Search results for "SIMULATION"

showing 10 items of 5095 documents

Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms.

2020

Abstract The purpose of this study was to determine the acute toxicity in aquatic organisms of one biocidal active substance and six metabolites derived from biocidal active substances and to assess the suitability of available QSAR models to predict the obtained values. We have reported the acute toxicity in sewage treatment plant (STP) microorganisms, in the freshwater microalgae Pseudokirchneriella subcapitata and in Daphnia magna following OECD test methods. We have also identified in silico models for acute toxicity of these trophic levels currently available in widely recognized platforms such as VEGA and the OECD QSAR ToolBox. A total of six, four and two models have been selected fo…

Quantitative structure–activity relationshipBiocideAquatic OrganismsHealth Toxicology and MutagenesisIn silicoMicroorganismDaphnia magna0211 other engineering and technologiesQuantitative Structure-Activity RelationshipFresh Water02 engineering and technology010501 environmental sciences01 natural sciencesDaphniaModels BiologicalChlorophyceaeMicroalgaeAnimalsComputer Simulation0105 earth and related environmental sciencesEC50021110 strategic defence & security studiesbiologyChemistryPublic Health Environmental and Occupational HealthGeneral Medicinebiology.organism_classificationPollutionAcute toxicityDaphniaEnvironmental chemistryWater Pollutants ChemicalDisinfectantsEcotoxicology and environmental safety
researchProduct

Use of Catalyst in a 3D-QSAR Study of the Interactions between Flavor Compounds and β-Lactoglobulin

2003

This paper reports a 3D-QSAR study using Catalyst software to explain the nature of interactions between flavor compounds and beta-lactoglobulin. A set of 35 compounds, for which dissociation constants were previously determined by affinity chromatography, was chosen. The set was divided into three subsets. An automated hypothesis generation, using HypoGen software, produced a model that made a valuable estimation of affinity and provided an explanation for the lack of correlation previously observed between the hydrophobicity of terpenes and the affinity for the protein. On the basis of these results, it appears that aroma binding to beta-lactoglobulin is caused by both hydrophobic interac…

Quantitative structure–activity relationshipChemical PhenomenaChemistry PhysicalTerpenesChemistryStereochemistryQuantitative Structure-Activity RelationshipHydrogen BondingLactoglobulinsGeneral ChemistryCatalysisDissociation constantModels ChemicalComputational chemistryOdorantsComputer SimulationDrug InteractionsGeneral Agricultural and Biological SciencesSoftwareFlavorJournal of Agricultural and Food Chemistry
researchProduct

Reliability of the capacity factor at zero micellar concentration and the solute-micelle association constant estimates by micellar liquid chromatogr…

1997

In micellar liquid chromatography, MLC, the hydrophobicity of a compound is the predominant effect on its retention and interaction with micelles. The capacity factors at zero micellar concentration, k(m), and the solute-micelle association constants, KAM- have recently been used as the hydrophobicity index of compounds and are important in QSAR studies. These parameters could be estimated (by regression) from the (k,[M]) data, where k is the capacity factor and [M] the surfactant concentration minus the critical micelle concentration. km and KAM are usually obtained from the intercept and slope, respectively, of the plot 1/k vs. [M]. In spite of the general use of this equation, the reliab…

Quantitative structure–activity relationshipChromatographyChemistrySurface PropertiesOrganic ChemistryOsmolar ConcentrationLinear modelAnalytical chemistryRegression analysisGeneral MedicineBiochemistryMicelleCapacity factorAnalytical ChemistryOsmolar ConcentrationModels ChemicalMicellar liquid chromatographyCritical micelle concentrationRegression AnalysisComputer SimulationDiureticsMicellesChromatography LiquidJournal of chromatography. A
researchProduct

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
researchProduct

Predicting antitrichomonal activity: A computational screening using atom-based bilinear indices and experimental proofs

2006

Existing Trichomonas vaginalis therapies are out of reach for most trichomoniasis people in developing countries and, where available, they are limited by their toxicity (mainly in pregnant women) and their cost. New antitrichomonal agents are needed to combat emerging metronidazole-resistant trichomoniasis and reduce the side effects associated with currently available drugs. Toward this end, atom-based bilinear indices, a new TOMOCOMD-CARDD molecular descriptor, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic atom-type bilinear in…

Quantitative structure–activity relationshipDatabases FactualMolecular modelStereochemistryClinical BiochemistryDrug Evaluation PreclinicalPharmaceutical ScienceAntitrichomonal AgentsLigandsBiochemistryCross-validationChemometricsStructure-Activity Relationshipchemistry.chemical_compoundArtificial IntelligencePredictive Value of TestsMolecular descriptorDrug DiscoveryTrichomonas vaginalisAnimalsCluster AnalysisComputer SimulationMolecular BiologyStochastic ProcessesOrganic ChemistryComputational BiologyReproducibility of ResultsLinear discriminant analysisAntitrichomonal agentchemistryData Interpretation StatisticalTopological indexLinear ModelsMolecular MedicineBiological systemAlgorithmsBioorganic & Medicinal Chemistry
researchProduct

Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays

2006

QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model Class=-96.067+1.988 x 10(2)X0Av +9 1.907 BIC3 + 6.853 CIC1 in the training set. External validation processes to assess the robustness and predictive pow…

