0000000000215086

AUTHOR

A.r. Martínez-fernández

showing 3 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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Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental as…

2005

In order to explore the ability of non-stochastic quadratic indices to encode chemical information in antimalarials, four quantitative models for the discrimination of compounds having this property were generated and statistically compared. Accuracies of 90.2% and 83.3% for the training and test sets, respectively, were observed for the best of all the models, which included non-stochastic quadratic fingerprints weighted with Pauling electronegativities. With a comparative purpose and as a second validation experiment, an exercise of virtual screening of 65 already-reported antimalarials was carried out. Finally, 17 new compounds were classified as either active/inactive ones and experimen…

Models MolecularQuantitative structure–activity relationshipStereochemistryDrug Evaluation PreclinicalMolecular ConformationQuantitative Structure-Activity RelationshipMolecular conformationChemometricsAntimalarialsQuadratic equationHeterocyclic CompoundsDrug DiscoveryComputer SimulationPharmacologyVirtual screeningChemistryComputer aidOrganic ChemistryReproducibility of ResultsChloroquineGeneral MedicineLinear discriminant analysisDrug DesignTopological indexHeminCrystallizationBiological systemAlgorithmsEuropean Journal of Medicinal Chemistry
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Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones

2014

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In …

Quantitative structure–activity relationshipClinical BiochemistryAntiprotozoal AgentsQuantitative Structure-Activity RelationshipPharmaceutical ScienceLinear classifierBioinformaticsMachine learningcomputer.software_genreBiochemistryQuinoxalinesMolecular descriptorDrug DiscoveryBioassayMolecular BiologyVirtual screeningMolecular Structurebusiness.industryChemistryOrganic ChemistryBenchmark databaseDrug developmentCyclizationMolecular MedicineIn silico StudyArtificial intelligenceTOMOCOMD-CARDD SoftwarebusinessClassifier (UML)computer
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