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:
- Conceptualization of Ideas, Formulation or evolution of overarching
research goals and aims.
- Methodology, Development and design of methodology, creation of models
Software, Programming, software development; designing computer
programs; implementation of the computer code and supporting
algorithms; testing of existing code components.
- Validation, Verification, whether as a part of the activity or
separate, of the overall replication/reproducibility of
results/experiments and other research outputs
- Formal analysis Application of statistical, mathematical,
computational, or other formal techniques to analyze or synthesize
study data.
- Investigation, conducting a research and investigation process,
specifically performing the experiments, or data/evidence collection.
- Resources of study materials, reagents, materials, patients,
laboratory samples, animals, instrumentation, computing resources, or
other analysis tools
- Data Curation, Management activities to annotate (produce metadata),
scrub data and maintain research data (including software code, where
it is necessary for interpreting the data itself) for initial use and
later reuse.
- Writing - Original Draft, Preparation, creation, and presentation of
the published work, specifically writing the initial draft (including
substantive translation)
- Writing - Review & Editing Preparation, creation and/or presentation
of the published work by those from the original research group,
specifically critical review, commentary, or revision – including
pre-or post-publication stages.
- Visualization, Preparation, creation, and presentation of the
published work, specifically visualization/ data presentation
- Supervision, Oversight and leadership responsibility for the research
activity planning and execution, including mentorship external to the
core team Project administration, Management and coordination
responsibility for the research activity planning and execution.
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
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