Quantitative structure–activity relationshipDatabases FactualStereochemistryTyrosinaseQuantitative Structure-Activity RelationshipComputational biologyLigandsChemometricschemistry.chemical_compoundPiperidinesDrug DiscoveryComputer SimulationPharmacologyVirtual screeningbiologyChemistryOrganic ChemistryIn vitro toxicologyComputational BiologyDiscriminant AnalysisReproducibility of ResultsGeneral MedicineLinear discriminant analysisEnzyme inhibitorDrug Designbiology.proteinPeptidesKojic acidSoftwareEuropean Journal of Medicinal Chemistry
researchProduct

Predictive modeling of aryl hydrocarbon receptor (AhR) agonism

2020

Abstract The aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolic machinery and detoxification systems. In the recent years, this receptor has attracted interest as a therapeutic target for immunological, oncogenic and inflammatory conditions. In the present report, in silico and in vitro approaches were combined to study the activation of the AhR. To this end, a large database of chemical compounds with known AhR agonistic activity was employed to build 5 classifiers based on the Adaboost (AdB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms, respectively. The built classifier…

Quantitative structure–activity relationshipEnvironmental EngineeringSupport Vector MachineHealth Toxicology and MutagenesisIn silico0208 environmental biotechnologyContext (language use)02 engineering and technologyComputational biology010501 environmental sciences01 natural scienceschemistry.chemical_compoundPhenolsBasic Helix-Loop-Helix Transcription FactorsEnvironmental ChemistryAnimalsHumans[CHIM]Chemical SciencesComputer SimulationBenzothiazolesProspective StudiesReceptorComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesRegulation of gene expressionbiologyChemistryPublic Health Environmental and Occupational HealthRobustness (evolution)General MedicineGeneral ChemistryAryl hydrocarbon receptorPollution020801 environmental engineering3. Good healthBenzothiazoleReceptors Aryl Hydrocarbonbiology.proteinNeural Networks Computer[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]Algorithms[CHIM.CHEM]Chemical Sciences/Cheminformatics
researchProduct

A topological substructural approach for the prediction of P-glycoprotein substrates

2006

A topological substructural molecular design approach (TOPS-MODE) has been used to predict whether a given compound is a P-glycoprotein (P-gp) substrate or not. A linear discriminant model was developed to classify a data set of 163 compounds as substrates or nonsubstrates (91 substrates and 72 nonsubstrates). The final model fit the data with sensitivity of 82.42% and specificity of 79.17%, for a final accuracy of 80.98%. The model was validated through the use of an external validation set (40 compounds, 22 substrates and 18 nonsubstrates) with a 77.50% of prediction accuracy; fivefold full cross-validation (removing 40 compounds in each cycle, 80.50% of good prediction) and the predictio…

Quantitative structure–activity relationshipMolecular modelLinear modelQuantitative Structure-Activity RelationshipPharmaceutical ScienceLinear discriminant analysisTopologyModels BiologicalData setSet (abstract data type)Pharmaceutical PreparationsPredictive Value of TestsTest setLinear ModelsComputer SimulationATP Binding Cassette Transporter Subfamily B Member 1Sensitivity (control systems)FluoroquinolonesMathematicsJournal of Pharmaceutical Sciences
researchProduct

New tyrosinase inhibitors selected by atomic linear indices-based classification models.

2005

In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behavior is shown between the theoretical and experimental results. These results provide a tool that can be used i…

Quantitative structure–activity relationshipMolecular modelStereochemistryTyrosinaseClinical BiochemistryMolecular ConformationPharmaceutical ScienceQuantitative Structure-Activity RelationshipBiochemistrySensitivity and SpecificityChemometricsDrug DiscoveryComputer SimulationEnzyme InhibitorsMolecular BiologyTraining setChemistryMonophenol MonooxygenaseOrganic ChemistryLinear discriminant analysisTriterpenesDiscriminantModels ChemicalTopological indexMolecular MedicineBiological systemBioorganicmedicinal chemistry letters
researchProduct

Prospective computational design and in vitro bio-analytical tests of new chemical entities as potential selective CYP17A1 lyase inhibitors

2019

[EN] The development and advancement of prostate cancer (PCa) into stage 4, where it metastasize, is a major problem mostly in elder males. The growth of PCa cells is stirred up by androgens and androgen receptor (AR). Therefore, therapeutic strategies such as blocking androgens synthesis and inhibiting AR binding have been explored in recent years. However, recently approved drugs (or in clinical phase) failed in improving the expected survival rates for this metastatic-castration resistant prostate cancer (mCRPC) patients. The selective CYP17A1 inhibition of 17,20-lyase route has emerged as a novel strategy. Such inhibition blocks the production of androgens everywhere they are found in t…

Quantitative structure–activity relationshipStereochemistry01 natural sciencesBiochemistryStructure-Activity Relationship3D-QSAR pharmacophore modelDrug DiscoveryCytochrome P-450 Enzyme InhibitorsHumansStructure–activity relationshipCYP17A1 InhibitorMolecular BiologyDensity Functional TheoryVirtual screeningDose-Response Relationship DrugMolecular Structure010405 organic chemistryChemistryOrganic ChemistryProspective computational designSteroid 17-alpha-Hydroxylasecomputer.file_format1720-lyase selective inhibitionProtein Data BankLyase0104 chemical sciencesMolecular Docking Simulation010404 medicinal & biomolecular chemistryDocking (molecular)CYP17A1 inhibitorsMetastatic-castration resistant prostate cancerPharmacophorecomputer
researchProduct