Search results for "virtual screening"

showing 10 items of 102 documents

Discovery of γ-secretase modulators with a novel activity profile by text-based virtual screening.

2012

We present an integrated approach to identify and optimize a novel class of γ-secretase modulators (GSMs) with a unique pharmacological profile. Our strategy included (i) virtual screening through application of a recently developed protocol (PhAST), (ii) synthetic chemistry to discover structure–activity relationships, and (iii) detailed in vitro pharmacological characterization. GSMs are promising agents for treatment or prevention of Alzheimer’s disease. They modulate the γ-secretase product spectrum (i.e., amyloid-β (Aβ) peptides of different length) and induce a shift from toxic Aβ42 to shorter Aβ species such as Aβ38 with no or minimal effect on the overall rate of γ-secretase cleavag…

PyridinesPyridonesMolecular Sequence DataPeptideComputational biologyCHO CellsBiochemistryStructure-Activity RelationshipAlzheimer DiseaseCricetinaeAnimalsHumansγ secretaseAmino Acid Sequencechemistry.chemical_classificationVirtual screeningActivity profileAmyloid beta-PeptidesChemistryGeneral MedicineIntegrated approachIn vitroMinimal effectDrug DesignMolecular MedicineAmyloid Precursor Protein SecretasesACS chemical biology
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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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Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays

2006

QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model Class=-96.067+1.988 x 10(2)X0Av +9 1.907 BIC3 + 6.853 CIC1 in the training set. External validation processes to assess the robustness and predictive pow…

Quantitative structure–activity relationshipDatabases FactualStereochemistryTyrosinaseQuantitative Structure-Activity RelationshipComputational biologyLigandsChemometricschemistry.chemical_compoundPiperidinesDrug DiscoveryComputer SimulationPharmacologyVirtual screeningbiologyChemistryOrganic ChemistryIn vitro toxicologyComputational BiologyDiscriminant AnalysisReproducibility of ResultsGeneral MedicineLinear discriminant analysisEnzyme inhibitorDrug Designbiology.proteinPeptidesKojic acidSoftwareEuropean Journal of Medicinal Chemistry
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Prospective computational design and in vitro bio-analytical tests of new chemical entities as potential selective CYP17A1 lyase inhibitors

2019

[EN] The development and advancement of prostate cancer (PCa) into stage 4, where it metastasize, is a major problem mostly in elder males. The growth of PCa cells is stirred up by androgens and androgen receptor (AR). Therefore, therapeutic strategies such as blocking androgens synthesis and inhibiting AR binding have been explored in recent years. However, recently approved drugs (or in clinical phase) failed in improving the expected survival rates for this metastatic-castration resistant prostate cancer (mCRPC) patients. The selective CYP17A1 inhibition of 17,20-lyase route has emerged as a novel strategy. Such inhibition blocks the production of androgens everywhere they are found in t…

Quantitative structure–activity relationshipStereochemistry01 natural sciencesBiochemistryStructure-Activity Relationship3D-QSAR pharmacophore modelDrug DiscoveryCytochrome P-450 Enzyme InhibitorsHumansStructure–activity relationshipCYP17A1 InhibitorMolecular BiologyDensity Functional TheoryVirtual screeningDose-Response Relationship DrugMolecular Structure010405 organic chemistryChemistryOrganic ChemistryProspective computational designSteroid 17-alpha-Hydroxylasecomputer.file_format1720-lyase selective inhibitionProtein Data BankLyase0104 chemical sciencesMolecular Docking Simulation010404 medicinal & biomolecular chemistryDocking (molecular)CYP17A1 inhibitorsMetastatic-castration resistant prostate cancerPharmacophorecomputer
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Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

2014

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of …

Quantitative structure–activity relationshipStereochemistryTrypanosoma cruziDrug Evaluation PreclinicalQuantitative Structure-Activity RelationshipBilinear interpolationSet (abstract data type)MiceDrug DiscoveryIc50 valuesmedicineAnimalsCells CulturedPharmacologyStochastic ProcessesVirtual screeningDose-Response Relationship DrugMolecular StructureChemistryMacrophagesOrganic ChemistryDiscriminant AnalysisGeneral MedicineLinear discriminant analysisTrypanocidal AgentsDiscriminantBenznidazoleBiological systemmedicine.drugEuropean Journal of Medicinal Chemistry
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Vanilloid Derivatives as Tyrosinase Inhibitors Driven by Virtual Screening-Based QSAR Models

