Papers by Keyword: QSPR

Abstract: The quantitative structure property relationship (QSPR) study was performed in this work to develop models to predict the normalized reaction rate constants for the reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). In order to consider the solvent effect, conductor-like polarizable continuum model (CPCM) was applied to optimize the geometries and obtain the molecular descriptors using the pseudopotential basis set. The prediction results with the inclusion of solvent effect are slightly better than that of the corresponding gas-phase calculations. The artificial neural network (ANN) model could be more satisfactory to predict the rate constants than the partial least squares regression (PLSR) and principal component analysis-multiple linear regression analysis (PCA-MLR) models.

Abstract: Neutral PBDEs congeners and their corresponding radical anions were studied with the pseudopotential method of stuttgart group (SDD) effective-core potentials basis set for the bromine atoms and the all-electron basis set for all other atoms. The pseudopotential method can be used for compounds containing heavy elements with relativistic effects and can reduce the computational time. The quantitative structure property relationship (QSPR) study was also performed in this work to develop models to predict the normolized reaction rate constants for the reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). The partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for the QSPR study between the molecular descriptors and the logarithm of normalized reaction rate constants of fourteen selected BDE congeners. The results show that the ANN models could be more satisfactorily to predict the rate constants than the PLSR and PCA-MLR models.

Abstract: The coffee flavor compounds acquire a significant place in the improving the flavor of cigarette. In the present paper, the gene expression programming (GEP) is used to develop quantitative relationships between the retention time (TR) and four molecular descriptors of 52 compounds. The model of GEP gives good statistical result. This method can be used to set up the good regression model.

Abstract: The coffee flavor compounds acquire a significant place in the improving the flavor of cigarette. In the present paper, the support vector machine is used to develop quantitative relationships between the retention time and four molecular descriptors of 52 compounds. The model of support vector machine gives good statistical results compared to those give by multiple linear regressions and support vector machine. The contribution of each descriptor to structure-retention time relationships was evaluated. It indicates the importance of the atoms number and type of parameter. The proposed method can be successfully used to predict the retention time with only four molecular descriptors which can be calculated directly from molecular structure alone.

Abstract: The coffee flavor compounds acquire a significant place in the improving the flavor of cigarette. In the present paper, the heuristic method is used to develop quantitative relationships between the retention time (TR) and four molecular descriptors of 52 compounds. The model of heuristic method gives good statistical result. The contribution of each descriptor to structure-retention time relationships was evaluated. It indicates the importance of the atoms number and type of parameter. The proposed method can be successfully used to predict the retention time with only four molecular descriptors which can be calculated directly from molecular structure alone.

Abstract: The impact sensitivity is a very important property for indicating the safety, reliability and stability of high-energy-density materials (HEDM). A quantitative structure-property relationship (QSPR) study was used for prediction of impact sensitivity of some nitro compounds. Employing the square of nitro group charge (Q_NO2²) and OB₁₀₀ as the parameters, a good QSPR model was built for predicting H₅₀ of two sorts of nitro compounds. The predictive ability of the model was assessed by leave-one-out cross-validation method. The cross-validation results shows that the model is significant and stable, and its predicted accuracy is within 0.21 m. This quantitative model may be a useful tool for the design of HEDM.

Abstract: On the basis of the quantitative structure-property relationship (QSPR) method and the quantum chemical descriptors including molecular van der Waals volume (Vmc), dipole moments (μ), the most negative formal charge in solute molecule (q-), and the most positive formal charge on a hydrogen atom in solute molecule (q+) of organic compounds, the values of activity coefficients at infinite dilution, , for 16 solutes in ionic liquid 1-ethyl-3-methylimidazolium diethylphosphate ([EMIM][DEP]) at 323.15 K were correlated with the descriptors. The result showed that the QSPR model had a good correlation and could successfully describe . The quantitative relationship between organic molecular structure and in [EMIM][DEP] was obtained and the correlation parameters were analyzed to understand the interactions that affect activity coefficients at infinite dilution.

Abstract: A quantitative structure property relationship (QSPR) study was performed in this work to develop models for predicting reaction rate constants for reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). Both multiple linear regression (MLR) and artificial neural network (ANN) methods were employed for QSPR studies based on the experimental kinetic data of the fourteen PBDE congeners. Both the developed MLR and ANN models could give satisfactory prediction abilities, and the performance of the ANN model seems slightly better than that of the MLR model. In addition, energy of lowest unoccupied molecular orbital (ELUMO) and total energy (TE) were found to be the two relatively important variables in the ANN model via the assessment using both the Garson’s algorithm and connection weight approach.

Abstract: Quantitative structure-property relationship (QSPR) models were developed in the present work for photodegradation rate constants (kp) of fifteen individual polybrominated diphenyl ethers (PBDEs) in methanol/water (8:2) by UV light in the sunlight region. The molecular descriptors used in the QSPR models were calculated by the two semi-empirical quantum mechanical methods, RM1 and PM6, respectively. Both multiple linear regression (MLR) and artificialneural network (ANN) were applied in this study. The statistic qualities of the MLR models based on the molecular parameters obtained by RM1 and PM6 calculations were both good with the R values of 0.987 and 0.990, respectively. The QSPR model built by the ANN method with the molecular parameters calculated with PM6 is slightly better than that with RM1.

Abstract: Base on structural descriptors including dipole moments (μ), Energy gap (∆ε), hydration energy (∆H), and hydrophobic parameter lg P of 25 organic solutes, the quantitative structure-property relationship (QSPR) method was used to correlate the values of activity coefficients at infinite dilution, , for the solutes in ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) at 323.15 K. The result showed that the QSPR model had a good correlation and could successfully describe . The quantitative relationship between organic molecular structure and in [EMIM][BF4] was obtained and the correlation parameters were analyzed to understand the interactions that affect activity coefficients at infinite dilution.