Conclusions
DNA-Protein-Ligand signatures in more general spacetimes enhanced by ZK-based proofs of nonlinear dynamics may be extended to hyper-symmetric equations of Chern-Simons Topology driven motion of a collection of nonlinearly coupled remerging harmonic oscillators. Of special interest here may be emerging pharmaceutical or medical applications, including medicinal products, gene therapy for biological pacemakers (Farraha et al, 2018), and for the nervous system (Bowers et al, 2011). Certain mathematical problems, such as factoring or discrete logarithms, have the property that solving them is believed to be hard unless you have knowledge of some secret information: a secret key. This secret key can be used in combination with a cryptographic algorithm to encrypt your internet traffic, payment information, or messages such that only the owner (s) of the secret key can decrypt the information. However, basing security on mathematics also introduces mathematical structure in encryptions and secret keys. (Table 6/) Here, for the first time we have generated the drug repositioning ALGORITHMQMMSR01 ROCCUSTYRNA In-Silico approaches through the example of two coupled Chern-Simons Topology driven anti-de Sitter black harmonic black-hole oscillators and brane spacetimes against the COVID-19, not only for constructing, remerging and generating chemical and physical small molecule libraries available through publicly available web servers, but also for the implementation of fragmentation and re-coring in-silico quantum phase cryptographic experiments introducing new fragment-based machine-learning virtual screening experiments and employing in-house ligand libraries applied for the design of a quantum thinking novel multi-chemo-structure against the protein targets of COVID-19 main protease, the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM small molecules (Scheme 1) which is the fusion product of such chemical space representations of negative energy selected representations as merged into the connection form, (a+ℓ) ta+ℓ{1/12+(∣∣α1′(t) ⟩CQ1 t∣ϕ(t) ⟩B+∣α2′(t) ⟩CQ2t∣ϕ(t) ⟩B) } + Fǫexp(iℏpˆ2A2mA +−1 2zEGk: 2zEG ⊗ 2zEG → ℝ (equation70) of the 6,9‐dihydro‐3H‐purin loop group (Scheme 2) (Scheme 3) (Scheme 4). By applying the Biogenetoligandorol accuracy (named EuTHTS Euclidean Topology Virtual Screening) algorithm, a Gravitational Topological (UFs) based Quantum-Parallel Particle Swarm Inspired framework was deployed by using 2D chemical features in which a generalized procedure of Quantization of classical heuristic fields was be fused together with QSAR automating modeling. I finally developed and implemented the two algorithms using natural Euclidean Geometric Topologies and Artificial Intelligence-Driven Predictive Neural Networks, showing that it is possible to well-defined surjective atom mapping and to automate phase group and ligand-based fragmentations based on computed diagonal chemical descriptors. By identifying chemical patterns, I made use of partial small fragment derivatives with the additional MM-PBSA-WSAS binding free energy calculation difficulties that the drug designs we deal with are not orthogonal. (30-42) Both Chern-Simon’s 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. (2,5-42) 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, and phylogeny of SARS coronavirus components, such as qubits (Scheme 5). Our Biogenetoligandorol platform also offers utility to researchers simply wishing to interrogate and organize generalized Hadamard where H ← [1 1; 1 −1]/2; (−1) (i, j) |j (2) (Scheme 6) and control-Z gates data, to create an inventory of available numerical docking ∈{0,1,000,111} and b ∈{++++,−} (Scheme 7) data with particular clinical or genomic features, of the shaded tangle into two-dimensional m ×m matrix I2 ←E (m) ; m2-dimensional vector |Φ〉←I2 →; (*maximally matrix M1 ←I2 −M0; (equation6) (Scheme 8) entangled state*) (*m-dimensional identity*) space such as available datasets or patients with particular mutations and calculate the fusion of the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM cluster of active fragments as it can be applied which may be used to draw independently of its drug identification Hilbert space CSG,k (S1 S ←X ⊗|0〉〈0| + C ⊗|1 W ←S (I ⊗H) M1; (equations7-70), (Scheme 9), capabilities. (26,29-42) More specifically, in this project we implemented a Quantum principal applied and a Kappa-Symmetry inspired Inverse Docking Algorithmic analysis with nonlinear electrodynamics indicated that the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM small molecules generated the highest negative docking energy values when virtually compared with Amprenavir, Asunaprevir, Atazanavir, Boceprevir, Cytarabine, Darunavir, Ritonavir, Sorivudine, Taribavirin, Tenofovir, Valganciclovir, Vidarabine, Lopinavir, Sofosbuvir, Zanamivir, Penciclovir, Nelfinavir, Merimepodib, Maribavir, Indinavir, Inarigivir, Galidesivir, Famciclovir, Faldaprevir FDA approved antiviral drugs against the SARS-COV-2 protein binding sites of the (PDB: 6M2Q) SARS-CoV-2 