QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIPS ON DISSOLVABILITY OF PCDD/Fs USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

2010 ◽  
Vol 09 (supp01) ◽  
pp. 9-22 ◽  
Author(s):  
GUI-NING LU ◽  
XUE-QIN TAO ◽  
ZHI DANG ◽  
WEILIN HUANG ◽  
ZHONG LI
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Quantum Chemical ◽  
Good Prediction ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantum Chemical Descriptors ◽  
Validation Test ◽  
Pls Analysis ◽  
Chemical Descriptors

The environmental fate of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) has become a major issue in recent decades. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, QSPR models were established for estimating water solubility (- log S W ) and n-octanol/water partition coefficient ( log KOW) of PCDD/Fs. Quantum chemical descriptors computed with density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for - log S W and log K OW of PCDD/Fs. Optimized models with high correlation coefficients (R2 > 0.983) were obtained for estimating - log S W and log K OW of PCDD/Fs. Both the internal cross validation test [Formula: see text] and external validation test (R2 > 0.965) results showed that the obtained models had high-precision and good prediction capability. The - log S W } and log K OW values predicted by the obtained models are very close to those observed. The PLS analysis indicated that PCDD/Fs with larger electronic spatial extent (R e ), lower molecular total energy (E T ), and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals (E LUMO -E HOMO ) tend to be less soluble in water but more lipophilic.

ESTIMATION OF DISSOLVABILITY OF CHLORIC AND ALKYL BENZENE DERIVATIVES USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

2008 ◽  
Vol 07 (05) ◽  
pp. 989-999 ◽  
Author(s):  
XUE-QIN TAO ◽  
GUI-NING LU ◽  
HONG-LIN FEI ◽  
KANG-QUN ZHOU
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Quantum Chemical ◽  
Density Functional ◽  
Benzene Derivatives ◽  
Quantum Chemical Descriptors ◽  
Alkyl Benzene ◽  
Validation Test ◽  
Pls Analysis ◽  
Chemical Descriptors

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports two optimal QSPR models for estimating water solubility ( log S W ) and n-octanol/water partition coefficient ( log K OW ) of chloric and alkyl benzene derivatives. Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log S W and log K OW of chloric and alkyl benzene derivatives. The correlation coefficients of the optimal models for log S W and log K OW were 0.973 and 0.990, respectively. The results of internal cross-validation test and external validation test showed that both of the optimal models had high fitting precision and good predicting ability. The log S W and log K OW values predicted by the optimal models are very close to those observed. The PLS analysis indicated that chloric and alkyl benzene derivatives with larger electronic spatial extent and lower molecular total energy tend to be more hydrophobic and lipophilic, and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals leads to larger dissolvability.

QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY

2008 ◽  
Vol 16 (02) ◽  
pp. 279-293 ◽  
Author(s):  
CHANIN NANTASENAMAT ◽  
THEERAPHON PIACHAM ◽  
TANAWUT TANTIMONGCOLWAT ◽  
THANAKORN NAENNA ◽  
CHARTCHALERM ISARANKURA-NA-AYUDHYA ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Quantum Chemical ◽  
Partial Least Squares Regression ◽  
Homoserine Lactone ◽  
Acyl Homoserine Lactone ◽  
Least Squares Regression ◽  
Quantum Chemical Descriptors ◽  
Data Set ◽  
Chemical Descriptors

A quantitative structure-activity relationship (QSAR) study was performed to model the lactonolysis activity of N-acyl-homoserine lactone lactonase. A data set comprising of 20 homoserine lactones and related compounds was taken from the work of Wang et al. Quantum chemical descriptors were calculated using the semiempirical AM1 method. Partial least squares regression was utilized to construct a predictive model. This computational approach reliably reproduced the lactonolysis activity with high accuracy as illustrated by the correlation coefficient in excess of 0.9. It is demonstrated that the combined use of quantum chemical descriptors with partial least squares regression are suitable for modeling the AHL lactonolysis activity.

scholarly journals Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

2008 ◽  
Vol 6 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Gui-Ning Lu ◽  
Xue-Qin Tao ◽  
Zhi Dang ◽  
Xiao-Yun Yi ◽  
Chen Yang
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Density Functional ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

DFT-based quantum theory QSPR studies of molar heat capacity and molar polarization of vinyl polymers

2008 ◽  
Vol 62 (6) ◽  
Author(s):  
Xinliang Yu ◽  
Bing Yi ◽  
Wenhao Yu ◽  
Xueye Wang
Keyword(s):  
Heat Capacity ◽  
Quantum Chemical ◽  
Molar Heat Capacity ◽  
Correlation Coefficients ◽  
Basis Set ◽  
Structure Property ◽  
Quantum Chemical Descriptors ◽  
Vinyl Polymers ◽  
Molar Polarization ◽  
Chemical Descriptors

AbstractIn this study, the DFT/B3LYP level of theory with the 6-31G (d) basis set was used to calculate a set of quantum chemical descriptors for structure units of vinyl polymers. These descriptors were used to predict the molar heat capacity of “liquid” at constant pressure (C P1(298 K)) and the molar Lorentz and Lorenz polarization (P LL). Two more physically meaningful quantitative structure-property relationship (QSPR) models obtained from the training sets applying multiple linear stepwise regression (MLR) analysis were evaluated externally using the test sets. Correlation coefficients between the predicted and the experimental values were: 0.998 for C P1(298 K) and 0.979 for P LL. The results indicate that the QSPR models constructed using quantum chemical descriptors can be applied to predict the properties of polymers confirming the role of quantum chemical descriptors in the QSPRs studies of polymers.

ESTIMATION OF SOIL SORPTION COEFFICIENTS OF POLYCYCLIC AROMATIC HYDROCARBONS BY QUANTUM CHEMICAL DESCRIPTORS

2008 ◽  
Vol 07 (01) ◽  
pp. 67-79 ◽  
Author(s):  
GUI-NING LU ◽  
CHEN YANG ◽  
XUE-QIN TAO ◽  
XIAO-YUN YI ◽  
ZHI DANG
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Soil Sorption ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Soils And Sediments ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic soil sorption coefficients (log K OC ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed using density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for log K OC of PAHs. The correlation coefficient of the optimal model was 0.993, and the results of a cross-validation test ([Formula: see text]) showed this optimal model had high fitting precision and good predicting ability. The log K OC values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent tend to more easily adsorb and accumulate in soils and sediments, whereas those with higher molecular total energy and larger energy gap between the lowest unoccupied and the highest occupied molecular orbital adsorb and accumulate in soils and sediments less readily.

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