Prediction of environmental partition coefficients and the Henry’s law constants for 135 congeners of chlorodibenzothiophene

This work presents the abilities in estimation and prediction of the octanol-water partition coefficient of some para-substituted phenols through the integration of complex structures information by the use of an original molecular descriptors family on the structure-property relationship approach. The proposed approach uses the complex information obtained from para-substituted phenols structure in order to generate and calculate the molecular descriptors family. The structure-property relationship models were built based on the generated descriptors. The obtained multi-varied models (model with two and four descriptors, respectively) were validated through the assessment of the crossvalidation leave-one-out score. The comparison between the multi-varied model with two and four descriptors was performed using Steiger's Z-test. The analysis of the statistical characteristics of the obtained models demonstrated that the model with four descriptors has greater ability to estimate and predict compared with the model with two descriptors. This observation was also sustained by the results of correlated-correlation analysis. The multi-varied model with four descriptors revealed that the octanol-water partition coefficient of studied para-substituted phenols is likely to be of geometry nature, it is strongly dependent on the partial charges of compounds and group electronegativity, and it is in relation to the elastic force.

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“…Partition coefficients are used in medicinal chemistry [2], drug design [3], toxicology [4], and environmental chemistry [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Jäntschi

Bolboacă

2007

Int J of Quantum Chemistry

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“…This approach was earlier applied to environmental contaminants such as polychlorinated naphthalenes (PCNs) and polychlorinated dibenzothiophenes (PCDTs). [19][20][21][22][23][24][25][26][27][28][29] A computational approach is a useful mode in generation of physical-chemical property data, especially for large sets of compounds lacking pure standards and experimental data. [26][27][28][29][30][31][32] The advantage of a neural network over the regression analysis methods in computational chemistry is its inherent ability to incorporate the non-linear relationships existing between descriptors and physical-chemical properties.…”

Section: Softwarementioning

confidence: 99%

Use of quantitative-structure property relationship (QSPR) and artificial neural network (ANN) based approaches for estimating the octanol-water partition coefficients of the 209 chlorinatedtrans-azobenzene congeners

Wilczyńska-Piliszek

Piliszek

Falandysz

2012

Journal of Environmental Science and Health, Part B

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Polychlorinated azobenzenes (PCABs) can be found as contaminant by products in 3,4-dichloroaniline and its derivatives and in the herbicides Diuron, Linuron, Methazole, Neburon, Propanil and SWEP. Trans congeners of PCABs are physically and chemically more stable and so are environmentally relevant, when compared to unstable cis congeners. In this study, to fulfill gaps on environmentally relevant partitioning properties of PCABs, the values of n-octanol/water partition coefficients (log K(OW)) have been determined for 209 congeners of chloro-trans-azobenzene (Ct-AB) by means of quantitative structure-property relationship (QSPR) approach and artificial neural networks (ANN) predictive ability. The QSPR methods used based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G basis set in Gaussian 03 and of the semi-empirical quantum chemistry method (PM6) of the molecular orbital package (MOPAC). Polychlorinated dibenzo-p-dioxins (PCDDs), -furans (PCDFs) and -biphenyls (PCBs), to which PCABs are related, were reference compounds in this study. An experimentally obtained data on physical and chemical properties of PCDD/Fs and PCBs were reference data for ANN predictions of log K(OW) values of Ct-ABs in this study. Both calculation methods gave similar results in term of absolute log K(OW) values, while the models generated by PM6 are considered highly efficient in time spent, when compared to these by DFT. The estimated log K(OW) values of 209 Ct-ABs varied between 5.22-5.57 and 5.45-5.60 for Mono-, 5.56-6.00 and 5.59-6.07 for Di-, 5.89-6.56 and 5.91-6.46 for Tri-, 6.10-7.05 and 6.13-6.80 for Tetra-, 6.43-7.39 and 6.48-7.14 for Penta-, 6.61-7.78 and 6.98-7.42 for Hexa-, 7.41-7.94 and 7.34-7.86 for Hepta-, 7.99-8.17 and 7.72-8.20 for Octa-, 8.35-8.42 and 8.10-8.62 for NonaCt-ABs, and 8.52-8.60 and 8.81-8.83 for DecaCt-AB. These log K(OW) values shows that Ct-ABs are compounds of relatively low environmental mobility (log K(OW) > 4.5) and of significant bioaccumulation potential.

