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

Assia Belhassan, Samir Chtita, Tahar Lakhlifi, Mohammed Bouachrine


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.v9i4.978



odorant molecules; retention/release; quantitative structure property relationship; multiple linear regression; artificial neural network

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