2010

A number of vanilloids have been tested as tyrosinase inhibitors using Ligand-Based Virtual Screening (LBVS) driven by QSAR (Quantitative Structure-Activity Relationship) models as the multi-agent classification system. A total of 81 models were used to screen this family. Then, a preliminary cluster analysis of the selected chemicals was carried out based on their bioactivity to detect possible similar substructural features among these compounds and the active database used in the QSAR model construction. The compounds identified were tested in vitro to corroborate the results obtained in silico. Among them, two chemicals, isovanillin (K(M) (app) = 1.08 mM) near to kojic acid (reference d…

Quantitative structure–activity relationshipStereochemistryTyrosinaseIn silicoQuantitative Structure-Activity RelationshipPharmaceutical ScienceIsovanillinModels BiologicalSkin DiseasesVanilloidsAnalytical Chemistrychemistry.chemical_compoundCluster AnalysisHumansEnvironmental ChemistryComputer SimulationEnzyme InhibitorsSpectroscopyVirtual screeningMonophenol MonooxygenaseReference drugCombinatorial chemistrychemistryBenzaldehydesDrug DesignKojic acidAlgorithmsDrug Testing and Analysis
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Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands

2015

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we…

Quantitative structure–activity relationshipTelomeraseGeneral Chemical EngineeringDrug Evaluation PreclinicalQuantitative Structure-Activity RelationshipComputational biologyLibrary and Information SciencesBiologyG-quadruplexCrystallography X-RayLigandsMolecular Docking Simulationchemistry.chemical_compoundDrug DiscoveryHumansCell ProliferationGeneticsVirtual screeningMolecular StructureDrug discoveryQSARGeneral ChemistryFibroblastsTelomereComputer Science ApplicationsTelomereG-QuadruplexesMolecular Docking SimulationchemistryAcridinesDNAHeLa Cells
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Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening.

2001

The generation of diversity and its further selection by an external system is a common mechanism for the evolution of the living species and for the current drug design methods. This assumption allows us to label the methods based on generation and selection of molecular diversity as "Darwinian" ones, and to distinguish them from the structure-based, structure-modulation approaches. An example of a Darwinian method is the inverse QSAR. It consists of the computational generation of candidate chemical structures and their selection according to a previously established QSAR model. New trends in the field of combinatorial chemical syntheses comprise the concepts of virtual combinatorial synt…

Quantitative structure–activity relationshipVirtual screeningCombinatorial Chemistry TechniquesChemistryOrganic ChemistryQuantitative Structure-Activity RelationshipGeneral MedicineInverse problemCombinatorial chemistryBiological EvolutionField (computer science)Computer Science ApplicationsDrug DesignDrug DiscoveryGraph (abstract data type)Combinatorial Chemistry TechniquesComputer SimulationDesign methodsSelection (genetic algorithm)Combinatorial chemistryhigh throughput screening
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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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Modeling Natural Anti-Inflammatory Compounds by Molecular Topology

2011

One of the main pharmacological problems today in the treatment of chronic inflammation diseases consists of the fact that anti-inflammatory drugs usually exhibit side effects. The natural products offer a great hope in the identification of bioactive lead compounds and their development into drugs for treating inflammatory diseases. Computer-aided drug design has proved to be a very useful tool for discovering new drugs and, specifically, Molecular Topology has become a good technique for such a goal. A topological-mathematical model, obtained by linear discriminant analysis, has been developed for the search of new anti-inflammatory natural compounds. An external validation obtained with …

Quantitative structure–activity relationshiplinear discriminant analysismedicine.drug_classAnti-Inflammatory AgentsQuantitative Structure-Activity RelationshipComputational biologyCatalysisAnti-inflammatoryNatural (archaeology)ArticleModel validationInorganic Chemistrylcsh:ChemistrymedicinePhysical and Theoretical ChemistryMolecular Biologylcsh:QH301-705.5Spectroscopynaturalanti-inflammatoryVirtual screeningBiological ProductsChemistryOrganic ChemistryExternal validationGeneral MedicineMolecular Topologyvirtual screeningCombinatorial chemistryComputer Science Applicationslcsh:Biology (General)lcsh:QD1-999Models ChemicalMolecular Topology; virtual screening; natural; anti-inflammatory; linear discriminant analysisIdentification (biology)Molecular topologyInternational Journal of Molecular Sciences
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