Search results for "relationship"

showing 10 items of 3616 documents

Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity.

2005

The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classificat…

Quantitative structure–activity relationshipStochastic ProcessesMolecular modelDatabases FactualChemistryOrganic ChemistryClinical BiochemistryMolecular ConformationPharmaceutical ScienceAtom (order theory)Quantitative Structure-Activity RelationshipModels TheoreticalLinear discriminant analysisBiochemistryAnti-Bacterial AgentsSet (abstract data type)Quadratic equationSimple (abstract algebra)Drug DiscoveryMolecular MedicineComputer SimulationBiological systemMolecular BiologyAntibacterial agentBioorganicmedicinal chemistry
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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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Applications of Bond-Based 3D-Chiral Quadratic Indices in QSAR Studies Related to Central Chirality Codification

2009

The concept of bond-based quadratic indices is generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design, we have modeled several well-known data sets. In particularly, Cramer's steroid data set has become a benchmark for the assessment of novel QSAR methods. This data set has been used by several researchers using 3D-QSAR approaches. Therefore, it is selected by us for the shake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design, we model the angiotensin-converting enzyme inhibitory activity o…

Quantitative structure–activity relationshipTheoretical computer scienceComputer scienceChemistryOrganic ChemistryComparabilityComputer Science ApplicationsData setSet (abstract data type)Quadratic equationComputational chemistryDrug DiscoveryMolecular symmetryBenchmark (computing)TrigonometryQSAR & Combinatorial Science
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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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2D- and 3D-QSAR Models of Interaction between Flavor Compounds and beta-Lactoglobulin Using Catalyst and Cerius2

2004

The present paper describes an application of Catalyst to three aroma sets (35, 24 and 21 compounds respectively) to generate activities-based alignments, using the best significant generated hypotheses. The obtained Catalyst models confirmed the existence of at least two binding sites on the BLG.

Quantitative structure–activity relationshipbiologyChemistryOrganic ChemistryDrug Discoverybiology.proteinOrganic chemistrybiology.organism_classificationBeta-lactoglobulinAromaFlavorComputer Science ApplicationsCatalysisQSAR & Combinatorial Science
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Prediction of potential environmental toxicity of chemicals in <em>Lactuca sativa</em> seed germination using computational tools

2019

The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of phytotoxicity effects of chemical compounds on the Lactuca sativa seeds germination. A database of 73 compounds, assayed against L. sativa and Dragon’s molecular descriptors are used to obtain a QSAR model for the prediction of the phytotoxicity. The model is carried out with QSARINS software and validated according to OECD principles. The best model showed good value for the determination coefficient (R2 = 0.917) and others parameters appropriate for fitting (s = 0.256 and RMSEtr= 0.236). The validation results confirmed that the model has good robustness and stability …

Quantitative structure–activity relationshipbiologyGerminationMolecular descriptorEnvironmental toxicologyPhytotoxicityLactucaBiological systembiology.organism_classificationMathematicsProceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition
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<strong>Predicting Proteasome Inhibition using Atomic Weighted Vector and Machine Learning</strong>

2018

Ubiquitin/Proteasome System (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. Through the concerted actions of a series of enzymes, proteins are marked for proteasomal degradation by being linked to the polypeptide co-factor, ubiquitin. The UPS participates in a wide array of biological functions such as antigen presentation, regulation of gene transcription and the cell cycle, and activation of NF-κB. Some researchers have applied QSAR method and machine learning in the study of proteasome inhibition (EC50(µmol/L)), such as: the analysis of proteasome inhibition prediction, in the prediction of multi-target inhibitors of UPP and in the prediction of p…

Quantitative structure–activity relationshipbusiness.industryProtein contact mapPerceptronMachine learningcomputer.software_genreCross-validationRandom forestStatistical classificationMolecular descriptorLinear regressionArtificial intelligencebusinesscomputerMathematicsProceedings of MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition
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QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

2001

The molecular topology model and discriminant analysis have been applied to the prediction of some pharmacological properties of hypoglycemic drugs using multiple regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies performed on the selected prediction models confirmed the goodness of the fits. The method used for hypoglycemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and selection of new hypoglycemic agents, and we …

Quantitative structure–activity relationshipbusiness.industryStatistical parameterRegression analysisPattern recognitionGeneral ChemistryMachine learningcomputer.software_genreLinear discriminant analysisStability (probability)Computer Science ApplicationsComputational Theory and MathematicsLinear regressionArtificial intelligencebusinesscomputerPredictive modellingSelection (genetic algorithm)Information SystemsMathematics
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Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

2015

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools.…

Quantitative structure–activity relationshipcytochrome P450In silicoMetabolitexenobioticReviewBiologyPharmacologyXenobiotics03 medical and health scienceschemistry.chemical_compound0302 clinical medicineCYP P450sToxicokineticsPharmacology (medical)aineenvaihdunta030304 developmental biologyADMEPharmacology0303 health sciencesIn silico modelingQSARlcsh:RM1-950Cytochrome P450docking studiesmodelingLigand (biochemistry)3. Good healthbiotransformationslcsh:Therapeutics. PharmacologychemistryBiochemistryin silico030220 oncology & carcinogenesisbiology.proteinXenobioticmetabolismFrontiers in Pharmacology
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