1. GENEA-Expacotissor-82381: Identification and computer-aided discovery of a Poly-Chemical motif-like Pharmaco-Peptidomimetic 4D-Structure targeted on the Stem Cell Developmental Stage-Specific Manner Cardiac Differentiation and Proliferation Molecular Pathway.

2. GENEA-Exporistemoren-90290: Algebraically discovery of a novel Poly-Peptide targeted highly conserved chemostructure on a Revealing Core Signaling regulatory mechanism for the Cord Blood Stem Cell Survival and SelfRenewal by introducing a new cluster of algorithms into a virtual mass-low screening of chemical libraries with Super-Agonist Stem Cell Expansion Pathways Insights.

3. GENEA-Vadocanciptoril-109100: Development of a Novel Class of Tubulin Inhibitors with Promising Anticancer Activities targeted on VDAC1-based peptides as novel pro-apoptotic agents and potential therapeutics for B-cell chronic lymphocytic leukemia.

4. GENEA-kiniposamoran-384376: Computer-aided Synthesis and Characterization of a Novel Prostate CancerTargeted PI3 Kinase Inhibitor Prodrug using the BiogenetoligandorolTM: A new cluster of algorithms and Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile.

5. GENEA-Glomeritoron-1045: An in silico high binding free energy affinity value predicted Novel Hyper-MultiTargeted computer-aided Inhibitor against 1045 Glioblastoma conserved motif-like messengerRNA conserved domains using the BiogenetoligandorolTM: A new cluster of algorithms a biochemio-informatic In-silico Drug Discovery Approach.

6. GENEA-Citoferoren-5959: In silico discovery of Small molecule correctors of F508del-CFTR discovered by the BiogenetoligandorolTM: A new cluster of algorithms of structure-based virtual screening molecular tools.

7. GENEA-Potevalimmune-82197: A Rational designed Peptide-based therapeutic vaccine-like pharmacostructure for allergic and autoimmune diseases using the BiogenetoligandorolTM new cluster of algorithms and the PL-PatchSurfer. a novel molecular local surface-based method for exploring protein-ligand interactions.

8. GENEA-ComPuhimirad-72345: In silico discovery and Computer-aided Development of Small-Molecule PUMA Inhibitors for Mitigating Radiation-Induced Cell Death.

9. GENEA-Ebolamimocalor-92990: Rational design of a computer-aided poly-pharmacophore synthetic molecule comprising therapeutic peptide-mimic super-agonistic properties against Ebola virus conserved protein regions using the BiogenetoligandorolTM: A new cluster of algorithms an in silico drug design structure peptidesequence-based combinatorial analysis by a multi-objective cluster of algorithms.

10. GENEA-Ebovapetiagovar-00321: Rational design of a computer-aided poly-pharmacophore synthetic molecule comprising therapeutic peptide-mimic superagonistic properties against to Ebola virus conserved protein regions.

11. GENEA-Plectasefung77: A Rational predicted Plectasin peptide-mimetic pharmacophore comprising antibiotic properties with therapeutic potential from a saprophytic fungus using the BiogenetoligandorolTM: A new cluster of algorithms and a combined MPI-CUDA parallel solution of linear and nonlinear Poisson-Boltzmann equation.

12. GENEA-Pancreovirocan-22234: An in silico rational computer aided discovery of a quantum adenovirus library mirror network displaying random peptidemimic pharmacophore ligands comprising Oncolytic virus therapeutic promising properties for pancreatic cancer using the BiogenetoligandorolTM: A new cluster of algorithms and a polyphony superposition independent methods for ensemble-based drug discovery.

13. GENEA-melohesiporex-89996: Development of a Unique Small Molecule Modulator of CXCR4 tumor-derived heat-shock protein peptide complex-96 (HSPPC-96) using the BiogenetoligandorolTM: A new cluster of algorithms for Hits ranking functional coevolution network of oncogenic mutations in the CDKN2A–CDK6 complex of High-Throughput Screen Identification of the Hydrophobic Pocket of Autotaxin/Lysophospholipase D as an Inhibitory Surface Molecular dynamic simulation and statistical coupling analysis.

14. GENEA-carhomosimar8548: An in silico Low Mass-Algorithm designed potent Poly-Peptide Chemo-Stimulator for the Chondrogenic Activation of Mesenchymal Stem Cell related messenger-RNA over-expressed transcripts using the BiogenetoligandorolTM: A new cluster of algorithms and a Fragment-Based Optimization Analysis of novel RARα and RARβ chemo- superantagonist.

15. GENEA-TovemoSteXamir-5443: In silico designed of Chemical Compounds for Modulating Lineage-Specific Stem Cells and Progenitors.

17. GENEA-Nafiκetuβamur-4098: In silico discovery of novel mechanistic chemo-hyperstructures as a novel drug discovery strategy for the computer aided generation of an enantiomeric antitumor agent targeting dual p53 and NF-κB pathways for the activation of the p53 tumor suppressor pathway by an engineered cyclotide-mimic pharmacophore.

18. GENEA-Cardimocytepor-38234: Discovering novel small peptide-mimetic molecules that promote cardiomyocyte generation by modulating Wnt signaling using the BiogenetoligandorolTM new cluster of algorithms fo the Identification of Chemicals Inducing Cardiomyocyte Proliferation in Developmental Stage-Specific Manner with Pluripotent Stem Cells.

19. GENEA-Nocologenar-3787: Lead identification and computer-aided molecular optimization of novel collagenase inhibitors using the BiogenetoligandorolTM: A new cluster of algorithms and an integrated pharmacophore and structure based studies.

