0000000000361762

AUTHOR

Alfredo Alvarez Bello

showing 2 related works from this author

Ligand-based discovery of novel trypanosomicidal drug-like compounds: In silico identification and experimental support

2010

Abstract Two-dimensional bond-based linear indices and linear discriminant analysis are used in this report to perform a quantitative structure–activity relationship study to identify new trypanosomicidal compounds. A database with 143 anti-trypanosomal and 297 compounds having other clinical uses, are utilized to develop the theoretical models. The best discriminant models computed using bond-based linear indices provides accuracies greater than 90 for both training and test sets. Our models identify as anti-trypanosomals five out of nine compounds of a set of already-synthesized substances. The in vitro anti-trypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi…

Databases FactualMolecular modelCell SurvivalStereochemistryTrypanosoma cruziIn silicoNitro compoundQuantitative Structure-Activity RelationshipComputational biologyLigandsChemometricsDrug DiscoveryAnimalsHumansChagas DiseaseTrypanosoma cruziAmastigotePharmacologychemistry.chemical_classificationLife Cycle StagesbiologyOrganic ChemistryDiscriminant AnalysisBiological activityGeneral MedicineFibroblastsModels Theoreticalbiology.organism_classificationLinear discriminant analysisTrypanocidal AgentsHigh-Throughput Screening AssayschemistryAlgorithmsSoftwareEuropean Journal of Medicinal 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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