2D- and 3D-QSRR Studies of Linear Retention Indices for Volatile Alkylated Phenols

In this study, 29 volatile alkylated phenols were subjected to a quantitative structure retention relationships (QSRR) studies; we have developed two- and three-dimensional quantitative structure retention relationships (2D- and 3D-QSRR) for this series; and these molecules were subjected to a 2D-QSRR analysis for their retention property using stepwise multiple linear regression (MLR) and 3D-QSRR analysis using partial least squares (PLS). The 28 descriptors are calculated for the 29 molecules using the ChemOffice and ChemSketch software to construct 2D-QSRR model. The 3D-QSRR models were constructed using comparative molecular field analysis (CoMFA) method. 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 statistical results indicate that the predicted values are in good agreement with the experimental results (r2 = 0.980; r2CV = 0.977 and r2 = 0.998; r2CV = 0.959 for MLR and CoMFA methods, respectively). To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated; in addition to a performance prediction power, this coefficient has a favorable estimation of stability for the two methods (rtest = 0.938 and rtest = 0.955 for MLR and CoMFA methods, respectively)

QSPR Study of the Retention/release Property of Odorant Molecules in Water Using Statistical Methods

An integrated approach physicochemistry and structures property relationships has been carried out to study the odorant molecules retention/release phenomenon in the water. This study aimed to identify the molecular properties (molecular descriptors) that govern this phenomenon assuming that modifying the structure leads automatically to a change in the retention/release property of odorant molecules. ACD/ChemSketch, MarvinSketch, and ChemOffice programs were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). A total of 37 molecules (2/3 of the data set) were placed in the training set to build the QSPR models, whereas the remaining, 14 molecules (1/3 of the data set) constitute the test set. The best descriptors were selected to establish the quantitative structure property relationship (QSPR) of the retention/release property of odorant molecules in water using multiple linear regression (MLR), multiple non-linear regression (MNLR) and an artificial neural network (ANN) methods. We propose a quantitative model according to these analyses. The models were used to predict the retention/release property of the test set compounds, and agreement between the experimental and predicted values was verified. The descriptors showed by QSPR study are 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 performant prediction power, a favorable estimation of stability. DOI: http://dx.doi.org/10.17807/orbital.v9i4.97

Tentative Pratique du Relation Quantitatives Structure-Activité/Propriété (QSAR/QSPR)

Différentes technique statistiques tel que la régression linéaire, non linéaire, ACP, PLS, et les réseaux de neurones artificiels (ANN) ont été utilisées pour mettre en place des modèles pour la prédiction des activités biologiques. Les descripteurs des modèles ont été sélectionnés dans un jeu étendu de plusieurs descripteurs (topologiques, géométriques et quantiques). La relation quantitative structure-activité/propriété (QSAR/QSPR) modélisation se rapporte à la construction de ces modèles prédictifs d'activités biologiques différentes, en fonction de l'information de structure moléculaire et d'une banque de composés. Cet avis vise à couvrir les concepts et techniques essentielles qui sont pertinents pour la réalisation d'études QSAR / QSPR grâce à l'utilisation d'exemples choisis dans nos travaux précédents

Basic approaches and applications of QSAR/QSPR methods

The main objective of this paper is todescribe briefly the applications and methodologies involved in QSAR/QSPR, relate and comparethem to some of our various preceding published works.An intriguing and important field of activity for applying the results discussed in this work is QSAR and QSPR. Theoretical and practical results toward the statistical analysis and modeling of molecular descriptors were presented. Particularly with more emphasis on employing statistical methods for modeling data by using molecular descriptors

QSAR Studies of Toxicity Towards Monocytes with (1,3-benzothiazol-2-yl) amino-9-(10H)-acridinone Derivatives Using Electronic Descriptors

DFT-B3LYP method, with the basis set 6-31G (d), was employed to calculate nine quantum chemical descriptors of 16 acridin-9-(10H)-ones substituted with amino or (1,3-benzothiazol-2-yl)-amino groups compounds. The above descriptors were used to establish a Quantitative Structure Activity Relationship (QSAR) of the Anti-proliferative towards human monocytes activity of these compounds by Multiple Linear Regression (MLR), Multiple Non Linear Regression (MNLR) and Artificial Neural Network (ANN). The statistical results indicate that the correlation coefficients R were 0.864, 0.908 and 0.844 respectively. Results showed that the three modeling methods can provide a good prediction of the studied  activity and may be useful for predicting the bioactivity of new compounds of similar class, and showed that the Multiple Non Linear Regression (MNLR) results have substantially better predictive capability than the MLR and ANN. The statistical results indicate that the models are statistically significant and show very good stability towards data variation in leave one out cross validation. DOI: http://dx.doi.org/10.17807/orbital.v7i2.67

Theoretical Study of 1,3-Dipolar Cycloadditions Regioselectivity of Benzyl Azide with Glycosyl-O Acetylene Using Density Functional Theory (DFT)

A theoretical study of 1,3-cycloaddition has been carried out using density functional theory (DFT) methods at the B3LYP/6-31G* level. The regioselectivity of the reaction have been clarified through different theoretical approaches: Case of a Two-Center Process (Domingo approach), HSAB principle (Gazquez and Mendez approach), and the activation energy calculations. The analysis of results shows that the reaction takes place along concerted asynchronous mechanism and the isomer meta is favored, in agreement with the experiment results. DOI: http://dx.doi.org/10.17807/orbital.v9i5.101

Méthodologie générale d’une étude RQSA/RQSP

Le développement des modèles prédictifs des relations quantitatives structure-activité/propriété (RQSA/RQSP) joue un rôle important dans la conception des produits chimiques à usage fine, par exemple les produits pharmaceutiques. Compte tenu de large application de différents types de produits chimiques dans la vie humaine, la modélisation RQSA/RQSP est un outil utile pour la prédiction de l’activité biologique, propriété physicochimique et toxicologique des produits chimiques non testées. Les descripteurs moléculaires jouent un rôle critique dans le développement d’un modèle RQSA/RQSP car ils représentent quantitativement les informations chimiques codées. Ils aident non seulement dans la dérivation d’une corrélation mathématique entre la structure chimique et la réponse d’intérêt, mais ils permettent aussi l’exploration de l’aspect mécanistique impliqué dans un processus biochimique. L’analyse RQSA/RQSP est maintenant largement utilisée comme outil rationnel pour la découverte de médicaments et l’évaluation de risques environnementales