20. GENEA-Hivocyclepir-4536: An In silico Energy potency optimization for the in silico discovery of a poly-target antagonists to HIV-II viral replication cycle associated enzymes using the BiogenetoligandorolTM new cluster of algorithms and a Pocket-Based Drug Design Methodology.

21. GENEA-Litumotranspovax-23112: In silico discovery of Differential inhibitors of LINE1 and LINE2 retrotransposition AID/APOBEC proteins motif derived binding domains.

22. GENEA-Neuroconsetran-35434: An in silico Entropically discovery of a Super-Agonistic activator targeted on Human NeuroPoietic Cell Progenitors motif-peptide related transcripts for the generation of highly Conserved Poly-Peptidomimic Pharmacophores associated to Neural Pathways for Neurodegenerative Disease Modeling utilizing the BiogenetoligandorolTM: A new cluster of algorithms and an advanced Fragment-based Multi-Dimensional Chenico-Informatic approach.

23. GENEA-Glurometabol-32111: Computer-aided molecular discovery and Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using the BiogenetoligandorolTM new cluster of algorithms and Virtual High-Throughput Screening advanced techniques.

24. GENEA-Hicoprotevir-CV922: In Silico Identification and Evaluation of Leads for the Simultaneous Inhibition of Protease and Helicase Activities of HCV NS3/4A Protease Using the BiogenetoligandorolTM new cluster of algorithms and Complex Based Pharmacophore Mapping and Virtual Screening Strategies.

25. GENEA-Malatrohir-66l33: In silico prediction of antimalarial drug target candidates using the BiogenetoligandorolTM new cluster of algorithms and Rational Prediction Techniques of Molecular Dynamics for Hit Identification

26. GENEA-Glydubexifer-74355: In silico Design of Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors using the BiogenetoligandorolTM new cluster of algorithms and a motif-like binding pocket Core Hopping Approach.

27. GENEA-Hivochemoder-88966: Molecular Modeling and in silico prediction of novel chemocompounds targeted on the Allosteric Inhibition Mechanism of HIV-1 Integrase motif like tetrapeptides by LEDGF/p75 Binding Site Inhibitors using the the BiogenetoligandorolTM new cluster of algorithms.

28. GENEA-Adegolunger-83277: An in silico energy optimization approach for the in silico molecular dynamics hit identification, drug discovery and for the Expression-Based In Silico Screening of Candidate Therapeutic Compounds for Lung Adenocarcinoma using the BiogenetoligandorolTM new cluster of algorithms for the a Rational Prediction of novel hyper-structures.

29. GENEA-Chondrecator-99788: An in silico Mass-Algorithm designed of a potent Poly Peptide Chemo Stimulator for the Chondrogenic Activation of Mesenchymal Stem Cell related messenger-RNA over-expressed transcripts using the BiogenetoligandorolTM new cluster of algorithms and a Fragment-Based Optimization analysis of novel RARα and RARβ chemo- superantagonist.

30. GENEA-Ebocompusuvir-9234451: Rational discovery of a computer-aided poly-pharmacophore synthetic molecule comprising therapeutic peptido-mimic super-agonistic properties against Ebola virus conserved fusion protein regions.

31. GENEA-Hivogepavir-12079: Design of pharmaco-scaffold peptide mimetics of HIV-1 gp120 for prevention and therapy of HIV disease using the BiogenetoligandorolTM: A method for inferring novel drug indications with application to personalized medicine.

32. GENEA-Napindicorid-8889: In silico supercomputer-aided predicted of Novel Peptides simulated hyperchemostructures of Therapeutic Promise from Indian Conidae.

33. GENEA-Asipohator-11865: Rational Discovery of adjuvant-induced arthritis by nasal administration of novel synthetic inhibitor peptide-mimetic from heat shock protein 65 using the BiogenetoligandorolTM new cluster of algorithms and an integrated Fpocket open source platform for ligand pocket detection approach.

34. GENEA-Pirehuntimur-2190: In silico discovery of a P42 peptide mimetic poly-pharmacophore as a new weapon to fight Huntington’s disease using the BiogenetoligandorolTM new cluster of algorithms and a Multi-scale Gaussian representation and outlinelearning based cell image segmentation approach.

35. GENEA-Hivogetogarival-22876: In silico discovery of an HIV Type 1 Gag suppressor as a Target for Antiviral Therapy using the BiogenetoligandorolTM new cluster of algorithms for the prediction of a large-scale disease-disease risk relationship knowledge base constructed from biomedical texts.

36. GENEA-Copadimiperogan-3367: Discover of a potent Anticancer activator consisting of CopA3 dimer peptide mimetic pharmacophoric agents targeted in human gastric cancer conserved mRNAS.

37. GENEA-Carbohypeptotum-9899: Computer-aided discovery of Carbohydrate-mimetic peptide polypharmacophore for pan anti-tumor responses.

38. GENEA-Lysolitum-88776: Identification and discovery of a novel lytic peptide mimetic chemical pharmacophore for the treatment of solid tumors.

39. GENEA-ovapetracan-9796: Identification and computer-aided discovery of a novel targeting peptide chemical pharmacophore for human ovarian cancer highly expressed mRNAs from ‘‘one-bead one-compound’’combinatorial libraries.v

40. GENEA-Hitilovusitor-6767: Discovery and in silico Development of Synthetic Peptide mimetic multi-targeted pharmacophore as a novel Potential HTLV-1 Fusion Inhibitor Therapeutics.

