Abstract

We discuss the fact that there is a crucial contradiction within Von Neumann’s theory. We derive a proposition concerning a quantum expected value under an assumption of the existence of the orientation of reference frames in N spin-1/2 systems (1 ≤ N < +∞). This assumption intuitively depictures our physical world. However, the quantum predictions within the formalism of Von Neumann’s projective measurement violate the proposition with a magnitude that grows exponentially with the number of particles. We have to give up either the existence of the directions or the formalism of Von Neumann’s projective measurement. Therefore, Von Neumann’s theory cannot depicture our physical world with a violation factor that grows exponentially with the number of particles. The theoretical formalism of the implementation of the Deutsch-Jozsa algorithm relies on Von Neumann’s theory. We investigate whether Von Neumann’s theory meets the Deutsch-Jozsa algorithm. We discuss the fact that the crucial contradiction makes the quantum-theoretical formulation of Deutsch-Jozsa algorithm questionable. Further, we discuss the fact that projective measurement theory does not meet an easy detector model for a single Pauli observable. Especially, we systematically describe our assertion based on more mathematical analysis using raw data. We propose a solution of the problem. Our solution is equivalent to changing Planck’s constant (h) to a new constant. It may be said that a new type of the quantum theory early approaches Newton’s theory in the macroscopic scale than the old quantum theory does. We discuss how our solution is used in an implementation of Von Neumann’s Theory, Projective Measurement and Quantum Computation Computational mining combined molecular docking-based and pharmacophore-based approach as a target prediction strategy through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores.

Keywords

Von Neumann’s Theory; Projective Measurement; Quantum Computation; Computational mining approach; combined molecular docking-based; pharmacophore-based; target prediction strategy; probabilistic fusion method; target ranking; anti-HIV-I P24-derived; peptide mimic; promising pharmacophores; Quantum Computation; Quantum Measurement Theory; Formalism;

Abstract

In this work the quantum chemistry Tersoff potential in combination with classical trajectory calculations was used to investigate the interaction of the DNA molecule with a carbon nanotube (CNT). The so-called hybrid approach—the classical and quantum-chemical modeling, where the force fields and interaction between particles are based on a definite (but not unique) description method, has been outlined in some detail. In such approach the molecules are described as a set of spheres and springs, thereby the spheres imitate classical particles and the spring the interaction force fields provided by quantum chemistry laws. The Tersoff potential in hybrid molecular dynamics (MD) simulations correctly describes the nature of covalent bonding. The aim of the present work was to estimate the dynamical and structural behavior of the DNA-CNT system at ambient temperature conditions. The dynamical configurations were built up for the DNA molecule interacting with the CNT. The analysis of generated МD configurations for the DNA-CNT complex was carried out. For the DNA-CNT system the observations reveal an encapsulation-like behavior of the DNA chain inside the CNT chain. The discussions were made on Von Neumann’s Theory Projective Measurement Quantum Computational mining combined molecular docking and pharmacophore-based approach on Molecular Dynamics Simulations of the DNA-CNT Interaction Process to Hybrid Quantum Chemistry Potential and Classical Trajectory prediction strategies through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores possible use of the DNA-CNT complex as a candidate material in drug delivery and related systems.

Keywords

Molecular Dynamics Simulations; DNA-CNT Interaction Process; Hybrid Quantum; Chemistry Potential; Classical Trajectory; Approach; Von Neumann’s Theory; Projective Measurement; Quantum Computation; Computational mining approach; combined molecular; docking-based; pharmacophore-based; target; prediction strategy; probabilistic fusion method; target ranking; anti-HIV-I P24-derived peptide; mimic promising pharmacophores;

Abstract

ASARM-peptides are substrates and ligands for PHEX, the gene responsible for X-linked hypophosphatemic rickets (HYP). PHEX binds to the DMP1-ASARM-motif to form a trimeric-complex with α5β3-integrin on the osteocyte surface and this suppresses FGF23 expression. ASARM-peptide disruption of this complex increases FGF23 expression. A 4.2kDa peptide (SPR4) has been previously used that binds to ASARM-peptide and ASARM-motif to DMP1-PHEX interact and by assessing SPR4 for treating inherited hypophosphatemic rickets. Here, we discovered for the first time the GENEA-Bonespemitron-5527, a Computer-aided designed of a SPR4-peptide-mimetic pharmacophoric super-agonist for the regulation of bone metabolism utilizing Computational modelling of biomolecular simulation methods in structural biology interfaces between physics, chemistry and biology on an atomistic scalable literature computer-based discovery of an annotated SPR4-peptide-similar multi-molecular pharmacophoric reverse docked super-agonist scaffold as a canditate bone metabolism regulator.

