, gene therapy for biological pacemakers ( (1)(x,y) fLH(1)(x,y) fHL(1)(x,y) fHH(1)(x,y) f1, .., 6=max{fLH(l)(x,y), fLH(l)(x,y),fLH(l)(x,y)} ,∀l=1,2 f7,..,12=min{fLH(l)(x,y), fLH(l)(x,y),fLH(l)(x,y)}, ∀l=1,2 f13,..,18=mean{fLH(l)(x,y), fLH(l)(x,y),fLH(l)(x,y)}, ∀l=1,2 f19, ..,24 of maximum fixed number of times through two coupled Chern-Simons Topology driven anti-de Sitter black harmonic black-hole oscillator by means of ordinary differential equation over the moduli space of fourpunctured spheres. This secret Machine Learning improver key introduces the conditional probabilities between quantum reference frames, which generalize a network of electronic structure communications and can be used in combination with cryptographic algorithms for every connected group G and level k ≥ 0 modular tensor categories that are additively equivalent to Repk(LG) to encrypt and decrypt chemical information. (Tab s6/) Here, for the first time we have generated molecular information systems transmitting "drug repositioning signals" of electron allocations to quantum refence frames by using Chern-Simons harmonic oscillators on anti-de Sitter brane spacetimes for constructing, remerging, and generating chemical and physical small molecule libraries available through publicly web servers. Implementations of in-silico quantum phase cryptographic experiments and fragmentations via the molecular network of the occupied electronic orbitals were re-cored as introduced in this fragment-based and machine-learning virtual screening experiment by employing in-house ligand libraries for the design of a quantum thinking novel multi-chemostructure against the protein targets of COVID-19 main protease. A combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM small molecules (Scheme 1) was generated as the fusion product of chemical space representations when merged into the connection form of (a+ℓ) ta+ℓ{1/12+(||α1′ (  Chern-Simons theories and knot theory algorithms applied in this project into merged pharmacophoric groups. Furthermore, the geometric topology-driven heuristic algorithms that were used in this project are capable of fragmenting and remerging small molecules that could not be fragmented by the algorithm of any of the known reference databases. (2, We have illustrated the power of such a Flexible heuristic algorithm approach interpreted as a distinct quantum circuit, qubit preparations, and certain 1-and 2-qudit gates for automatic molecule fragmentation in a meaningful application to Molecular epidemiology, evolution, [N](a4cos4θ)−j<i⟨ψi||ρˆ0||ψj⟩ 22vKS1(r)a4cos2θ+a2r2+r4)Σ3' ⟨ψi||ρˆ0||ψj⟩ qubits (Scheme 5). We suggest that such ligand-protein binding site topology maps will be useful for building further understanding of the relationship between diagonal chemical descriptors, small molecules, and complex viral biological systems. (43,44) This generalized Hadamard approach is potentially applicable to the discovery of hit matter for novel biological targets, with clinical or genomic features for predicting and rationalizing ligand poly-pharmacology and for predicting new ligand functions. In addition, we suggest that such an objective binding site symmetrized map, which encompasses unliganded cavities, will also be useful for optimizing compound screening collections towards a more complete chemical coverage of multi-targeted pharmacophoric space. Our Biogenetoligandorol platform may also offer utility to researchers to interrogate and organize generalized k-nearest neighbors on an Adaptive Weighted [N](a4cos4θ)− j<i⟨ψi||ρˆ0||ψj⟩ 22vKS1(r)a4cos2θ+a2r2+r4)Σ3'⟨ψi||ρˆ0||ψj⟩ coefficiencies (26,(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42). More specifically, in this project we unified generalized k-nearest values and topology geometric methods for generalized formalisms of k-nearest neighbors of Molecular Pairs (MMP) and von Neumann formulations for Dirac formulation states as a Tipping-Ogilvie and Machine Learning application within the quantum computing context with algebraic multi-metrics characteristics targeting the atomistic level of the protein apparatus of the SARS-COV-2 viral characteristics. An Adaptive Weighted KNN Positioning approach through nonlinear electrodynamics to simulate an advanced quantum mechanical inverse docking algorithm were applied in this unified protocol by providing further insight on a ℓneuron(ι):=φ∘D∘R2∘S∘R1 improver for Chern-Simons Topology Euclidean Geometrics for generating a negative docking energy effect of the highest docking energy value against a specific protein target. (Illustration1) As a result for Entangled Neural Networks and Quantum-Inspired Kappa-Symmetrizing frameworks and by using Chern-Simons normalized Shannon entropy quantities through Tipping-Ogilvie potentials we generated the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM small molecules which were able of producing the highest rates of negative docking energy values when virtually compared with Amprenavir, Asunaprevir, Atazanavir, Boceprevir, Cytarabine, Darunavir, Ritonavir, Sorivudine, Taribavirin