41. GENEA-Histochemospanord-7676: In silico discovery of a novel multi-chemostructure superagonist mimetic to the Blood CD34 + Hematopoietic Stem Cells/Hematopoietic Progenitor CellshOX Decoy related Peptide for the Enhancement of the Ex Vivo Expansion of Human Umbilical Cord.

42. GENEA-Insulinopeptir-7556: In silico discovery of an insulin like peptide-mimetic pharmacophore using the BiogenetoligandorolTM: A new cluster of algorithms for the in Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on advanced Chemogenomic Databases.

43. GENEA-Supprevegifur-87345: In silico discovery of a Novel peptide mimetic pharmacostructure suppressor VEGFR-3 activity and antagonize VEGFR-3-mediated oncogenic effects.

44. GENEA-Thrombocovinter-12103: In silico prediction and design of a Single Administration of p2TA (AB103), a CD28antagonist Peptide mimetic chemostructure that Prevents Inflammatory and Thrombotic Reactions and Protects againstGastrointestinal Injuries.

45. GENEA-Inhibalotenoric-3838: The Rational Design of Specific Peptide mimetic Pharmacophore Inhibitor againstp38a MAPK at Allosteric-Sites.

46. GENEA-Haspinatoxider-55221: Rational design of a synthetic chemostructure for the enchacement of the Activation Effect of Hainantoxin-I, a Peptide Toxin mimetic cluster of pharmacophores from the Chinese Spider, Ornithoctonus hainana, on Intermediate-Conductance Ca2+-Activated K+ Channels.

47. GENEA-Anticamphir-2288: Discovery and computer-logic design of a Anticancer mechanistic pharmacophore of action motif-mimetic to of two small amphipathic β2,2-amino acid antimicrobial peptides derivatives.

48. GENEA-Electropermean-8833: Electrical potentiation of the membrane permeabilization by new peptidemimetic in silico designed poly-pharmacophores with anticancer properties.

49. GENEA-Hivenocaxecur-432673: Discovery of a Env sequence targeted multi-pharmacophore determinants in CXCR4-using human immunodeficiency virus type-1 subtype C.

50. GENEA-Mucibolvatron-3123: In siliuco discovery of synthetic pharmacophores as chemogenomic peptide mimetic hyperstructures comprising vaccination properties against the WT1-, Proteinase3- and MUC1-derived peptides in combination with MontanideISA51and CpG7909.

51. GENEA-Survitacelum-63254: In silico designed of a pharmacophore Survivin-specific T-cell reactivity targeted and correlates with tumor responseand patient survival mimetic to the peptide vaccination trialin metastatic melanoma.

52. GENEA-Melopeptimor-25532: Randomized Multicenter Trial of the Effects of a Melanoma-Associated Helper Peptide-mimetic pharmacophore on the Immunogenicity of a Multipeptide Melanoma Vaccine.

53. GENEA-Inducankeglir-27922: An In silico designed pharmacophore for the Induction of CD8_ T-Cell Responses Against NovelGlioma–Associated Antigen Peptides and Clinical Activityby Vaccinations With _-Type 1 Polarized Dendritic Cells and Polyinosinic-Polycytidylic Acid Stabilized by Lysine andCarboxymethylcellulose in Patients With Recurrent MalignantGlioma.

54. GENEA-Ebovacerbur-7744: An Algebraically designed Ebolavirus _-Peptide mimetic Immunoadhesins pharmascaffold for the Inhibition of Marburgvirus andEbolavirus Cell Entry_Assessing Drug Target Association.

55. GENEA-Mimopiruval-I6881: An in silico designed pharmacophore for the Fusion-Inhibiting Peptide mimicking properties against Rift Valley FeverVirus for the Inhibition of Multiple, Diverse Viruses.

56. GENEA-Peponcomore-76987: An In silico computer-aided molecular designed (X-315 (Oncopore™) short synthetic anticancer peptide simulated pharmacopoly-agent mimic to a novel immunotherapeutic agent.

57. GENEA-Wesothelolung-7986: In silico designed of a CD4WT1 vaccination PEPTIDE simulated pharmacophore for the INDUCTION OF THE CD4 AND CD8 T CELLIMMUNE RESPONSES IN PATIENTS WITH MESOTHELIOMA ANDNON-SMALL CELL LUNG CANCER.

58. GENEA-Cetolakador-4990: An In silico predicted and computer-aided molecular designed CTLA-4 blockador for the increasement of the antigen-specific CD8+ T-cells to the inprevaccinated patients with melanoma.

59. GENEA-gepivamelor-2017: A computer simulated gp100 Peptide mimic designed pharmacophore as a Vaccine like and Interleukin-2 superagonist in Patients withAdvanced Melanoma.

60. GENEA-MimoMartelon-67122: Computer designed of a Safe and immunogenic pharmacophore activator mimic physicochemical properties of the MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) inadjuvantwith PF-3512676 and GM-CSF in metastatic melanoma.

61. GENEA-Anticansivast-16886: In silico designed of an Anticancer Peptide SVS-1 multipharmacophore with Efficacy in Preceding Membrane Neutralization using the the BiogenetoligandorolTM new cluster of algorithms.

62. GENEA-Cancerestogan-10100: Algebraically in silico discovered of a multi-epitope mimic polypharmacophore to Multiple Peptides Derived from Cancer-Testis Antigens for the maintance of a Specific T-cell Response in Longterm Vaccinated patients with Multiple Peptides Derived from an Achieve Disease Stability in Advanced Biliary Tract Cancer.

