F. Nasri

Bio: F. Nasri is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

QSPR study of molar diamagnetic susceptibility of diverse organic compounds using multiple linear regression analysis

Abstract: The multiple linear regression (MLR) was used to build the linear quantitative structure-property relationship (QSPR) model for the prediction of the molar diamagnetic susceptibility (χm) for 140 diverse organic compounds using the three significant descriptors calculated from the molecular structures alone and selected by stepwise regression method. Stepwise regression was employed to develop a regression equation based on 100 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The stability of the proposed model was validated using Leave-One-Out cross-validation and randomization test. Application of the developed model to a testing set of 40 organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. By applying MLR method we can predict the test set (40 compounds) with Q 2 ext of 0.9894 and average root mean square error (RMSE) of 2.2550. The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data. The prediction results are in good agreement with the experimental values.

Quantitative Modeling for Prediction of Critical Temperature of Refrigerant Compounds

Abstract: The quantitative structure-property relationship (QSPR) method is used to develop the correlation between structures of refrigerants (198 compounds) and their critical temperature. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using a genetic algorithm (GA) was used in the QSPR model development. Multiple linear regressions (MLR) were utilized to construct the linear prediction model. The prediction result agrees well with the experimental value of this property. The comparison results indicate the superiority of the presented model and reveal that it can be effectively used to predict the critical temperatures of refrigerant compounds from the molecular structures alone. The stability and predictivity of the proposed model were validated using internal validation, external validation and Y-scrambling. Application of the developed model to a testing set of 39 organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. The R 2 , RMSEtr and Q 2 loo values for the training set were 0.9752, 13.8994 and 0.9742; Q 2 ext and RMSEpr for test set were 0.9766 and 12.8654 for GA-MLR model, respectively. The prediction results are in good agreement with the experimental values. In addition, the applicability domain (AD) of the model was analyzed based on the Williams plot.

Computational Model For Chromatographic Relative Retention Time of Polychlorinated Biphenyls Using Sub-structural Molecular Fragments

Abstract: Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q 2) of 0.9913 and root mean square error (RMSE) of 0.0169.

Quantitative Modeling of Physical Properties of Crude Oil Hydrocarbons Using Volsurf+ Molecular Descriptors

Abstract: The quantitative structure-property relationship (QSPR) method is used to develop the correlation between structures of crude oil hydrocarbons and their physical properties. In this study, we used VolSurf+ descriptors for QSPR modeling of the boiling point, Henry law constant and water solubility of eighty crude oil hydrocarbons. A subset of the calculated descriptors selected using stepwise regression (SR) was used in the QSPR model development. Multivariate linear regressions (MLR) are utilized to construct the linear models. The prediction results agree well with the experimental values of these properties. The comparison results indicate the superiority of the presented models and reveal that it can be effectively used to predict the boiling point, Henry law constant and water solubility values of crude oil hydrocarbons from the molecular structures alone. The stability and predictivity of the proposed models were validated using internal validation (leave one out and leave many out) and external validation. Application of the developed models to test a set of 16 compounds demonstrates that the new models are reliable with good predictive accuracy and simple formulation.