Search results for "Quantitative Structure-Activity Relationship"

showing 10 items of 113 documents

Role of hydrophobicity on the monoamine receptor binding affinities of central nervous system drugs: a quantitative retention-activity relationships …

2004

Abstract Biological action and activity reflect an aspect of the fundamental physicochemical properties of the bioactive compounds. As an alternative to classical QSAR studies, in this work different quantitative retention–activity relationships (QRAR) models are proposed, which are able to describe the role of hydrophobicity on the binding affinity to different brain monoamine receptors (H 1 -histamine, α 1 -noradrenergic and 5-HT 2 -serotonergic) of different families of psychotherapeutic drugs. The retention of compounds is measured in a biopartitioning micellar chromatography (BMC) system using Brij-35 mobile phases. The adequacy of the QRAR models developed is due to the fact that both…

Steric effectsQuantitative structure–activity relationshipStereochemistryClinical BiochemistryQuantitative Structure-Activity RelationshipSerotonergicBiochemistryAnalytical ChemistryReceptors Biogenic AmineReceptors Adrenergic alpha-1AnimalsReceptors Histamine H1ReceptorMicellesChromatographyChromatographyMolecular StructureChemistryCell MembraneBrainCell BiologyGeneral MedicineAffinitiesMonoamine neurotransmitterSerotonin 5-HT2 Receptor AntagonistsPharmacophoreReceptors Serotonin 5-HT2Quantitative analysis (chemistry)Central Nervous System AgentsJournal of chromatography. B, Analytical technologies in the biomedical and life sciences
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Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification

2008

The recently introduced non-stochastic and stochastic bond-based linear indices are been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These improved modified descriptors are applied to several well-known data sets to validate each one of them. Particularly, Cramer's steroid data set has become a benchmark for the assessment of novel quantitative structure activity relationship methods. This data set has been used by several researchers using 3D-QSAR approaches such as Comparative Molecular Field Analysis, Molecular Quantum Similarity Measures, Comparative Molecular Moment Analysis, E-state, Mapping Prope…

Stochastic ProcessesQuantitative structure–activity relationshipIndolesProperty (programming)ChemistryComparabilityQuantitative Structure-Activity RelationshipAngiotensin-Converting Enzyme InhibitorsStereoisomerismGeneral ChemistrySet (abstract data type)Data setComputational MathematicsModels ChemicalPiperidinesComputational chemistryDrug DesignBenchmark (computing)Molecular symmetryCombinatorial Chemistry TechniquesReceptors sigmaThermodynamicsTrigonometryAlgorithmJournal of Computational Chemistry
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Molecular orbital studies on brominated diphenyl ethers. Part II—reactivity and quantitative structure–activity (property) relationships

2005

Polybrominated diphenyl ethers (PBDEs) are widely used as flame retardants and are increasingly turning up in the environment. Their structural similarities to polychlorinated biphenyls and thyroid hormones suggest they may be a risk to human health. The present study examines the reactivity of brominated diphenyl ethers (BDEs) on the basis of the electronic structures as calculated by semiempirical AM1 self-consistent field molecular orbital (SCF-MO) method. Frontier orbital energies were used to elucidate the reactivity of BDEs in electrophilic, nucleophilic and photolytic reactions. From an examination of the frontier electron densities, the regioselectivity, or orientation, of metabolic…

Thyroid HormonesQuantitative structure–activity relationshipChromatography GasEnvironmental EngineeringHealth Toxicology and MutagenesisPolybrominated BiphenylsMolecular ConformationQuantitative Structure-Activity RelationshipEtherChemistry Techniques AnalyticalMass Spectrometrychemistry.chemical_compoundPolybrominated diphenyl ethersComputational chemistryAb initio quantum chemistry methodsEnvironmental ChemistryOrganic chemistryMolecular orbitalReactivity (chemistry)LuciferasesFlame RetardantsPhenyl EthersPublic Health Environmental and Occupational HealthRegioselectivityGeneral MedicineGeneral ChemistryPollutionchemistryElectrophileChemosphere
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Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective.

2020

The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two tox…

ToxicophoreModels MolecularQuantitative structure–activity relationshipCell SurvivalRecombinant Fusion ProteinsQuantitative Structure-Activity RelationshipBioengineeringComputational biology01 natural sciencesSmall Molecule LibrariesCell Line TumorDrug DiscoveryCALUXBioassayAnimalsToxicologiaLuciferase GeneLuciferasesbiology010405 organic chemistryChemistryRobustness (evolution)Reproducibility of ResultsGeneral Medicinerespiratory systemAryl hydrocarbon receptor0104 chemical sciences010404 medicinal & biomolecular chemistryEstructura químicaReceptors Aryl Hydrocarbonbiology.proteinMolecular MedicineEnvironmental PollutantsAntagonismProteïnesSAR and QSAR in environmental research
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Topological virtual screening: a way to find new anticonvulsant drugs from chemical diversity.

2003

A topological virtual screening (tvs) test is presented, which is capable of identifying new drug leaders with anticonvulsant activity. Molecular structures of both anticonvulsant-active and non active compounds, extracted from the Merck Index database, were represented using topological indexes. By means of the application of a linear discriminant analysis to both sets of structures, a topological anticonvulsant model (tam) was obtained, which defines a connectivity function. On the basis of this model, 41 new structures with anticonvulsant activity have been identified by a topological virtual screening.