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“…[9,20] The partition coefficients such as n-octanol/water partition (log K OW ) is among the key partitioning constants in the environmental sciences and engineering that enable on assessment of environmental fate of substances including a bioaccumulation potential (BAP) or atmospheric longrange transport potential (LRTP). [21][22][23][24][25][26] The quantitative-structure property relationship (QSPR) and artificial neural network (ANN) predictions with semiempirical and ab initio quantum-chemistry computational methods highly help in determination of physical and chemical properties of numerous environmentally relevant compounds lacking analytical standards and experimentall data. [24,[27][28][29][30][31][32][33][34][35][36] In this study, to fulfill the data gaps, the comprehensive computational into the values of octanolwater partition coefficient (log K OW ) undertaken have been for all theoretically possible 399 congeners chloro transazoxybenzene (Ct-AOB).…”

Section: 4-dichloroanilinementioning

confidence: 99%

N-octanol-water partition coefficients (log K_OW) of 399 congeners of polychlorinated azoxybenzenes (PCAOBs) determined by QSPR- and ANN-based approach

Piliszek

Wilczyńska-Piliszek

Falandysz

2011

Journal of Environmental Science and Health, Part A

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Polychlorinated azoxybenzenes (PCAOBs) theoretically consist of 798 congeners with 399 in cis and 399 in trans configuration. PCAOBs in trans configuration are largerly planar compounds and some are highly toxic and environmentally relevant compared to cis congeners. Trans-PCAOBs can be found as by-side products in 3,4-dichloroaniline and some herbicides. To fulfill gaps in physical and chemical properties of PCAOBs, the values of log K(OW) were determined for 399 congeners of t-CAOB using a computational approach. We used the semi-empirical RM1 in MOPAC and DFT B3LYP in Gaussian 03 methods, artificial neural net (ANN) predictions, and the standardized variables with and without the normal varimax rotation. The models created predicted the values of log K(OW) of all 399 chlorinated derivatives of trans-azoxybenzenes (C-t-AOBs). The values of log K(OW) of C-t-AOBs varied between 5.08 and 5.42 for Mono-, 5.16 and 5.96 for Di-, 5.79 and 6.73 for Tri-, 6.26 and 7.18 for Tetra-, 6.65 and 7.54 for Penta-, 7.13 and 7.94 for Hexa-, 7.20 and 8.20 for Hepta-, 7.96 and 8.32 for Octa-, 8.32 and 8.43 for Nonachloro-t-AOBs and 8.55 and 8.97 for Decachloro-t-AOB. These log K(OW) values were similar per chloro-t-AOB congener and independent of the calculation method. C-t-AOBs have log K(OW) values above 4.5, and what relates to contaminants of low or very low environmentally mobility but a high predilection to the soil and sediment particles and with potential for bioaccumulation. The models that used the standardized variables had smallest errors and higher correlation coefficients compared to the models that based on the normal varimax rotation of standardized structural descriptors. In light of these data, the semi-empirical RM1 calculations in MOPAC software and followed by ANN were a much less time consuming and less expensive compared to the DFT B3LYP method.

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Prediction of environmental partition coefficients and the Henry’s law constants for 135 congeners of chlorodibenzothiophene

Cited by 36 publications

References 33 publications

Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Use of quantitative-structure property relationship (QSPR) and artificial neural network (ANN) based approaches for estimating the octanol-water partition coefficients of the 209 chlorinatedtrans-azobenzene congeners

N-octanol-water partition coefficients (log K_OW) of 399 congeners of polychlorinated azoxybenzenes (PCAOBs) determined by QSPR- and ANN-based approach

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Prediction of environmental partition coefficients and the Henry’s law constants for 135 congeners of chlorodibenzothiophene

Cited by 36 publications

References 33 publications

Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Modeling the octanol–water partition coefficient of substituted phenols by the use of structure information

Use of quantitative-structure property relationship (QSPR) and artificial neural network (ANN) based approaches for estimating the octanol-water partition coefficients of the 209 chlorinatedtrans-azobenzene congeners

N-octanol-water partition coefficients (log KOW) of 399 congeners of polychlorinated azoxybenzenes (PCAOBs) determined by QSPR- and ANN-based approach

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N-octanol-water partition coefficients (log K_OW) of 399 congeners of polychlorinated azoxybenzenes (PCAOBs) determined by QSPR- and ANN-based approach