Keywords

SPR4 peptide mimetic; pharmacophoric; super agonist; regulation; bone-metabolism; scalable Literature Based; β-catenin; Computational modelling; biomolecular systems; simulation methods; structural biology; interfaces; physics chemistry and biology in atomistic biomolecular simulation scalable literature;

Abstract

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug–drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). In this study we analyse the impact of bone active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using assessments of comparison of dynamic and static algorithmic models for predicting drug–drug interactions via inhibition mechanisms for a scalable literature Computer-based discovery of an annotated SPR4-peptide-similar multi-molecular reverse docked super-agonist pharmacophoric scaffold as a canditate bone metabolism regulator.

Keywords

Assessment algorithms; predicting drug–drug interactions; via inhibition mechanisms; comparison; dynamic; static models; scalable literature; Computer-based discovery; annotated SPR4-peptide-similar; multi-molecular pharmacophoric; reverse docked; super-agonist scaffold; canditate regulator; bone metabolism;

Abstract

One of the largest challenges of computational chemistry is calculation of accurate free energies for the binding of a small molecule to a biological macromolecule, which has immense implications in drug development. It is well-known that standard molecular-mechanics force fields used in most such calculations have a limited accuracy. Therefore, there has been a great interest in improving the estimates using quantum-mechanical (QM) methods. We review here approaches involving explicit QM energies to calculate binding affinities, with an emphasis on the methods, rather than on specific applications. Many different QM methods have been employed, ranging from semiempirical QM calculations, via density-functional theory, to strict coupled-cluster calculations. Dispersion and other empirical corrections are mandatory for the approximate methods, as well as large basis sets for the stricter methods. QM has been used for the ligand, for a few crucial groups around the ligand, for all the closest atoms (200–1000 atoms), or for the full receptor–ligand complex, but it is likely that with a proper embedding it might be enough to include all groups within ∼6 Å of the ligand. Approaches involving minimized structures, simulations of the end states of the binding reaction, or full free-energy simulations have been tested in this study on an atomistic scalable literature computer-based discovery of an annotated SPR4-peptide-similar multi-molecular pharmacophoric reverse docked super-agonist scaffold as a canditate bone metabolism regulator.

Abstract

Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted. In the work, we propose an approach to “smart design” of heterostructures (quantum wells and superlattices) based on the combination of Inverse Scattering Problem Method and the direct solution of the eigenvalue problem for the Schrödinger equation with reconstructed potentials. Potential shape reconstructed in this way can be substituted then by some approximation, so that the output spectrum obtained by solving the Schrödinger equation with such approximated potential, differs only slightly from the input one. In our opinion, the approach can be used in many applications, for instance, for developing the new electronic devices such as tunable THz detectors. Haemophilus influenzae type b (Hib) is one of the leading causes of invasive bacterial infection in young children. It is characterized by inflammation that is mainly mediated by cytokines and chemokines. One of the most abundant components of the Hib outer membrane is the P2 porin, which has been shown to induce the release of several inflammatory cytokines. A synthetic peptide corresponding to loop L7 of the porin activates JNK and p38 mitogen-activated protein kinase (MAPK) pathways. It has also been reported that a novel use of the complementary peptide approach to design a peptide that is able to bind selectively to the protein P2, thereby reducing its activity. In this in silico study we used of higher levels of our complement conserved structure ligand based binding pocket drug interactive theory to increase the accuracy of protein-ligand binding affinity predictions, resulting in better hit identification success rates as well as more efficient lead optimization processes. Here, we discovered for the first time the GENEA-Poriflunzaten-5567 a Peptide-mimic novel pharmacoelements complementary to the active loop of porin P2 from Haemophilus influenzae for the annotated modulation of its activity using Molecular simulation methods in arational in silico drug-target flexibility complement “Smart Design” methodology of Quantum Wells and Double-Quantum Wells Structures for the generation of a peptide-mimic novel pharmacoelement binding to the amino acid conserved sequences of the active loop of a Haemophilus influenzae porin P2.