Virtual screeningBasis (linear algebra)Databases FactualMolecular StructureChemistryOrganic ChemistryClinical BiochemistryPharmaceutical ScienceDiscriminant AnalysisQuantitative Structure-Activity RelationshipTopologyLinear discriminant analysisBiochemistryDatabase indexChemical diversityDrug DesignDrug DiscoveryMolecular MedicineAnticonvulsantsComputer SimulationMolecular BiologyAnticonvulsant drugsTopology (chemistry)Bioorganicmedicinal chemistry letters
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Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse…

2008

The great cost associated with the development of new anabolic-androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic-androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test al…

Virtual screeningQuantitative structure–activity relationshipAnabolismChemical PhenomenaQuantitative Structure-Activity RelationshipComputational biologyLDA-assisted QSAR modelLigandsPattern Recognition AutomatedAnabolic AgentsMolecular descriptorCluster AnalysisComputer SimulationVirtual screeningMolecular StructureChemistryChemistry PhysicalDiscriminant AnalysisReproducibility of ResultsGeneral ChemistryLinear discriminant analysisCombinatorial chemistryAnabolic–androgenic ratioComputational MathematicsPattern recognition (psychology)Quantum and physicochemical molecular descriptorQuantum TheorySteroidsAnabolic–androgenic steroidAlgorithmsJournal of computational chemistry
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Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear dis…

2009

Herein we present results of a quantitative structure-activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the …

Virtual screeningQuantitative structure–activity relationshipModels StatisticalMolecular StructureStochastic modellingOrganic chemicalsStereochemistryCell SurvivalBondTrypanosoma cruziLinear modelPharmaceutical ScienceValue (computer science)Discriminant AnalysisQuantitative Structure-Activity RelationshipLinear discriminant analysisTrypanocidal AgentsQuadratic equationDrug DiscoveryApplied mathematicsComputer-Aided DesignBiological systemCells CulturedMathematicsEuropean journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
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Modeling anti-allergic natural compounds by molecular topology.

2013

Molecular topology has been applied to the search of QSAR models able to identify the anti-allergic activity of a wide group of heterogeneous compounds. Through the linear discriminant analysis and artificial neural networks, correct classification percentages above 85% for both the training set and the test set have been obtained. After carrying out a virtual screening with a natural product library, about thirty compounds with theoretical anti-allergic activity have been selected. Among them, hesperidin, naringin, salinomycin, sorbitol, curcumol, myricitrin, diosmin and kinetin stand out. Some of these compounds have already been referenced as having anti-allergic activity.

Virtual screeningQuantitative structure–activity relationshipStereochemistryOrganic ChemistryDiosminDiscriminant AnalysisQuantitative Structure-Activity RelationshipGeneral MedicineComputational biologyLinear discriminant analysisModels BiologicalComputer Science Applicationschemistry.chemical_compoundHesperidinchemistryArtificial IntelligenceTest setDrug DiscoveryAnti-Allergic AgentsmedicineHumansNeural Networks ComputerMyricitrinNaringinmedicine.drugCombinatorial chemistryhigh throughput screening
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Retention pharmacokinetic and pharmacodynamic parameter relationships of antihistamine drugs using biopartitioning micellar chromatography

2001

Abstract Antihistamines are drugs which act by competitive inhibition of the H1 or H2 histamine receptors. Little has been known about their clinical pharmacokinetics and biological responses until the last few years. In this paper, we propose quantitative retention–activity relationship, QRAR, models based on the retention data of antihistamines in a biopartitioning micellar chromatography (BMC) system using a Brij35 mobile phase for describing pharmacokinetic parameters such as half-life and volume of distribution, or the pharmacodynamic parameters, therapeutic plasma levels, lethal doses and drug-receptor dissociation constant. The predictive ability of these models is statistically vali…

Volume of distributionQuantitative structure–activity relationshipChromatographyChemistrymedicine.medical_treatmentQuantitative Structure-Activity RelationshipGeneral ChemistryHigh-performance liquid chromatographyDissociation constantPharmacokineticsPharmacodynamicsLipophilicityHistamine H1 AntagonistsmedicineSpectrophotometry UltravioletAntihistamineChromatography LiquidJournal of Chromatography B: Biomedical Sciences and Applications
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1-methil-3H-pyrazolo[1-2-a]benzo[1-2-3-4]tetrazin-3-ones, design synthesis and biological activity of new antitumoral agents

2005

1-Methylpyrazolo[1,2-a]benzo[1,2,3,4]tetrazin-3-ones 4, synthesized in good to excellent yields, were designed as novel alkylating agents because of their peculiar chemical behavior. All derivatives showed antiproliferative activity against more than 50 types of tumor cell lines with GI50 reaching sub-micromolar values. SAR studies revealed that the presence of a chlorine atom is well-tolerated in both positions 8 and 9, whereas in the case of the methyl group, switching from the 8 to the 9 position gives rise to the most active compound of the series, 4g, either for the number of cell lines inhibited and for selectivity against leukaemia and renal cancer subpanels. COMPARE and 3D-MIND comp…

antiproliferative activityQuantitative structure–activity relationshipStereochemistry2-a]benzotetrazinoneQuantitative Structure-Activity RelationshipRifamycinsAntineoplastic Agents1-Methylpyrazolo[12-a]benzo[1234]tetrazin-3-oneChemical synthesischemistry.chemical_compoundantiproliferativeCell Line TumorDrug DiscoveryCOMPARE and 3D-MIND analysisHumansComputer Simulationpyrazolo[1CytotoxicityBiological activityCytidinechemistryDrug Designantitumor agentMolecular MedicinePyrazolesDrug Screening Assays AntitumorSelectivityHeterocyclic Compounds 3-RingMethyl group
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