63. GENEA-TeloMirsamor-9877: A Telomerase Peptide Vaccination simulated poly-chemo structure Combined with Temozolomide in A Clinical Trial in Stage IV Melanoma Patients.

64. GENEA-Ikerumimept-79076: A COMPUTER-assisted Identified Ii-Key/HER-2/ neu(776-790) Hybrid Peptide Vaccine poly-mimic chemostructure with active pharmacophore sites in PatientsResults from a Phase I Clinical Research Scientific Project of the Novelwith Prostate Cancer.

65. GENEA-Mikifaprebine-2037: An in silico KIF20A-derivedPeptide mimic designed poly-chemo-scaffold for the Phase I Clinical Trial of Vaccination in Combination with Gemcitabine for PatientsWith Advanced Pancreatic Cancer.

66. GENEA-Compestecovir-7271: A Computational Assay to Design an Epitope-Based Peptide Vaccine Mimetic PharmaScaffold against Saint Louis Encephalitis Virus.

67. GENEA-Watecancetem-9111: An In silico designed Wilms’ Tumour 1 (WT1) peptide mimetic pharmacophore for vaccinated patients with acute myeloid leukaemia inducing a short-lived WT1-specific immune responses Immunogenicity.

68. GENEA-Fusihimopept-1008: An in silico designed Fusion Inhibitor with Greatly PharmacoMimic Properties to a Rationally Engineered Anti-HIV Peptide.

69. GENEA-Motifapoptor-6669: An in silico designed conserved tetrapeptide motif mimetic pharmastructure for the potentiating apoptosis through IAP-binding.

70. GENEA-Autopocrimmune-7584: Computer-aided designed of a TCR Peptide Therapy Mimetic Pharmacophore in Human Autoimmune Diseases.

71. GENEA-Bicytonafer-2889: Rational Design and Optimisation of a Bioactive Cyclic mimetic-Peptide Pharmacophore for the Generation of a Down-Regulator of TNF Secretion by Investigating Drug-Target Association and Dissociation Mechanisms.

72. GENEA-Aposimocor-I009: Rational design of ApoA-I Mimetic-polypharmacophore integrating nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.

73. GENEA-Sapemitor-45345: In silico Structural design of apelin-analogue pharmacophoric agent for the mitochondrial ROS inhibition and cardiometabolic protection in myocardial ischemia-reperfusion injury.

73. GENEA-Antipsorerisikobinor-10715: An in silico rational computer-aided designed of Antimicrobial Peptides Psoriasin (S100A7) and Koebnerisin (S100A15) mimetic pharmacophore for the Suppression of the Extracellular Matrix Production and Proliferation of Human Fibroblasts by Predicting interacting residues.

74. GENEA-Polygadherystinor-76112: Rationally designed of Polycystin-1 and Gα12 poly-mimic pharmacophoric agents for the regulation of the cleavage of E-cadherin in kidney epithelial cells.

75. GENEA-Delivernarex-3308: In silico rationally designed of a Peptide-mimic pharmacologic polychemostructure for the delivery of gene constructs through for efficient internalization.

76. GENEA-Natriolipontin-0073: In silico design of a C-type natriuretic mimetic peptide pharmacophore for the attenuation of lipopolysaccharide-induced acute lung injury.

77. GENEA-Pepavolycanceptor-8845: In silico design of Peptide-based mimetic pharmacophoric vaccine-like agents for cancer therapy using the BiogenetoligandorolTM new cluster of novel integrated frame Research and Scientific algorithms: An improved methodology of computer-aided drug design by Automated docking for novel drug discovery integrating Stochastic voyages into uncharted chemical space library of all possible drug-like compounds on NetResearch and Scientific Project pharmacology strategies toward multi-target anticancer therapies from computational models to experimental design principles.

78. GENEA-Tollarepomir-5579: Depletion of tumor-associated macrophages enhances the anti-tumor immunity induced by a Toll-like receptor agonist-conjugated peptide-mimetic pharmacophoric multi-targeted agent.

79. GENEA-Immunomagetor-45700: Rationally in silico Identification of immunogenic MAGED4B peptidemimetic pharmacophoric agent for a drug of vaccine-mimic comprising properties in oral cancer immunotherapy.

80. GENEA-Poriflunzaten-5567: A rational in silico drug design methodology for the generation of Peptide-mimic novel pharmacoelements complementary to the active loop of porin P2 from Haemophilus influenzae for the modulation of its activity.

81. GENEA-Thionitroxartyl-77802: Computer aided prediction of Thioredoxin peptide-mimetic pharmacophores as novel catalysts of S-denitrosylation and anti-nitrosative stress agents based on Cheminformatics and molecular Mechanics.

82. GENEA-Immunostalechor-8847: Identification of structural pharmacophoric determinants on tau proteinmimic elements essential for its pathological function.

83. GENEA-Duchemonuclebir-99482: Bi-specific splice-switching PMO oligonucleotides conjugatation via a single peptide-mimo-active pharmaco-structure in Duchenne muscular dystrophy.

84. GENEA-Myocardiprom-33890: Meta-node description of a novel in silico drug design methodology for the generation of Anti-inflammatory peptide-mimic pharmacophores for the amelioration of the cardiac progenitor’s dysfunction after myocardial infarction.

