Search results for "QSAR model"

showing 4 items of 4 documents

Chemometric and chemoinformatic analyses of anabolic and androgenic activities of testosterone and dihydrotestosterone analogues

2008

Predictive quantitative structure-activity relationship (QSAR) models of anabolic and androgenic activities for the testosterone and dihydrotestosterone steroid analogues were obtained by means of multiple linear regression using quantum and physicochemical molecular descriptors (MD) as well as a genetic algorithm for the selection of the best subset of variables. Quantitative models found for describing the anabolic (androgenic) activity are significant from a statistical point of view: R2 of 0.84 (0.72 and 0.70). A leave-one-out cross-validation procedure revealed that the regression models had a fairly good predictability [q2 of 0.80 (0.60 and 0.59)]. In addition, other QSAR models were …

MaleQuantitative structure–activity relationshipAnabolismStereochemistrymedicine.medical_treatmentClinical BiochemistryAnabolic and androgenic activitiesQSAR modelQuantitative Structure-Activity RelationshipPharmaceutical ScienceBiochemistrySteroidAnabolic AgentsMolecular descriptorDrug DiscoveryLinear regressionmedicineCluster AnalysisHumansComputer SimulationTestosteroneMolecular BiologyChemistryOrganic ChemistryDihydrotestosteroneModels ChemicalGenetic algorithmDihydrotestosteroneAndrogensQuantum and physicochemical molecular descriptorMolecular MedicineTestosterone and dihydrotestosterone steroid analoguesAlgorithmsAnabolic steroidApplicability domainmedicine.drugBioorganic and Medicinal Chemistry 16: 6448-6459 (2008)
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Dry selection and wet evaluation for the rational discovery of new anthelmintics

2017

Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a TOMOCOMD-CARDD (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmin…

0301 basic medicineBiophysicsNon-stochastic and stochastic atom-based bilinear indicesBilinear interpolationLDA-based QSAR modelQuBiLs-MAS module01 natural sciencesSet (abstract data type)03 medical and health sciencesMolecular descriptorStatisticsPhysical and Theoretical ChemistryMolecular BiologySelection (genetic algorithm)MathematicsFree and open source softwareTraining setTOMOCOMD-CARDD softwareExternal validationAnthelmintic activityAtom (order theory)Computational creeningCondensed Matter PhysicsLinear discriminant analysis0104 chemical sciencesIndazole010404 medicinal & biomolecular chemistry030104 developmental biologyLead generationMolecular Physics
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QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents

2015

The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correc…

AntifungalQuantitative structure–activity relationshipAntifungal AgentsLinear discriminant analysismedicine.drug_classIn silicoAtom-based quadratic indicesQSAR modelQuantitative Structure-Activity RelationshipBioengineeringDrug developmentComputational biologyQuantitative structure activity relationVrtual screening antifungal agentDrug DiscoverymedicineComputer SimulationDrug identificationChemistryDrug discoveryLinear modelDiscriminant AnalysisGeneral MedicineLinear discriminant analysisCombinatorial chemistryChemistryTest setLinear ModelsMolecular MedicineQuBiLs-MAS softwareStatistical modelAntifungal agent
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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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