QSAR analysis of the toxicity of phenols and thiophenols using MLR and ANN

Ghamali, Mounir; Chtita, Samir; Ousaa, Abdellah; Elidrissi, B.; Bouachrine, Mohammed; Lakhlifi, Tahar

doi:10.1016/j.jtusci.2016.03.002

Cited by 27 publications

(11 citation statements)

References 15 publications

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“…The matrix obtained provides information on the high or low interrelationship between the variables. In general, good co-linearity (r > 0.5) [11] was observed between most of the variables. High interrelationship was observed between O% and C% (r = −0.975), and low interrelationship was observed between W and I (r = −2.567.10 −4 ).…”

Section: Principal Components Analysismentioning

confidence: 78%

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

Belhassan

Chtita

Lakhlifi

et al. 2017

Journal of Taibah University for Science

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The ACD/ChemSketch, MarvinSketch, and ChemOffice programmes were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). The best descriptors were selected to establish the Quantitative Structure-Property Relationship (QSPR) of the retention/release property of odorant molecules in pectin gels using Principal Components Analysis (PCA), Multiple Linear Regression (MLR), Multiple Non-linear Regression (MNLR) and Artificial Neural Network (ANN) methods We propose a quantitative model based on these analyses. PCA has been used to select descriptors that exhibit high correlation with the retention/release property. The MLR method yielded correlation coefficients of 0.960 and 0.958 for PG-0.4 (pectin concentration: 0.4% w/w) and PG-0.8 (pectin concentration: 0.8% w/w) media, respectively. Internal and external validations were used to determine the statistical quality of the QSPR of the two MLR models. The MNLR method, considering the relevant descriptors obtained from the MLR, yielded correlation coefficients of 0.978 and 0.975 for PG-0.4 and PG-0.8 media, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outside compounds. The effects of different descriptors on the retention/release property are described, and these descriptors were used to study and design new compounds with higher and lower values of the property than the existing ones.

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Section: Principal Components Analysismentioning

confidence: 78%

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

Belhassan

Chtita

Lakhlifi

et al. 2017

Journal of Taibah University for Science

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“…It has also served to select descriptors that are used as input parameters in multiple nonlinear regression (MNLR).The MLR and MNLR techniques was employed to model the structure-toxicity relationships. The equations were justified by the correlation coefficient (R), the Mean Squared Error (MSE), the Fisher F-statistic (F), and the significance level (p-value) [10].…”

Section: Discussionmentioning

confidence: 99%

Quantitative structure - toxicity relationship studies of aromatic aldehydes to Tetrahymena pyriformis base d on electronic and topological descriptors

Oussa¹,

Elidrissi²,

Ghamali³

et al. 2018

jmes

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“…Using these new variables, the dimensionality of the system is reduced with a minimum loss of information 15 . The obtained matrix of coordinates allows us to analyze the dispersion of individuals in the new defined space [16][17][18] . After that, the principal component analysis (PCA) was used to determine the non-linearity and nonmulticollinearity among variables and to select descriptors that correlate with the activity.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

QSAR Study of (5-Nitroheteroaryl-1,3,4-thiadiazole-2- yl)piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

2019

Chem Sci Trans

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To search of newer and potent antileishmanial drugs, a series of 36 compounds of 5-(5nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives were subjected to a quantitative structure-activity relationship (QSAR) analysis for studying, interpreting and predicting activities and designing new compounds using several statistical tools, The multiple linear regression (MLR), non-linear regression (RNLM) and artificial neural network (ANN) models were developed using 30 molecules having pIC 50 ranging from 3.155 to 5.046. The best generated MLR, RNLM and ANN models show conventional correlation coefficients R of 0.750, 0.782 and 0.967 as well as their leave-one-out cross-validation correlation coefficients R CV of 0.722, 0.744 and 0.720, respectively. The predictive ability of those models was evaluated by the external validation using a test set of 6 molecules with predicted correlation coefficients R test of 0.840, 0.850 and 0.802, respectively. The applicability domains of MLR and MNLR transparent models were investigated using William's plot to detect outliers and outsides compounds. We expect that this study would be of great help in lead optimization for early drug discovery of new similar compounds.

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QSAR analysis of the toxicity of phenols and thiophenols using MLR and ANN

Cited by 27 publications

References 15 publications

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

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

Quantitative structure - toxicity relationship studies of aromatic aldehydes to Tetrahymena pyriformis base d on electronic and topological descriptors

QSAR Study of (5-Nitroheteroaryl-1,3,4-thiadiazole-2- yl)piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

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