85. GENEA-Bonespemitron-5527: Computer-aided designed of a SPR4-peptide-mimetic pharmacophoric superagonist for the regulation of bone metabolism

86. GENEA-Antimamphiler-109: Rational Strategies employed in the in silico design and optimization of synthetic antimicrobial peptide mimic amphiphile-based pharmacophoric agents with promising enhanced therapeutic potentials.

87. GENEA-StacHIVenar-10085: A computer aided predicted of Stapled HIV-1 peptide mimic pharmacophoric poly-agent recapitulating antigenic structures.

88. GENEA-Neutrimmunitor-37890: A multifunctional peptide-mimic pharma-active chemo-structure based on the neutrophil immune defense CAP37 molecule, comprising in-silico antibacterial and wound-healing properties.

89. GENEA-HIVallopen-24976: Computational prediction of anti HIV-1 peptide-mimic pharmastructures of anti HIV-1 activity of HIV-1 P24-derived peptides.

90. GENEA-Aromahibinir-4492: Computational methods for the design of potent peptide mimic aromatase small poly-active inhibitors.

91. GENEA-Slingephotasir-22038: Drug Discovery, Homology modeling and virtual screening approaches to identify potent pharmacophoric-inhibitors of slingshot phosphatase 1.

92. GENEA-Hectubiquten-9921: Discovery of Peptide-mimetic and small molecule inhibitors of HECT-type ubiquitin ligases.

93. GENEA-Alphecanitir-4846: Design of Alpha-Helical Cationic Anticancer Peptide-mimetic Pharmacophores as promising candidates of novel anticancer Drugs utilizing a novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases.

94. GENEA-Sirnosomolic-62234: Design and Evaluation of Endosomolytic Biocompatible Peptide-mimetic Pharmacophores as Carriers for siRNA Delivery using a novel automated lazy learning QSAR (ALL-QSAR) approach and amethod development, applications, and virtual screening of chemical databases.

95. GENEA-AfiMoloplaque-5556: Rationally predicted of β-amyloid Peptide mimetic pharmacogenomics-like structures for the dissociation of Amyloid Plaques in Alzheimer’s disease.

96. GENEA-ilopentinor-9923: In silico designed of a multichemoagent a novel small peptide mimetic noncompetitive antagonist specific targeting of the IL-23 receptor for the down-regulation of the inflammatory response.

97. GENEA-AdevaloCur-65758: Rationally designed a Peptide Vaccination mimetic Poly-Chemo-structure for Previously Treated Advanced Colorectal Cancer.

98. GENEA-Chondrigenoter-11457: An in silico Mass-Algorithm designed potent Poly-Peptide Chemo-Stimulator for the Chondrogenic Activation of Mesenchymal Stem Cell related messenger-RNA over-expressed transcripts using a Fragment-Based Optimization Analysis of novel RARα and RARβ chemo- superantagonist.

99. GEΝΕΑ-Cardiloxepar-3367: Identification and computer-aided discovery of a Poly-Chemical motif-like Pharmaco-Peptidomimetic 4DStructure targeted on the Stem Cell Developmental Stage-Specific Manner Cardiac Differentiation pathway.

100. GENEA-eXpostemoren-4476: Algebraically designed of a novel Poly-Peptide targeted highly conserved chemostructure on a Revealing Core Signaling regulatory mechanism for the Cord Blood Stem Cell Survival and SelfRenewal.

Abstract

Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors [M. D. Altman et al. J. Am. Chem. Soc. 130: 6099–6013, 2008]. Here we have evaluated the new method using the well studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from non-binders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the non-binders. Anti-cytotoxic T-lymphocyte antigen-4 (CTLA-4) antibodies, such as ipilimumab, have generated measurable immune responses to Melan-A, NY-ESO-1, and gp100 antigens in metastatic melanoma. Vaccination against such targets has potential forimmunogenicity and may produce an effector memory T-cell response. It has been previously determined the effect of CTLA-4 blockador on antigen-specific responses following vaccination. In-depth immune monitoring was performed on three ipilimumab-treated patientsprevaccinated with gp100 DNA (IMF-24), gp100209–217 and tyrosinase peptides plus GM-CSFDNA (IMF-32), or NY-ESO-1 protein plus imiquimod (IMF-11). In previous studies it was shown that peripheral blood mononuclearcells were analyzed by tetramer and/or intracellular cytokine staining following 10-day culturewith HLA-A*0201-restricted gp100209–217 (ITDQVPFSV), tyrosinase369–377 (YMDGTMSQV),or 20-mer NY-ESO-1 overlapping peptides, respectively. It has also been evaluated on the PDBbind v2012 core set where istar platform combining with RF-Score manages to reproduce Pearson’s correlation coefficient and Spearman’s correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. Here, we have discovered for the first time an in silico predicted and computer-aided molecular designed CTLA-4 (YMDGTMSQV) mimic blockador for the increasement of the antigen-specific CD8+ T-cells to the inprevaccinated patients with melanoma.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, In silico predicted, computer-aided molecular designed CTLA-4 blockador, increasement, antigen-specific CD8+ T-cells, inprevaccinated patients, melanoma, new cluster, algorithms, Large-Scale Protein-Ligand Docking experiment, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design.

