0000000000215087

AUTHOR

Ysaias Alvarado

0000-0002-2709-409x

showing 5 related works from this author

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
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Nucleotide's bilinear indices: Novel bio-macromolecular descriptors for bioinformatics studies of nucleic acids. I. Prediction of paromomycin's affin…

2009

A new set of nucleotide-based bio-macromolecular descriptors are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to nucleic acids quantitative structure-activity relationship (QSAR) studies. These bio-macromolecular indices are based on the calculus of bilinear maps on Re(n)[b(mk)(x (m),y (m)):Re(n) x Re(n)--Re] in canonical basis. Nucleic acid's bilinear indices are calculated from kth power of non-stochastic and stochastic nucleotide's graph-theoretic electronic-contact matrices, M(m)(k) and (s)M(m)(k), respectively. That is to say, the kth non-stochastic and stochastic nucleic acid's bilinear indices are calculated using M(m)(k)…

Models MolecularStatistics and ProbabilityPure mathematicsQuantitative structure–activity relationshipParomomycinMolecular Sequence DataDNA FootprintingQuantitative Structure-Activity RelationshipBilinear interpolationGeneral Biochemistry Genetics and Molecular BiologyInterpretation (model theory)DNA PackagingLinear regressionOrder (group theory)MathematicsStochastic ProcessesBase SequenceGeneral Immunology and MicrobiologyApplied MathematicsComputational BiologyGeneral MedicineModeling and SimulationDNA ViralLinear algebraStandard basisHIV-1Nucleic acidRNA ViralGeneral Agricultural and Biological SciencesAlgorithmJournal of Theoretical Biology
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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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Atom-Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results ofIn Silico Studies Supported by Experimental Results

2007

Herein we present results of QSAR studies of tyrosinase inhibitors employing one of the atom-based TOMOCOMD-CARDD (acronym of TOpological MOlecular COMputer Design-Computer Aided “Rational” Drug Design) descriptors, molecular quadratic indices, and Linear Discriminant Analysis (LDA) as pattern recognition method. In this way, a database of 246 organic chemicals, reported as tyrosinase inhibitors having great structural variability, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. In total, 12 LDA-based QSAR models were obtained, the first six with the non-stochastic total and local quadratic indices and the six rema…

Quantitative structure–activity relationshipVirtual screeningDrug discoveryChemistryIn silicoTyrosinaseOrganic ChemistryComputational biologyMatthews correlation coefficientLinear discriminant analysisCombinatorial chemistryComputer Science ApplicationsMolecular descriptorDrug DiscoveryQSAR & Combinatorial Science
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Novel 3D bio-macromolecular bilinear descriptors for protein science: Predicting protein structural classes

2015

In the present study, we introduce novel 3D protein descriptors based on the bilinear algebraic form in the ℝn space on the coulombic matrix. For the calculation of these descriptors, macromolecular vectors belonging to ℝn space, whose components represent certain amino acid side-chain properties, were used as weighting schemes. Generalization approaches for the calculation of inter-amino acidic residue spatial distances based on Minkowski metrics are proposed. The simple- and double-stochastic schemes were defined as approaches to normalize the coulombic matrix. The local-fragment indices for both amino acid-types and amino acid-groups are presented in order to permit characterizing fragme…

Models MolecularProtein structural classesMathematical parametersProtein functionQuantitative Structure-Activity RelationshipBilinear interpolationQuantitative structure activity relation3D protein descriptorBilinear formProceduresChemical structureStatistical parametersMinkowski spaceProtein analysisAmino AcidsPriority journalMathematicsInterpretabilityQuantitative Biology::BiomoleculesApplied MathematicsStatistical parameterValidation studyGeneral MedicineComputer simulationDiscriminant analysisReproducibilityAmino acidAlgorithmChemistryProtein conformationModeling and SimulationStatistical modelGeneral Agricultural and Biological SciencesBiological systemAmino acid analysisAlgorithmsNonbiological modelStatistics and ProbabilityCorrelation coefficientLDAMacromolecular SubstancesMarkov chainMacromoleculeStructure analysisModels BiologicalArticleGeneral Biochemistry Genetics and Molecular BiologyCombinatoricsStochastic processesBilinear formBiologyMatrixGeneral Immunology and MicrobiologyProteinCoulombic matrixComputational BiologyProteinsReproducibility of ResultsLinear discriminant analysisWeightingCorrelation coefficientProtein structureBiological modelLinear ModelsThree-dimensional modelingJournal of Theoretical Biology
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