0000000000524880

AUTHOR

Vladimir V. Kouznetsov

showing 4 related works from this author

New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors.

2008

Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental resu…

DrugAdultQuantitative structure–activity relationshipStereochemistrymedia_common.quotation_subjectOvariectomyDrug Evaluation PreclinicalTrichomonas InfectionsAntitrichomonal AgentsBiochemistryAnalytical Chemistrychemistry.chemical_compoundIn vivoMolecular descriptorDrug Resistance BacterialTrichomonas vaginalisAnimalsHumansRats Wistarmedia_commonChromatographyMolecular StructureChemistryDiscriminant AnalysisLinear discriminant analysisRatsAntitrichomonal agentEdge basedMolecular MedicineComputer-Aided DesignFemaleSoftwareBiotechnologyJournal of biomolecular screening
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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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Identification In Silico and In Vitro of Novel Trypanosomicidal Drug-Like Compounds

2012

Atom-based bilinear indices and linear discriminant analysis are used to discover novel trypanosomicidal compounds. The obtained linear discriminant analysis-based quantitative structure–activity relationship models, using non-stochastic and stochastic indices, provide accuracies of 89.02% (85.11%) and 89.60% (88.30%) of the chemicals in the training (test) sets, respectively. Later, both models were applied to the virtual screening of 18 in-house synthesized compounds to find new pro-lead antitrypanosomal agents. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Predictions agree with experimental results to a great extent (16/18…

PharmacologyDrugVirtual screeningbiologyStereochemistryIn silicomedia_common.quotation_subjectOrganic ChemistryLinear discriminant analysisbiology.organism_classificationBiochemistryIn vitroDrug DiscoverymedicineMolecular MedicineNifurtimoxTrypanosoma cruzimedicine.drugmedia_commonChemical Biology & Drug Design
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Identification <i>In Silico</i> and <i>In Vitro</i> of Novel Trypanosomicidal Drug-like Compounds

2012

Atom-based bilinear indices and linear discriminant analysis are used to discover novel trypanosomicidal compounds. The obtained linear discriminant analysis-based quantitative structure–activity relationship models, using non-stochastic and stochastic indices, provide accuracies of 89.02% (85.11%) and 89.60% (88.30%) of the chemicals in the training (test) sets, respectively. Later, both models were applied to the virtual screening of 18 in-house synthesized compounds to find new pro-lead antitrypanosomal agents. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Predictions agree with experimental results to a great extent (16/18…

DrugVirtual screeningChromatographybiologyChemistrymedia_common.quotation_subjectIn silicobiology.organism_classificationLinear discriminant analysisIn vitromedicineTrypanosoma cruziNifurtimoxmedia_commonmedicine.drugProceedings of The 16th International Electronic Conference on Synthetic Organic Chemistry
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