Abstract

Molecular mechanics (MM) methods are computationally affordable tools for screening chemical libraries of novel compounds for sites of P450 metabolism. One challenge for MM methods has been the absence of a consistent and transferable set of parameters for the heme within the P450 active-site. Experimental data indicates that mammalian P450 enzymes vary greatly in the size, architecture, and plasticity of their active sites. Thus, obtaining x-ray based geometries for the development of accurate MM parameters for the major classes of hepatic P450 remains a daunting task. Our previous work with preliminary gas-phase quantum mechanics (QM) derived atomic partial charges, greatly improved the accuracy of docking studies of raloxifene to CYP3A4. Different patterns for substrate docking are also observed depending on the choice of heme model and state. Newly parameterized heme models are tested in implicit and explicitly solvated MD simulations in the absence and presence of enzyme structures, for CYP3A4, and appear to be stable on the nanosecond simulation timescale. The new force field for the various heme states may aid the community for simulations of P450 enzymes and other heme containing enzymes. The prognosis of patients with advanced biliary tract cancer (BTC) is extremely poor and thereare only a few standard treatments. We conducted a phase I trial to investigate the safety, immune response,and antitumor effect of vaccination with four peptides derived from cancer-testis antigens, with a focus ontheir fluctuations during long-term vaccination until the disease had progressed. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics, 25, i305–i312.]. These algorithms have been extensively benchmarked and shown to outperform most existing algorithms. Moreover, several predictions resulting from SMAP-WS have been validated experimentally. Thus far SMAP-WS has been applied to predict drug side effects, and to repurpose existing drugs for new indications. SMAP-WS provides both a user-friendly web interface and programming API for scientists to address a wide range of compute intense questions in biology and drug discovery. Here, we have for the first time discovered a multi-epitope mimic poly-pharmacophore to Multiple Peptides Derived from Cancer-Testis Antigens for the maintance of a Specific T-cell Response in Long-term Vaccinated patients with Advanced Biliary Tract Cancer using the BiogenetoligandorolTM based SMAP-WS chemical informatic parallel web service for structural proteome-wide ligand-binding site comparison.

Keywords

Algebraically in silico discovery, multi-epitope, mimic, poly-pharmacophore, Multiple Peptides, Cancer-Testis, Antigens, anti-tumor, pharmaco-agent, Specific T-cell Response, Long-term, Vaccinated patients, Advanced Biliary Tract Cancer, parallel web service, structural proteome-wide, ligand-binding site, comparison, Cytochrome P450 enzymes, heme force field parameters, molecular mechanics, RESP charges, AMBER, drug-metabolism,

Abstract

Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors [M. D. Altman et al. J. Am. Chem. Soc. 130: 6099–6013, 2008]. Here we have evaluated the new method using the well studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from non-binders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the non-binders. An in silico designed dosimetric autologous living vaccine consisting of with Multiple Wilms’ Tumor 1 WT1-ConSynthetic–Restricted Peptide mimotopic Epitopes RMFPNAPYLP pulsed dendritic cells on a personalized Active Network analysis for asymptomatic or minimally symptomatic metastatic Pancreatic Cancer.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, in silico designed, dosimetric, autologous living vaccine, Multiple Wilms’ Tumor 1 WT1-ConSynthetic–Restricted, Peptide, mimotopic Epitopes, RMFPNAPYLP pulsed, dendritic cells, personalized, Active Network, analysis, asymptomatic, minimally, symptomatic metastatic, Pancreatic Cancer, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design,

Abstract

Quantum mechanics has long been recognized as a counter-intuitive theory, with ideas such as wave-particle duality, quantum superposition and entanglement defying our natural way of thinking. In recent years, these sorts of uniquely quantum properties are being exploited to develop revolutionary technologies, such as quantum cryptography, quantum metrology and perhaps the most well-known example, quantum computation. In the field of quantum computation, the circuit model was used to show that universal quantum computation is possible1, and the circuit model has since been an incredibly successful tool, leading to important quantum algorithms which greatly outperform their classical counterparts2. The circuit model takes advantage of the fact that quantum mechanics allows for the superposition and interference of quantum bits (qubits) in different states to achieve a computational speed-up. However, in addition to the superpositions of states, quantum mechanics also allows for the superposition of quantum circuits3,4—a feature which is not used in the standard quantum circuit model. Nevertheless, such superpositions of quantum circuits are rapidly becoming central to several foundational research programs studying the role of time and causality in quantum theory5,6,7,8,9. These superpositions of quantum circuits (sometimes called a ‘superposition of causal orders’) give rise to new counter-intuitive quantum predictions, and it has recently been predicted that they could provide quantum computers with even further computational advantages8,10. In particular, superimposing quantum circuits, each with a different gate ordering, can allow one to accomplish a specific computational task with fewer quantum gate uses than a quantum computer which has a fixed-gate order10. Quantum computers achieve a speed-up by placing quantum bits (qubits) in superpositions of different states. However, it has recently been appreciated that quantum mechanics also allows one to ‘superimpose different operations’. Furthermore, it has been shown that using a qubit to coherently control the gate order allows one to accomplish a task—determining if two gates commute or anti-commute—with fewer gate uses than any known quantum algorithm. Here we experimentally demonstrate this advantage, in a photonic context, using a second qubit to control the order in which two gates are applied to a first qubit. We create the required superposition of gate orders by using additional degrees of freedom of the photons encoding our qubits. The new resource we exploit can be interpreted as a superposition of causal orders, and could allow quantum algorithms to be implemented with an efficiency unlikely to be achieved on a fixed-gate-order quantum computer.An in silico designed dosimetric autologous living vaccine consisting of with Multiple Wilms’ Tumor 1 WT1-ConSynthetic–Restricted Peptide mimotopic Epitopes RMFPNAPYLP pulsed dendritic cells on a personalized Active Network analysis for asymptomatic or minimally symptomatic metastatic Pancreatic Cancer.