3CL protease (3CL pro) apo structure (space group C21) protein targets inside the sequence of V-M-ARG-4, V-S-ARG-4, V-S-MET-6, V-M-ALA-7, V-S-PHE-8, V-M-GLY-11, V-M-LYS-12, V-S-LYS-12, V-M-GLU-14, V-S-GLU-14, V-M-GLY-15, V-M-THR-24, V-S-THR-24, V-M-THR-25, V-S-THR-25, V-M-THR-26, V-S-THR-26, V-M-VAL-35, V-S-VAL-35, V-S-ARG-40, V-S-HIS-41, V-M-THR-45, V-M-SER-46, V-S-SER-46, V-S-MET-49, V-M-ASN-53, V-S-ASN-53, V-S-TYR-54, V-M-ALA-70, V-M-GLY-71, V-M-ASN-95, V-S-LYS-97, V-M-PRO-99, V-S-LYS-102, V-S-VAL-104, V-M-ILE-106, V-S-GLN-107, V-M-PRO-108, V-M-GLY-109, V-S-GLN-110, V-M-THR-111, V-S-ASN-119, V-M-GLY-124, V-S-TYR-126, V-M-GLN-127, V-M-CYS-128, V-S-ARG-131, V-S-LYS-137, V-M-LEU-141, V-M-ASN-142, V-S-ASN-142, V-M-GLY-143, V-M-ASN-151, V-S-ASN-151, V-M-ILE-152, V-M-ASP-153, V-S-ASP-153, V-S-SER-158, V-M-MET-165, V-S-MET-165, V-M-GLU-166, V-S-GLU-166, V-M-LEU-167, V-S-PRO-168, V-M-GLU-178, V-M-VAL-186, V-S-VAL-186, V-S-ARG-188, V-M-GLN-189, V-S-GLN-189, V-M-THR-190, V-S-TRP-218, V-M-LEU-220, V-M-ASN-221, V-S-PHE-223, V-M-TYR-237, V-S-TYR-237, V-S-TYR-239, V-M-ASP-245, V-S-ASP-245, V-S-HIS-246, V-S-ILE-249, V-M-GLU-270, V-S-GLU-270, V-S-LEU-271, V-M-LEU-272, V-M-GLN-273, V-M-ASN-274, V-S-ASN-274, V-M-GLY-275, V-M-MET-276, V-M-ASN-277, V-S-ASN-277, V-M-GLY-278, V-M-LEU-286, V-S-LEU-286, V-M-LEU-287, V-S-LEU-287, V-S-ASP-289, V-S-GLU-290, V-S-THR-292, V-S-PRO-293, V-M-PHE-294, V-S-PHE-294, V-S-ARG-298, V-M-GLN-299, V-S-GLN-299, V-M-GLY-302, V-M-VAL-303, V-M-PHE-305. Additionally, the same combination of drug design novelties interacted with the highest docking energy values onto the binding sites of the (PDB: 6WOJ) Structure of the SARS-CoV-2 macrodomain (NSP3) in complex with ADP-ribose of the targeting sequence of V-M-ALA-21, V-M-ASP-22, V-S-ASP-22, V-M-GLU-25, V-S-GLU-25, V-M-ALA-38, V-M-ALA-39, V-S-ASN-40, V-M-TYR-42, V-M-GLY-46, V-M-GLY-47, V-M-GLY-48, V-M-VAL-49, V-S-VAL-49, V-M-ALA-50, V-M-GLY-51, V-M-ALA-52, V-S-LEU-53, V-M-VAL-95, V-S-VAL-95, V-M-VAL-96, V-M-PRO-98, V-S-VAL-100, V-S-ASN-101, V-S-LEU-109, V-S-PRO-125, V-M-LEU-126, V-S-LEU-126, V-M-SER-128, V-M-ALA-129, V-M-GLY-130, V-M-ILE-131, V-S-ILE-131, V-S-PHE-132, V-M-GLY-133, V-M-ALA-134, V-S-PRO-136, V-M-SER-139, V-M-ALA-154, V-M-VAL-155, 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, Vidarabine, Lopinavir, Sofosbuvir, Zanamivir, Penciclovir, Nelfinavir, Merimepodib, Maribavir, Indinavir, Inarigivir, Galidesivir, Famciclovir, Faldaprevir FDA approved antiviral drugs while targeting the PDB:7khp (Figure 9/A), PDB: 6WOJ (Figure 9/B), PDB: 7B3D (Figure 9/C), PDB:6M2Q Figure 9/D), PDB:6LU7 (Figure 9/E), PDB: 6WZU (Figure 9/F), PDB:1XU9 (Figure 9/G), PDB: 3TWU (Figure 9/H), PDB:7BEO (Figure 9/H), PDB:1XAK (Figure 9/I) protein targets. It is probably true that the injudicious use involving the management of these quantum ideas or points can cause problems, it is also true that they do and should play an important role quantum mechanically in this drug discovery field (Figure 7/), (Table 8/), (Figure 8/), (Table 9/), (Figure10/)..
Significant Statements
In this project, I implemented Inverse Docking Algorithms with nonlinear electrodynamics for the cryptographic designing of the combination of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands which generated the highest negative docking energies when compared to other FDA approved small molecules onto the SARS-COV-2 protein targets by solving Chern-Simons Topology Euclidean Geometrics in a Lindenbaum-Tarski equations (1-70) based QSAR automating modeling for practical quantum computing, and Artificial Intelligence-Driven Predictive Neural Networks.
Availability of data and materials
The author confirms that the data supporting the findings of this study are available upon request. Authors will release the atomic coordinates and experimental data upon article publication.
Competing interests
No potential competing interest was reported by the author.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Data Availability Statements
Due to confidentiality agreements, data included in this research work have been generated at Biogenea’s central large-scale facility Department of Biogeneto -ligandorolQMMIDDD/QPRPICA/MACHNOT/ QIICDNNDCA ADMET/QIICDNNDCA Stations and are available upon request.
• Authors’ contributionsAuthor’s diverse contributions to the published work are accurate and agreed.Author has contributed to the below multiple roles:
AcknowledgmentsI would like to cordially express my special thanks of gratitude to my father and teacher (George Grigoriadis Pharmacist) as well as our principal (Nikolaos Grigoriadis Phd Pharmacist) who gave me the opportunity to generalized Hadamard gates, to apply Chern-Simons Topology Geometrics, and to do this wonderful project on the Drug Discovery and Quantum Chemistry topic, for the generation of the RoccustyrnaTM molecule, a ligand targeting COVID-19-SARS-COV-2 SPIKE D614G binding sites.Ancillary Information
Ancillary Informations are attached as a separate file.
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