Wyniki wyszukiwania - BazTech

1

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

Saaidpour S., Ghaderi F.

Computational Methods in Science and Technology

|

2016

|

Vol. 22, No. 3

133--141

EN

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.

2

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

Saaidpour S.

Computational Methods in Science and Technology

|

2016

|

Vol. 22, No. 1

41--53

EN

Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromatographic 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 (Q2 ext) of 0.9913 and root mean square error (RMSE) of 0.0169.

3

Prediction the Normal Boiling Points of Primary, Secondary and Tertiary Liquid Amines from their Molecular Structure Descriptors

Saaidpour S., Bahmani A., Rostami A.

Computational Methods in Science and Technology

|

2015

|

Vol. 21, No. 4

201--210

EN

In this article, at first, a quantitative structure–property relationship (QSPR) model for estimation of the normal boiling point of liquid amines is developed. QSPR study based multiple linear regression was applied to predict the boiling points of primary, secondary and tertiary amines. The geometry of all amines was optimized by the semi-empirical method AM1 and used to calculate different types of molecular descriptors. The molecular descriptors of structures were calculated using Molecular Modeling Pro plus software. Stepwise regression was used for selection of relevance descriptors. The linear models developed with Molegro Data Modeller (MDM) allow accurate estimate of the boiling points of amines using molar mass (MM), Hansen dispersion forces (DF), molar refractivity (MR) and hydrogen bonding (HB) (1◦ and 2◦ amines) descriptors. The information encoded in the descriptors allows an interpretation of the boiling point studied based on the intermolecular interactions. Multiple linear regression (MLR) was used to develop three linear models for 1◦ , 2◦ and 3◦ amines containing four and three variables with a high precision root mean squares error, 15.92 K, 9.89 K and 15.76 K and a good correlation with the squared correlation coefficient 0.96, 0.98 and 0.96, respectively. The predictive power and robustness of the QSPR models were characterized by the statistical validation and applicability domain (AD).