Keywords

Experimental superposition of orders of quantum gatesAn in silico designed dosimetric autologous living vaccine consisting of with Multiple Wilms’ Tumor 1 WT1-ConSynthetic–Restricted Peptide mimotopic Epitopes RMFPNAPYLP pulsed dendritic cells on a personalized Active Network analysis for asymptomatic or minimally symptomatic metastatic Pancreatic Cancer.

Abstract

It is well known that quantum computers are superior to classical computers in efficiently simulating quantum systems. Here we report the first experimental simulation of quantum tunneling through potential barriers, a widespread phenomenon of a unique quantum nature, via NMR techniques. Our experiment is based on a digital particle simulation algorithm and requires very few spin-1/2 nuclei without the need of ancillary qubits. The occurrence of quantum tunneling through a barrier, together with the oscillation of the state in potential wells, are clearly observed through the experimental results. This experiment has clearly demonstrated the possibility to observe and study profound physical phenomena within even the reach of small quantum computers. Quantum simulation is one of the most important aims of quantum computation ever since Feynman studied the likelihood of simulating one quantum system by another1. Recent years have witnessed fruitful results in the development of quantum computation, and it has been demonstrated that quantum computers can solve certain types of problems with a level of efficiency beyond the capability of classical computers2,3,4,5,6, among which the simulation of the dynamics of quantum systems is especially attractive because of the exponential improvement in computational resources and speeds. Quantum simulation has become a subject of intense investigation and has been realized in various situations, such as system evolution with a many-body interaction Hamiltonian7,8,9,10, the dynamics of entanglement11,12, quantum phase transitions13,14, and calculations of molecular properties15,16,17,18,19. Since we live in a dirty environment, we have developed many host defenses to contend with microorganisms. The epithelial lining of our skin, gastrointestinal tract and bronchial tree produces a number of antibacterial peptides, and our phagocytic neutrophils rapidly ingest and enzymatically degrade invading organisms, as well as produce peptides and enzymes with antimicrobial activities. Some of these antimicrobial moieties also appear to alert host cells involved in both innate host defense and adaptive immune responses.RNAs fold into intricate and precise secondary structures. In this study for the first time we have been evaluted experimental simulation of Novel procedure quantum tunneling in small Computational Scaffolding systems on tumorigenic stem cell bacterial infected hybrids for the in silico rescaffolding and side-chain optimization on the neutrophil immune defense CAP37 protein.

Keywords

Novel procedure Computational Scaffolding, tumorigenic stem cell, bacterial infected hybrids, in silico rescaffolding, side-chain optimization, neutrophil immune defense, CAP37 protein, Experimental simulation, quantum tunneling, small systems.

Abstract

As quantum counterpart of classical computer, quantum computer reveals incredible efficiency to execute arithmetic tasks and threatens the security of classical communication. Quantum algorithm is the sole of quantum computation, which shows the amazing power of quantum parallelism and quantum interference. It attracts particular concern to develop new quantum algorithms in recent years. The concept of simulating physics progresses with quantum computers was originated in Richard Feynman’s observation that computers built from quantum mechanical components would be ideally suited to simulating quantum mechanics1. Since then, the first efficient quantum algorithm was proposed by Deutsch in 19852 and generalized by Deutsch and Jozsa in 19873. Lately, an increasing number of practical programs were presented, such as factoring large integer4, Grover’s searching algorithm for database5 and Simon’s exponential acceleration algorithm for the black box problem6. What’s more, Harrow et al. came up with a quantum scheme to decrease the computational complexity of solving linear system of equations from O(n) to log(n) , and this was the first quantum algorithm to work out the most fundamental problems in engineering science7. Some quantum algorithms have been demonstrated in different physical systems, such as ion traps8,9,10,11, superconducting devices12,13,14, optical lattices15,16, quantum dots17,18, and linear optics19,20,21,22,23,24,25. Due to its good scalability, easy-handling and high stability, linear optical system is a good candidate for implementing quantum algorithms.We report an experiment to demonstrate a quantum permutation determining algorithm with linear optical system. By employing photon’s polarization and spatial mode, we realize the quantum ququart states and all the essential permutation transformations. The quantum permutation determining algorithm displays the speedup of quantum algorithm by determining the parity of the permutation in only one step of evaluation compared with two for classical algorithm. This experiment is accomplished in single photon level and the method exhibits universality in high-dimensional quantum computation.Combinatorial learning procedures and graph transformations for the discovery of tumor-like cardiomyocyte derived eletroporated combined hybrids on a Meta-Dynamic Meta-node stemness reconstructing approach for the in silico generation of a anti-(JAM-A) drug-construct.

Keywords

Demonstration, quantum permutation algorithm, single photon, ququart, Combinatorial learning procedures, graph transformations, discovery tumor-like cardiomyocyte, eletroporated, combined hybrids, Meta-Dynamic, Meta-node, stemness, reconstructing, approach, in silico, generation, anti-(JAM-A), drug-construct.

