Search results for "equation"

showing 10 items of 4219 documents

Iterative integral equation methods for structural coarse-graining

2021

In this paper, new Newton and Gauss-Newton methods for iterative coarse-graining based on integral equation theory are evaluated and extended. In these methods, the potential update is calculated from the current and target radial distribution function, similar to iterative Boltzmann inversion, but gives a potential update of quality comparable with inverse Monte Carlo. This works well for the coarse-graining of molecules to single beads, which we demonstrate for water. We also extend the methods to systems that include coarse-grained bonded interactions and examine their convergence behavior. Finally, using the Gauss-Newton method with constraints, we derive a model for single bead methano…

Quantitative Biology::BiomoleculesMonte Carlo methodGeneral Physics and AstronomyInverseRadial distribution functionIntegral equationInversion (discrete mathematics)symbols.namesakeBoltzmann constantConvergence (routing)symbolsApplied mathematicsGranularityPhysical and Theoretical ChemistryMathematicsThe Journal of Chemical Physics
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Intermolecular structure factors of macromolecules in solution: Integral equation results

1999

The inter-molecular structure of semidilute polymer solutions is studied theoretically. The low density limit of a generalized Ornstein-Zernicke integral equation approach to polymeric liquids is considered. Scaling laws for the dilute-to-semidilute crossover of random phase (RPA) like structure are derived for the inter-molecular structure factor on large distances when inter-molecular excluded volume is incorporated at the microscopic level. This leads to a non-linear equation for the excluded volume interaction parameter. For macromolecular size-mass scaling exponents, $\nu$, above a spatial-dimension dependent value, $\nu_c=2/d$, mean field like density scaling is recovered, but for $\n…

Quantitative Biology::BiomoleculesMonte Carlo methodIntermolecular forcepacs:61.20.JaFOS: Physical sciencesCondensed Matter - Soft Condensed MatterFlory–Huggins solution theoryIntegral equationCondensed Matter::Soft Condensed Matterpacs:61.25.HqExcluded volumeExponentSoft Condensed Matter (cond-mat.soft)ddc:530Statistical physicspacs:61.12.ExStructure factorScalingMathematics
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Monte Carlo simulation of polymeric materials: Recent progress

1993

Monte Carlo simulations are presented, dealing with phase diagrams of block copolymer melts and polymer blends, including the unmixing kinetics of the latter systems. The theoretical background is briefly reviewed: Ginzburg-type criteria reveal that in mixtures of long flexible polymers a “crossover” occurs from mean-field behavior (as described by Flory-Huggins theory) to nonclassical Ising-type behavior, and spinodal curves can be unusually sharp. This crossover is demonstrated by large scale simulations of the bond fluctuation model, and it is also shown that for symmetric mixtures the critical temperature scales with chain length as Tc α N. The prefactor in this relation is distinctly s…

Quantitative Biology::BiomoleculesSpinodalMaterials sciencePolymers and PlasticsSpinodal decompositionOrganic ChemistryCrossoverMonte Carlo methodMesophaseCondensed Matter PhysicsIntegral equationCondensed Matter::Soft Condensed MatterPhase (matter)Materials ChemistryStatistical physicsPhase diagramMakromolekulare Chemie. Macromolecular Symposia
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<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>

2015

The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .

Quantitative structure–activity relationshipArtificial neural networkSeries (mathematics)Computer sciencebusiness.industryMachine learningcomputer.software_genreRandom forestSupport vector machineSet (abstract data type)Quadratic equationProteasome inhibitormedicineArtificial intelligencebusinesscomputermedicine.drugProceedings of MOL2NET, International Conference on Multidisciplinary Sciences
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Atom-based 3D-chiral quadratic indices. Part 2: prediction of the corticosteroid-binding globulinbinding affinity of the 31 benchmark steroids data s…

2005

A quantitative structure-activity relationship (QSAR) study to predict the relative affinities of the steroid 'benchmark' data set to the corticosteroid-binding globulin (CBG) is described. It is shown that the 3D-chiral quadratic indices closely correlate with the measured CBG affinity values for the 31 steroids. The calculated descriptors were correlated with biological data through multiple linear regressions. Two statistically significant models were obtained when non-stochastic (R = 0.924 and s = 0.46) as well as stochastic (R = 0.929 and s = 0.46) 3D-chiral quadratic indices were used. A leave-one-out (LOO) approach to model validation is used here; the best results obtained in the cr…

Quantitative structure–activity relationshipClinical BiochemistryPharmaceutical ScienceQuantitative Structure-Activity RelationshipBiochemistryCross-validationStructure-Activity RelationshipQuadratic equationDrug DiscoveryLinear regressionApplied mathematicsComputer SimulationMolecular BiologyTranscortinChromatographyMolecular StructureChemistryOrganic ChemistryComputational BiologyRegression analysisAffinitiesData setDatabases as TopicModels ChemicalTopological indexMolecular MedicineSteroidsBioorganicmedicinal chemistry
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QSAR multi-target in drug discovery: a review.

