10,052 research outputs found

    Quantitative structure-activity relationship for antimalarial activity of artemisinin

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    The increase in resistance to older drugs and the emergence of new types of infection have created an urgent need for discovery and development of new compounds with antimalarial activity. Quantitative-Structure Activity Relationship (QSAR) methodology has been performed to develop models that correlate antimalarial activity of artemisinin analogs and their molecular structures. In this study, the data set consisted of 197 compounds with their activities expressed as log RA (relative activity). These compounds were randomly divided into training set (n=157) and test set (n=40). The initial stage of the study was the generation of a series of descriptors from three-dimensional representations of the compounds in the data set. Several types of descriptors which include topological, connectivity indices, geometrical, physical properties and charge descriptors have been generated. The number of descriptors was then reduced to a set of relevant descriptors by performing a systematic variable selection procedure which includes zero test, pairwise correlation analysis and genetic algorithm (GA). Several models were developed using different combinations of modelling techniques such as multiple linear regression (MLR) and partial least square (PLS) regression. Statistical significance of the final model was characterized by correlation coefficient, r2 and root-mean-square error calibration, RMSEC. The results obtained were comparable to those from previous study on the same data set with r2 values greater than 0.8. Both internal and external validations were carried out to verify that the models have good stability, robustness and predictive ability. The cross-validated regression coefficient (r2 cv) and prediction regression coefficient (r2 test) for the external test set were consistently greater than 0.7. The QSAR models developed in this study should facilitate the search for new compounds with antimalarial activity

    Pattern recognition system based on support vector machines: HIV-1 integrase inhibitors application

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    Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive Quantitative Structure-Activity Relationship (QSAR) models using molecular descriptors. The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: Support Vector Machines; Artificial Neural Network; Quantitative Structure-Activity Relationship

    Assessment of Potential Carcinogenicity by Quantitative Structure-Activity Relationship (QSAR)

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    Already in 1978, Elisabeth C. Miller and James A. Miller came with a presumption that electrophilic molecules are predicted to be carcinogens. It is because DNA molecule is reached in nucleophilic centres that may covalently bind to such substances. Rules deduced by Millers are even nowadays irrefutable, and they are used as the basis of testing of the substance for its carcinogenicity potential. Toxicological discipline that emerged from Millers’ research is based on dependence of chemical structure of the substance and their biological activity. Even further, there are strict regularities between molecular structures and activities. The tool used in assessment of biological activity of a substance is known as SAR, an abbreviation from structure–activity relationship. Besides electrophilic centres, in assessment of carcinogenic potential of a substance, the SAR also encounters chemical surrounding (neighbouring functional groups), size of the substance, its lipophilicity, number and position of aryl rings, substitutions of hydrogens, epoxides in aliphatic moieties or rings, resonance stabilisation, etc. To these days, SAR has been upgraded to quantitative SAR (QSAR) which applies multivariate statistical methods quantitatively comparing detected characteristics of “alerts” with biological activity of known carcinogens. Nowadays, chemical industry developing novel active substances is unthinkable without application of QSAR

    Application of support vector machines for prediction of anti-HIV activity of TIBO Derivatives.

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    The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: support vector machine (SVM); ANN; QSA

    Quantitative structure-activity relationship of some 1-benzylbenzimidazole derivatives as antifungal agents

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    In the present study, the antifungal activity of some 1-benzylbenzimidazole derivatives against yeast Saccharomyces cerevisiae was investigated. The tested benzimidazoles displayed in vitro antifungal activity and minimum inhibitory concentration (MIC) was determined for all the compounds. Quantitative structure-activity relationship (QSAR) has been used to study the relationships between the antifungal activity and lipophilicity parameter, logP, calculated by using CS Chem-Office Software version 7.0. The results are discussed on the basis of statistical data. The best QSAR model for prediction of antifungal activity of the investigated series of benzimidazoles was developed. High agreement between experimental and predicted inhibitory values was obtained. The results of this study indicate that the lipophilicity parameter has a significant effect on antifungal activity of this class of compounds, which simplify design of new biologically active molecules

    Quantitative structure-activity relationship to elucidate human CYP2A6 inhibition by organosulfur compounds