Keywords

combined-applicationknowledge-basedpose-scoringphysical-forcefield-basedhit-scoringfunctions “Smart Design” of Quantum Wells and Double-Quantum Wells Structures A rational in silico drug-target flexibility complement methodology-design for the generation of a peptide-mimic novel pharmacoelement binding to the amino acid conserved sequences of the active loop of a Haemophilus influenzae porin P2, biomolecular simulation, molecular modelling, molecular dynamics, force fields, quantum mechanics/molecular mechanics, quantum chemical modelling

Abstract

In this paper, a novel neural network is proposed based on quantum rotation gate and controlled- NOT gate. Both the input layer and the hide layer are quantum-inspired neurons. The input is given by qubits, and the output is the probability of qubit in the state. By employing the gradient descent method, a training algorithm is introduced. The experimental results show that this model is superior to the common BP networks in Quantum-Inspired Neural Networks with Application in silico drug-target flexibility complement methodology-design for the generation of a peptide-mimic novel pharmacoelement binding to the amino acid conserved sequences of the active loop of a Haemophilus influenzae porin P2.

Keywords

Quantum-Inspired,Neural Networks;Application;rational in silico;drug-target;flexibility;complement methodology-design;generation peptide-mimic;novel pharmacoelement;binding amino acid;conserved sequences; active loop; Haemophilus influenzae porin P2.

Abstract

Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted. The ever-increasing number of tumor-associated antigens has provided a major stimulus for the development of therapeutic peptides vaccines. Tumor-associated peptides can induce high immune response rates and have been developed as vaccines for several types of solid tumors, and many are at various stages of clinical testing. MAGED4B, a melanoma antigen, is overexpressed in oral squamous cell carcinoma (OSCC) and this expression promotes proliferation and cell migration. In previous scientifc projects it has also been identified that 9 short peptides derived from MAGED4B protein are restricted in binding to the HLA subtypes common in the Asian population (HLA-A2, A11, and A24). As a result, we here discovered for the first time the GENEA-Immunomagetor-45700d utilizing the In silico rational Biomolecular simulation and modelling: status, progress and prospects identifications of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies.

Keywords

genetic-algorithm;(meta)-ensembles-approachbinary-classification;ligand-baseddrug-design;MAGED4B;oral cancer immunotherapies; Biomolecular simulation; modelling; status; progress; prospects;Rationally; in silico Identification; immunogenic MAGED4B; peptide-mimetic pharmacophoric; robust agent; potential fragment-library; drug-compound; comprising vaccine mimic; annotated properties; oral cancer immunotherapies;

Abstract

Strategies for CADD vary depending on the extent of structural and other information available regarding the target (enzyme/receptor) and the ligands. Computer-aided drug design (CADD) is an exciting and diverse discipline where various aspects of applied and basic research merge and stimulate each other. In the early stage of a drug discovery process, researchers may be faced with little or no structure activity relationship (SAR) information. The process by which a new drug is brought to market stage is referred to by a number of names most commonly as the development chain or “pipeline” and consists of a number of distinct stages. To design a rational drug, we must firstly find out which proteins can be the drug targets in pathogenesis. In present review we reported a CADD, DNA as target, receptor theory, structure optimization, structure-based drug design, virtual high-throughput screening (vHTS), Computer-Aided Drug Design graph machines as Innovative Tools for the in silico Modeling Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies. Rationally in silico Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies.

Keywords

CADD; HTS; Software;General Purpose; Molecular Modeling; SBDD, Computer-Aided Drug Design; Innovative Tool; Modeling Rationally; in silico Identification; immunogenic; MAGED4B; peptide-mimetic; pharmacophoric; robust agent; potential; fragment-library; drug-compound; vaccine mimic; annotated properties; oral cancer immunotherapies;

Abstract

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein−ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The NNScore: Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes Computer-Aided Drug Design scoring function presented here as an Innovative Tool for the in silico Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies, used either in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.

Keywords

NNScore; Neural-Network-Based; Scoring Function; Characterization; Protein−Ligand Complexes; Computer-Aided Drug Design: immunogenic; MAGED4B peptide-mimetic pharmacophoric; robust agent; potential fragment-library; drug-compound; vaccine mimic; annotated properties; oral cancer immunotherapies;