Search results for "Molecular Descriptor"

showing 10 items of 54 documents

Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

2019

Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the the…

Models MolecularChemical compoundComputer scienceAntiprotozoal AgentsDrug Evaluation PreclinicalMachine learningcomputer.software_genre01 natural sciencesMachine Learningchemistry.chemical_compoundParasitic Sensitivity TestsMolecular descriptorDrug DiscoveryLeishmaniaComputational modelLeishmania amazonensisVirtual screeningbiologyArtificial neural networkbusiness.industryGeneral Medicinebiology.organism_classification0104 chemical sciencesSupport vector machine010404 medicinal & biomolecular chemistryIdentification (information)chemistryArtificial intelligencebusinesscomputerSoftwareCurrent topics in medicinal chemistry
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Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

2007

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further supp…

Models MolecularMultivariate statisticsMultivariate analysisAnti-HIV AgentsCombined useHuman immunodeficiency virus (HIV)Computational biologyDrug resistanceBiologyLigandsBioinformaticsmedicine.disease_causeHIV ProteaseMolecular descriptorDrug Resistance ViralDrug DiscoverymedicineHumansDOCKINGPhysical and Theoretical ChemistryBinding SitesHIV Protease InhibitorsSettore CHIM/08 - Chimica FarmaceuticaHIV Reverse TranscriptaseComputer Science ApplicationsDRUG RESISTANCEDocking (molecular)Drug DesignMultivariate AnalysisMutationHIV-1Computer-Aided DesignReverse Transcriptase InhibitorsMultivariate statisticalJournal of Computer-Aided Molecular Design
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TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-bas…

2006

Abstract A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure–activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six us…

Models MolecularQuantitative structure–activity relationshipMolecular modelStereochemistryTyrosinaseClinical BiochemistryPharmaceutical ScienceQuantitative Structure-Activity RelationshipBiochemistryModels BiologicalChemometricsMolecular descriptorDrug DiscoveryComputer SimulationMolecular BiologyVirtual screeningMolecular StructureChemistryMonophenol MonooxygenaseOrganic ChemistryDiscriminant AnalysisLinear discriminant analysisModels ChemicalTopological indexMolecular MedicineBiological systemAgaricalesPeptidesAlgorithmsBioorganicmedicinal chemistry
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On the contribution of molecular topology to drug design and discovery.

2010

Abstract The role of molecular topology (MT) in the design and selection of new drugs is discussed. After an overview of the different in silico molecular design current technologies, the QSAR analysis is dealt in detail with particular emphasis in the use of topological indices as molecular descriptors. The results of the application of MT in drug design and discovery are described and finally a possible explanation is given about some of the key reasons explaining it's the extraordinary performance.

Models MolecularQuantitative structure–activity relationshipTheoretical computer scienceComputer scienceIn silicoQuantitative Structure-Activity RelationshipGeneral MedicinePharmaceutical PreparationsMolecular descriptorDrug DesignDrug DiscoveryMolecular MedicineAnimalsComputer-Aided DesignHumansComputer SimulationMolecular topologyCurrent computer-aided drug design
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Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

2006

Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure …

Models MolecularQuantitative structure–activity relationshipbusiness.industryComputer scienceOrganic ChemistryQuantitative Structure-Activity RelationshipQuantitative structureFeature selectionGeneral MedicineMachine learningcomputer.software_genreCombinatorial chemistryField (computer science)Computer Science ApplicationsDomain (software engineering)Molecular descriptorDrug DiscoveryArtificial intelligencebusinesscomputerApplicability domainCombinatorial Chemistry & High Throughput Screening
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Modelling the enantioresolution capability of cellulose tris(3,5-dichlorophenylcarbamate) stationary phase in reversed phase conditions for neutral a…

2018

[EN] To the best of our knowledge, the prediction of the enantioresolution ability of polysaccharides-based stationary phases in liquid chromatography for structurally unrelated compounds has not been previously reported. In this study, structural information of neutral and basic compounds is used to model their enantioresolution levels obtained from an immobilised cellulose tris(3,5-dichlorophenylcarbamate) stationary phase in reversed phase conditions. Thirty-four structurally unrelated chiral drugs and pesticides, from seven families, are studied. Categorical enantioresolution levels (RsC, 0 = no baseline enantioresolution and 1 = baseline enantioresolution) are established from the expe…