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    CYP2A6 is a human enzyme responsible for the metabolic elimination of nicotine, and it is also involved in the activation of procarcinogenic nitrosamines, especially those present in tobacco smoke. Several investigations have reported that reducing this enzyme activity may contribute to anti-smoking therapy as well as reducing the risk of promutagens in the body. For these reasons, several authors investigate selective inhibitors molecules toward this enzyme. The aim of this study was to evaluate the interactions between a set of organosulfur compounds and the CYP2A6 enzyme by a quantitative structure-activity relationship (QSAR) analysis. The present work provides a better understanding of the mechanisms involved, with the final goal of providing information for the future design of CYP2A6 inhibitors based on dietary compounds. The reported activity data were modeled by means of multiple regression analysis (MLR) and partial least-squares (PLS) techniques. The results indicate that hydrophobic and steric factors govern the union, while electronic factors are strongly involved in the case of monosulfides.Fil: Ramirez, Daniela Andrea. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - Mendoza. Instituto de BiologĂ­a AgrĂ­cola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de BiologĂ­a AgrĂ­cola de Mendoza; ArgentinaFil: Marchevsky, Eduardo Jorge. Universidad Nacional de San Luis. Facultad de QuĂ­mica, BioquĂ­mica y Farmacia; Argentina. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas; ArgentinaFil: Luco, Juan Maria. Universidad Nacional de San Luis. Facultad de QuĂ­mica, BioquĂ­mica y Farmacia; ArgentinaFil: Camargo, Alejandra Beatriz. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - Mendoza. Instituto de BiologĂ­a AgrĂ­cola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de BiologĂ­a AgrĂ­cola de Mendoza; Argentin

    Prediction of terpenoid toxicity based on a quantitative structure–activity relationship model

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    Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2 ) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.info:eu-repo/semantics/publishedVersio

    A quantitative structure-activity relationship (QSAR) study of chlorinated cyclodiene insecticide analogs

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    Quantitative structure-activity relationships (QSAR) between the inhibitory effect of specific t-butylbicyclophosphorothionate (TBPS) binding to rat brain P2 membrane, a lipophilic parameter, and topological indices, were studied for 33 chlorinated alicyclic insecticides such as heptachlor, aldrin and their structural analogs. This study shows that lipophilicity plays an important role in the action of cyclodiene compounds. The epoxide or ketone structural congeners, and the non-epoxide, non-ketone cyclodiene analogs exhibit two different QSARs and may bind to different regions, respectively, at the common GABA receptor. The epoxide or ketone congeners may bind at a slightly more hydrophilic region, and a negatively correlated linear relationship exists between the inhibition of TBPS-binding and lipophilicity. However, the non-epoxide, non-ketone analogs may bind at a very lipophilic region, and there is a positively correlated linear relationship between their binding and their lipophilicity. The epoxide feature of the cyclodienes seems to be an essential structural requirement for eliciting high inhibitory activity at the GABA receptor. Further the dependence of biological activity on structure can be described by a multiple-variate model with a combination of three explanatory variables among the first-, second-, third- and fourth-valence molecular connectivity indices, i.e., [superscript]1[chi][superscript] v, [superscript]2[chi][superscript] v, [superscript]3[chi][superscript] v, and [superscript]4[chi][superscript] v. High correlation coefficients (r = 0.934 to 0.941) between the biological response variable and the explanatory molecular connectivity indices demonstrated that the topological and steric attributes of the cyclodienes are structural characteristics important to for their biological activity. Electronic effects probably also contribute to the toxicity of the cyclodienes, but the parameter selected in the study, i.e., the bridge-carbon protons\u27 chemical shift in NMR spectra, does not reveal any relationship to the TBPS binding;The information drawn from such studies will benefit our understanding of the structural determinants for the biological action of the classic cyclodiene insecticides, and future approaches could be directed at the synthesis of modified cyclodiene-type insecticides, perhaps bearing fewer chlorines in the molecular framework. A better understanding of cyclodiene QSARs will also contribute to an improved capability to assess the toxicological significance of the ubiquitous environmental residues of the cyclodienes and their degradation products
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