<i>QSPR</i> study of the retention/release property of odorant molecules in pectin gels using statistical methods

Belhassan, Assia; Chtita, Samir; Lakhlifi, Tahar; Bouachrine, Mohammed

doi:10.1016/j.jtusci.2017.05.004

Cited by 11 publications

(5 citation statements)

References 14 publications

(10 reference statements)

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“…Molecular descriptors were generated using Gaussian 09W, ACD/ChemSketch and ChemOffice programs [30,31], to predict the correlation between these parameters and their odors thresholds property by developing a linear model (multiple linear regression (MLR)) [32]. 35 descriptors are selected to calculate different types of molecular properties that can govern this property, assuming that a change of the molecular structure leads modifying in the odors thresholds property of pyrazine derivatives (Table 2).…”

Section: -3-molecular Descriptors Generationmentioning

confidence: 99%

2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

Belhassan

Chtita²,

Lakhlifi³

et al. 2019

Emerg Sci J

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In this study, we have established two-dimensional quantitative structure propriety relationships (2D-QSPR) model, for a group of 78 molecules based on pyrazine, these molecules were subjected to a 2D-QSPR analyze for their odors thresholds propriety using stepwise Multiple Linear Regression (MLR). The 35 parameters are calculated for the 78 studied compounds using the Gaussian 09W, ChemOffice and ChemSketch softwares. Quantum chemical calculations are used to calculate electronic and quantum chemical descriptors, using the density functional theory (B3LYP/6-31G (d) DFT) methods.The model was used to predict the odors thresholds propriety of the test and training set compounds, and the statistical results exhibited high internal and external consistency as demonstrated by the validation methods.

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Section: -3-molecular Descriptors Generationmentioning

confidence: 99%

2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

Belhassan

Chtita²,

Lakhlifi³

et al. 2019

Emerg Sci J

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“…The correlation between the predicted and observed log(LRI) is shown in Figure 1. The descriptors proposed by MLR are therefore used as input parameters in the MNLR and ANN 14,15 .…”

Section: Multiple Linear Regressions (Mlr)mentioning

confidence: 99%

QSRR Study of Linear Retention Indices for Volatile Compounds using Statistical Methods

2018

Chem Sci Trans

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ACD/ChemSketch, MarvinSketch and ChemOffice programs were used to calculate several molecular descriptors of 138 volatile compounds (32 hydrocarbons, 29 ketones, 28 aldehydes, 23 alcohols, 7 carboxylic acids, 6 halogenated compounds, 4 furans, 2 pyrazines, 1 ester, 1 sulphur compounds, 1 pyridine, 1 amine and three other compounds). The best descriptors were selected to establish the quantitative structure retention relationship (QSRR) of linear retention indices of volatile compounds using multiple linear regressions (MLR), multiple non-linear regressions (MNLR) and artificial neural network (ANN) methods. We propose quantitative models according to these analyses. The models were used to predict the linear retention indices of the test set compounds and agreement between the experimental and predicted values was verified. The descriptors showed by QSRR study were used for study and designing of new compounds. The statistical results indicate that the predicted values are in good agreement with the experimental results. To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated and has both in addition to a performance prediction power, a favorable estimation of stability.

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“…Recently, various research articles in the literature investigated the QSPR studies of different chemical compounds. For example, the reader may go through the QSPR analysis of COVID‐19 drugs structure [12, 27], molnupiravir with some other antiviral COVID‐19 drugs [4], polycyclic aromatic hydrocarbons [9], some asthma drugs using neighborhood degree‐based indices [28],

9-

aniliioacridine derivatives [29], odorant molecules [30–32], etc.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of GQ and QG indices as potentially favorable molecular descriptors

Kumar,

Das

2024

Int J of Quantum Chemistry

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In chemical graph theory, several degree‐based topological indices are introduced and put forward in the development of quantitative structure‐property relationships (QSPR)/quantitative structure‐activity relationships (QSAR) studies. However, only a few of them are considered employable in the prediction of physical and chemical properties and biological activities of molecular compounds. Here, we focus our attention on some foremost characteristics of newly defined Geometric–Quadratic and Quadratic–Geometric indices such as prediction power, degeneracy and structure sensitivity. Based on these attributes, we discuss their comparison with other well‐established degree‐based topological indices with the help of statistical analysis and computational techniques on the data sets of octane, nonane and decane isomers. Some of the graphical approaches, statistical outcomes and computational tests exhibit the dominating nature of the and indices over other topological indices.

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QSPR study of the retention/release property of odorant molecules in pectin gels using statistical methods

Cited by 11 publications

References 14 publications

2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

QSRR Study of Linear Retention Indices for Volatile Compounds using Statistical Methods

Comparative study of GQ and QG indices as potentially favorable molecular descriptors

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