Abstract

We report an experiment to demonstrate a quantum permutation determining algorithm with linear optical system. By employing photon’s polarization and spatial mode, we realize the quantum ququart states and all the essential permutation transformations. The quantum permutation determining algorithm displays the speedup of quantum algorithm by determining the parity of the permutation in only one step of evaluation compared with two for classical algorithm. This experiment is accomplished in single photon level and the method exhibits universality in high-dimensional quantum computation. As quantum counterpart of classical computer, quantum computer reveals incredible efficiency to execute arithmetic tasks and threatens the security of classical communication. Quantum algorithm is the sole of quantum computation, which shows the amazing power of quantum parallelism and quantum interference. It attracts particular concern to develop new quantum algorithms in recent years. The concept of simulating physics progresses with quantum computers was originated in Richard Feynman’s observation that computers built from quantum mechanical components would be ideally suited to simulating quantum mechanics1. Since then, the first efficient quantum algorithm was proposed by Deutsch in 19852 and generalized by Deutsch and Jozsa in 19873. Lately, an increasing number of practical programs were presented, such as factoring large integer4, Grover’s searching algorithm for database5 and Simon’s exponential acceleration algorithm for the black box problem6. What’s more, Harrow et al. came up with a quantum scheme to decrease the computational complexity of solving linear system of equations from O(n) to log(n) , and this was the first quantum algorithm to work out the most fundamental problems in engineering science7. Some quantum algorithms have been demonstrated in different physical systems, such as ion traps8,9,10,11, superconducting devices12,13,14, optical lattices15,16, quantum dots17,18, and linear optics19,20,21,22,23,24,25. Due to its good scalability, easy-handling and high stability, linear optical system is a good candidate for implementing quantum algorithms. Here, for the first time we have performed a fast stochastic optimization of a quantum permutation algorithm with a single photon ququart algorithm on DC-tumor like high yield minimal magnetic signatures of electrotransfectioned ex vivo mediated hybrids for the generation of a computer-aided designed candidate drugable Toll-like receptor (Pam2IDG) peptide-domain agonistic agent.

Keywords

Fast stochastic optimization algorithm, DC-tumor like high yield, minimal magnetic signatures, electrotransfectioned ex vivo mediated hybrids, generation, computer-aided, designed candidate, drugable, Toll-like receptor, (Pam2IDG) peptide-domain, agonistic agent, demonstration, quantum permutation, algorithm, single, photon ququart.

Abstract

The structure of peptide p6.7, a mimotope of the nicotinic receptor ligand site that binds alpha-bungarotoxin and neutralizes its toxicity, was compared to that of the acetylcholine binding protein. The central loop of p6.7, when complexed with alpha-bungarotoxin, fits the structure of the acetylcholine binding protein (AChBP) ligand site, whereas peptide terminal residues seem to be less involved in toxin binding. The minimal binding sequence of p6.7 was confirmed experimentally by synthesis of progressively deleted peptides. Affinity maturation was then achieved by random addition of residues flanking the minimal binding sequence and by selection of new alpha-bungarotoxin binding peptides on the basis of their dissociation kinetic rate. The MAP peptide binds alpha-bungarotoxin in solution and inhibits its binding to the receptor with a K(A) and an IC(50) similar to the monomeric peptide. Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide: e.g. stability against proteolysis (duration of activity) and poor bioavailability. In this regard, we discuss its potential to become a routinely used drug design tool of QM/MM in rational drug discovery and molecular diversity for the construction of anti-alpha-bungarotoxin binding peptide mimetic antidotes consisting of essential elements with high affinity and promised vivo efficiency.

Keywords

QM/MM, rational drug discovery, molecular diversity, construction, anti-alpha-bungarotoxin, binding peptide, mimetic antidotes, elements, high affinity, MAP-p6.7 peptide mimetic ligand, nicotinic receptor, binding site, potent snake neurotoxin, synthetic antidote, merging, scoring, molecular interactions,

Abstract

The Schrödinger perturbation energy for an arbitrary order N of the perturbation has been presented with the aid of a circular scale of time. The method is of a recurrent character and developed for a non-degenerate quantum state. It allows one to reduce the inflation of terms necessary to calculate known from the Feynman’s diagrammatical approach to a number below that applied in the original Schrödinger perturbation theory. he structure of peptide p6.7, a mimotope of the nicotinic receptor ligand site that binds alpha-bungarotoxin and neutralizes its toxicity, was compared to that of the acetylcholine binding protein. The central loop of p6.7, when complexed with alpha-bungarotoxin, fits the structure of the acetylcholine binding protein (AChBP) ligand site, whereas peptide terminal residues seem to be less involved in toxin binding. The minimal binding sequence of p6.7 was confirmed experimentally by synthesis of progressively deleted peptides. Affinity maturation was then achieved by random addition of residues flanking the minimal binding sequence and by selection of new alpha-bungarotoxin binding peptides on the basis of their dissociation kinetic rate. The MAP peptide binds alpha-bungarotoxin in solution and inhibits its binding to the receptor with a K(A) and an IC(50) similar to the monomeric peptide. Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide: e.g. stability against proteolysis (duration of activity) and poor bioavailability. In this regard, we discuss its potential to become a routinely used drug design tool of QM/MM in rational drug discovery and molecular diversity for the construction of anti-alpha-bungarotoxin binding peptide mimetic antidotes consisting of essential elements with high affinity and promised vivo efficiency. Finally, we applied to the Circular Scale of Time computations as a Way of Calculating the Quantum-Mechanical Perturbation Energy Given by the Schrödinger Method in QM/MM rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote.

Keywords

Quantum-Mechanical, Perturbation Energy, Circular Scale of Time, QM/MM, rational drug discovery, molecular diversity, construction, anti-alpha-bungarotoxin, binding MAP-p6.7 peptide, mimetic ligand, against nicotinic receptor, binding site, potent snake neurotoxin, synthetic antidote, Circular Scale of Time, Way of Calculating, Quantum-Mechanical, Perturbation Energy, Schrödinger Method