Search results for "Quantitative structure"

showing 10 items of 192 documents

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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In Silico Prediction of Caco-2 Cell Permeability by a Classification QSAR Approach

2011

In the present study, 21 validated QSAR models that discriminate compounds with high Caco-2 permeability (Papp ≥8×10(-6)  cm/s) from those with moderate-poor permeability (Papp <8×10(-6)  cm/s) were developed on a novel large dataset of 674 compounds. 20 DRAGON descriptor families were used. The global accuracies of obtained models were ranking between 78-82 %. A general model combining all types of molecular descriptors was developed and it classified correctly 81.56 % and 83.94 % for training and test sets, respectively. An external set of 10 compounds was predicted and 80 % was correctly assessed by in vitro Caco-2 assays. The potential use of the final model was evaluated by a virtual s…

Virtual screeningQuantitative structure–activity relationshipIn silicoOrganic ChemistryComputational biologyBiologyBioinformaticsComputer Science ApplicationsStructural BiologyMolecular descriptorDrug DiscoveryHuman intestinal absorptionMolecular MedicineCell permeabilityMolecular Informatics
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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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In silicoAntibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

2015

In the recent times, the race to cope with the increasing multidrug resistance of pathogenic bacteria has lost much of its momentum and health professionals are grasping for solutions to deal with the unprecedented resistance levels. As a result, there is an urgent need for a concerted effort towards the development of new antimicrobial drugs to stay ahead in the fight against the ever adapting bacteria. In the present report, antibacterial classification functions (models) based on the topological molecular computational design-computer aided >rational> drug design (TOMOCOMD-CARDD) atom-based non-stochastic and stochastic bilinear indices are presented. These models were built using the li…

Virtual screeningQuantitative structure–activity relationshipVirtual screeninglinear discriminant analysisLinear discriminant analysisQSARTOMOCOMD-CARDD softwareIn silicoDegrees of freedom (statistics)Bilinear interpolationNanotechnologyGeneral Chemistryatom-based bilinear indexvirtual screeningLinear discriminant analysisRange (mathematics)antibacterial activityAtom-based bilinear indexAntibacterial activityBiological systemAntibacterial activityMathematicsJournal of the Brazilian Chemical Society
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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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Biopartitioning micellar chromatography: An alternative high-throughput method for assessing the ecotoxicity of anilines and phenols

2007

An investigation of the use of the chromatographic retention (log k) as an in vitro approach for modelling the toxicity to Fathead Minnows of anilines and phenols is developed. A data set of 65 compounds with available experimental toxicity data was used. Log k data at three pH values were used for the compounds classification and two groups or 'MODEs' were identified. For one 'MODE' a quantitative retention-activity relationship (QRAR) model was calculated. Finally, it was used to estimate the toxicity to Fathead minnows of anilines and phenols for which experimental data are not available. These estimations were compared to those obtained from another toxicity (to Tetrahymena pyriformis) …

chemistry.chemical_classificationQuantitative structure–activity relationshipAniline CompoundsChromatographyToxicity dataTetrahymena pyriformisClinical BiochemistryCyprinidaeQuantitative Structure-Activity RelationshipAromatic amineExperimental dataCell BiologyGeneral MedicineBiochemistryAnalytical Chemistrychemistry.chemical_compoundPhenolschemistryTetrahymena pyriformisToxicityAnimalsSpectrophotometry UltravioletPhenolsEcotoxicityChromatography Micellar Electrokinetic CapillaryJournal of Chromatography B
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Application of Molecular Topology to the Prediction of the Reaction Yield and Anticancer Activity of Imidazole and Guanidine Derivatives

2013

In this study molecular topology based QSAR has been applied to predict the reaction yield and anticancer activity of 18 imidazole and guanidine derivatives. Four properties were evaluated, namely reaction yield, anti prostatic-cancer activity, anti breast-cancer activity and anti lung-cancer activity. The four models have been validated by both internal and cross validation, and also by randomness tests. The results obtained are in full agreement with the experimental results and confirm the precision, accuracy and robustness of the method followed. After carrying out a virtual screening upon such models, new imidazole and guanidine derivatives with potential anticancer activity are propos…

chemistry.chemical_compoundVirtual screeningQuantitative structure–activity relationshipchemistryStereochemistryImidazoleMultiple linear regression analysisMolecular topologyCombinatorial chemistryGuanidine derivativesInternational Journal of Chemoinformatics and Chemical Engineering
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