Models MolecularTrisPhenylcarbamatesEnantioresolution modelling01 natural sciencesBiochemistryAnalytical Chemistrychemistry.chemical_compoundMolecular descriptorPhase (matter)Tris(35-dichlorophenylcarbamate)MoleculeLeast-Squares AnalysisPesticidesCelluloseCelluloseChromatography High Pressure LiquidReversed phase liquid chromatographyEnantioseparationsChromatography Reverse-PhasePrincipal Component AnalysisChromatography010405 organic chemistry010401 analytical chemistryOrganic ChemistryDiscriminant partial least squaresDiscriminant AnalysisStereoisomerismGeneral MedicineReversed-phase chromatography0104 chemical scienceschemistryStationary phaseAsymmetric carbonStationary phaseJournal of Chromatography A
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Density functional theory fragment descriptors to quantify the reactivity of a molecular family: Application to amino acids

2007

By using the exact density functional theory, one demonstrates that the value of the local electronic softness of a molecular fragment is directly related to the polarization charge (Coulomb hole) induced by a test electron removed (or added) from (at) the fragment. Our finding generalizes to a chemical group a formal relation between these molecular descriptors recently obtained for an atom in a molecule using an approximate atomistic model [P. Senet and M. Yang, J. Chem. Sci. 117, 411 (2005)]. In addition, a practical ab initio computational scheme of the Coulomb hole and related local descriptors of reactivity of a molecular family having in common a similar fragment is presented. As a b…

Models Molecularchemistry.chemical_classificationQuantitative Biology::BiomoleculesQuantitative structure–activity relationshipBinding SitesChemistryAb initioGeneral Physics and AstronomyAmino acidModels ChemicalAb initio quantum chemistry methodsComputational chemistryMolecular descriptorMoleculeComputer SimulationDensity functional theoryAmino AcidsPhysical and Theoretical ChemistryAlgorithmsFragment molecular orbitalProtein BindingThe Journal of Chemical Physics
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Study and identification of new molecular descriptors, finalized to the development of Virtual Screening techniques through the use of deep neural ne…

2022

Molecular DescriptorDeep LearningVirtual ScreeningDrug DesignDrug DiscoveryNMREmbeddingBioactivity Prediction
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A Multivariate Analysis on Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors and Resistance Induced by Mutation

2003

This paper describes the use of multivariate statistical procedure PCA as a tool to explore the inhibitory activity of classes of NNRTIs against HIV-1 viruses (wild type and more frequent mutants, Y181C, V106A, K103N, L100I) and against RT enzyme. The analysis of correlations between biological activity and molecular descriptors or similarity indexes allowed a reliable classification of the fifty five derivatives considered in this study. The best results were obtained in the case of L100I and K103N mutants for which the higher number of assignments was found when the principal components derived from the descriptors were used. On this basis this statistical approach is proposed as a reliab…

Multivariate analysisOrganic ChemistryMutantWild typevirus diseasesBiological activityComputational biologyBiologyBioinformaticsSettore CHIM/08 - Chimica FarmaceuticaReverse transcriptaseComputer Science ApplicationsMolecular descriptorDrug DiscoveryMutation (genetic algorithm)Principal component analysisNNRTIs PCA DA resistance mutation
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Prediction of tyrosinase inhibition activity using atom-based bilinear indices.

2007

A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, M(k) and S(k), respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using M(k) and S(k) as matrix operators of bilinear transformations. Chemical information is coded by using different pair com…

PharmacologyMelaninsQuantitative structure–activity relationshipStochastic ProcessesSeries (mathematics)Molecular StructureChemistryMonophenol MonooxygenaseOrganic ChemistryBilinear interpolationLinear discriminant analysisBiochemistryStructure-Activity RelationshipDiscriminantModels ChemicalComputational chemistryMolecular descriptorDrug DiscoveryLinear algebraMolecular MedicineComputer SimulationGeneral Pharmacology Toxicology and PharmaceuticsBilinear mapEnzyme InhibitorsBiological systemChemMedChem
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