2013

The main purpose of the present review is to summarize the most significant works up to date in the field of multi-target QSAR (mt-QSAR), in order to emphasize the importance that this technique has acquired over the last decade. Unlike traditional QSAR techniques, mt-QSAR permits to calculate the probability of activity of a given compound against different biological or pharmacological targets. In simple terms, a single equation for multiple outputs. To emphasize more the importance of the mt-QSAR in the field of drug discovery, we also present a novel mt-QSAR model, made on purpose by our research group, for the prediction of the susceptibility of Gram + and Gram - anaerobic bacteria.

Quantitative structure–activity relationshipDrug discoveryQuantitative Structure-Activity RelationshipGeneral MedicineComputational biologyBiologyBioinformaticsMulti targetDrug DiscoverySingle equationMolecular MedicineAnimalsHumansAnaerobic bacteriaMolecular Targeted TherapyAlgorithmsProbabilityCurrent computer-aided drug design
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Atom-Based Quadratic Indices to Predict Aquatic Toxicity of Benzene Derivatives to <i>Tetrahymena pyriformis</i>

2009

The non-stochastic and stochastic atom-based quadratic indices are applied to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity. The used dataset, consisting of 392 benzene derivatives for which toxicity data to the ciliate Tetrahymena pyriformis were available, is divided into training and test sets. The obtained multiple linear regression models are statistically significant (R2 = 0.787 and s = 0.347, R2 = 0.806 and s = 0.329, for non-stochastic and stochastic quadratic indices, respectively) and show rather good stability in a cross-validation experiment (q2 = 0.769 and scv = 0.357, q2 = 0.791 and scv = 0.337, correspondingly). In a…

Quantitative structure–activity relationshipQuadratic equationTest setToxicityLinear regressionTetrahymena pyriformisBiological systemStability (probability)MathematicsAquatic toxicologyProceedings of The 13th International Electronic Conference on Synthetic Organic Chemistry
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Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity.

2005

The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classificat…

Quantitative structure–activity relationshipStochastic ProcessesMolecular modelDatabases FactualChemistryOrganic ChemistryClinical BiochemistryMolecular ConformationPharmaceutical ScienceAtom (order theory)Quantitative Structure-Activity RelationshipModels TheoreticalLinear discriminant analysisBiochemistryAnti-Bacterial AgentsSet (abstract data type)Quadratic equationSimple (abstract algebra)Drug DiscoveryMolecular MedicineComputer SimulationBiological systemMolecular BiologyAntibacterial agentBioorganicmedicinal chemistry
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Applications of Bond-Based 3D-Chiral Quadratic Indices in QSAR Studies Related to Central Chirality Codification

2009

The concept of bond-based quadratic indices is generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design, we have modeled several well-known data sets. In particularly, Cramer's steroid data set has become a benchmark for the assessment of novel QSAR methods. This data set has been used by several researchers using 3D-QSAR approaches. Therefore, it is selected by us for the shake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design, we model the angiotensin-converting enzyme inhibitory activity o…

Quantitative structure–activity relationshipTheoretical computer scienceComputer scienceChemistryOrganic ChemistryComparabilityComputer Science ApplicationsData setSet (abstract data type)Quadratic equationComputational chemistryDrug DiscoveryMolecular symmetryBenchmark (computing)TrigonometryQSAR & Combinatorial Science
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New approach to describe two coupled spins in a variable magnetic field

2021

We propose a method to describe the evolution of two spins coupled by hyperfine i nteraction in an external time- dependent magnetic field. We apply the approach to the case of hyperfine interaction with axial symmetry, which can be solved exactly in a constant, appropriately oriented magnetic field. In order to t reat t he n onstationary d ynamical p roblem, we modify the time-dependent Schrödinger equation through a change of representation that, by exploiting an instantaneous (adiabatic) basis makes the time-dependent Hamiltonian diagonal at any time instant. The solution of the transformed time-dependent Schrödinger FRVBUJPO in the form of chronologically ordered exponents with transpar…

Quantum ComputationPhysicsQuantum PhysicsGeometric PhaseSpinsQuantum Physics; Quantum PhysicsFOS: Physical sciencesSchrödinger equationMagnetic fieldsymbols.namesakeExact solutions in general relativityQuantum mechanicssymbolsHamiltonian (quantum mechanics)Adiabatic processAxial symmetryQuantum Physics (quant-ph)QubitsHyperfine structure
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