A QSAR for the hydroxyl radical reaction rate constant: validation, domain of application, and prediction

Öberg, Tomas

doi:10.1016/j.atmosenv.2005.01.007

Cited by 78 publications

(43 citation statements)

References 34 publications

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“…The rms errors of the training and test sets for aromatics in this paper are 0.282 and 0.260, respectively. While the rms errors in previous models [9][10][11]13 containing aromatics and non-aromatics range between 0.391 and 0.501, which are greater than the results in our model (Eq. 10).…”

Section: Comparison Of Modelscontrasting

confidence: 44%

“…The model had the prediction standard error of 0.501 log units, through selecting 333 descriptors and compressing to 7 latent variables. 11 Recently, Fatemi and Baher successfully developed six QSAR models for k OH of 98 alkenes, by applying several chemometric tools including MLR, genetic algorithms (GAs), ANNs and support vector machines (SVMs). 12 Wang et al used 22 molecular descriptors to build a PLS model having a rms error of 0.430 for the test set.…”

Section: -18mentioning

confidence: 99%

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Prediction of Reaction Rate Constants of Hydroxyl Radical With Organic Compounds

Chen

Zhang

2014

J. Chil. Chem. Soc.

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In this work we have performed a QSAR study of atmospheric reactions with hydroxyl radical, at the B3LYP level of theory with 6-31G(d) basis set. Molecular descriptors selected by applying multiple linear stepwise regression (MLR) analysis were used to predict the reaction rate constants (-logk OH ) of OH radicals with organic compounds in the atmosphere, including 98 alkenes and 80 aromatic compounds. For setting our reactions, we have calculated 98 alkenes and 80 aromatic compounds. A four-descriptor MLR model (rms = 0.102 and R 2 = 0.938) for 98 alkenes was developed based on the number of R=CHX functional group counts, E HOMO parameter, and Fukui indices of the double-bonded C atoms. We found vinyl chemicals with conjugated double bonds and electron-donor substituents are the most reactive systems; while alkenes with multiple halogen substitutions are the least reactive molecules. Additionally, a three-descriptor MLR model (rms = 0.282 and R 2 = 0.910) was built to predict OH radical rate constants for 80 aromatic compounds, which was dominated by the E HOMO parameter, a topological descriptor for steric hindrance, and the most positive net atomic charge on hydrogen atoms. Aromatics with electron-donor and electron-acceptor groups, respectively, possess high and low degradation rates. The halogen aromatics are less reactive, especially for aromatics with multiple halogen substitutions. In comparison to existing models, the two models obtained in this paper show better statistical quality.

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Section: Comparison Of Modelscontrasting

confidence: 44%

Section: -18mentioning

confidence: 99%

Prediction of Reaction Rate Constants of Hydroxyl Radical With Organic Compounds

Chen

Zhang

2014

J. Chil. Chem. Soc.

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“…Such a virtual screening has previously been applied to describe toxicity and environmental persistence [47 -49]. The present model could in a similar fashion be combined with the model for environmental persistence to assess the potential for a long range atmospheric transport [48,50]. It should also be noted that there is a substantial variation in the vapor pressure at environmentally relevant conditions, both seasonal and regional variations (between tropical -subtropical, temperate, and polar regions).…”

Section: Discussionmentioning

confidence: 95%

Global and Local PLS Regression Models to Predict Vapor Pressure

Öberg¹,

Liu²

2008

QSAR Comb. Sci.

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The vapor pressure is a key property in determining the distribution and fate of environmentally relevant compounds, but experimental determinations are only available for a limited number of the chemicals in current commercial use. Despite experimental efforts there is a need for estimation methods. The liquid or subcooled liquid vapor pressures at 298.15 K were collected from the literature for a diverse set of 1340 organic compounds. Theoretical molecular descriptors were derived after optimization to low-energy conformations and used to investigate the performance of global and local Quantitative Structure -Property Relationships (QSPR). A global PLSR model with ten latent variables was found to be optimal. The predictive performance of this model, within the domain of applicability, was estimated at n ¼ 420, Q 2 Ext ¼ 0.980, and RMSEP ¼ 0.410 (log Pa). This model can be used in conjunction with other estimation models to assess the potential for a long range atmospheric transport.

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“…With the development of the computer technology and quantum chemistry, the quantum chemical descriptors can clearly describe the molecular properties and can be gotten accurately and easily. Numerous studies have been published on the applications of quantum chemical descriptors in QSAR or quantitative structure-property relationships (QSPR) (Niu and Yu 2004;Thanikaivelan et al 2000). So the quantum chemical descriptors which are potentially more powerful than the other approaches were adopted in the QSAR model in this study.…”

Section: Methodsmentioning

confidence: 99%

“…However, considering large expenditures of money and time, it is difficult to determine all the mutagenic activities of NNs and MNNs in ambient atmosphere (Pamela et al 1996). Quantitative structure-activity relationships (QSARs) have been applied successfully to various areas of toxicology to predict the toxicity of organic chemicals to environmentally important species and to elucidate their link to characteristics of the molecular structure of the compounds (Cronin et al 1998;Mohan et al 2007;Niu and Yu 2004;Yan et al 2005). Hence, it is necessary to develop quantitative structure-activity relationship between mutagenicity and molecular structural descriptors to obtain the predicted value of MA effectively and efficiently.…”

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confidence: 99%

A QSAR Model for Predicting Mutagenicity of Nitronaphthalenes and Methylnitronaphthalenes

Zhang

Niu

Zhi

2008

Bull Environ Contam Toxicol

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A quantitative structure-activity relationship model for prediction of mutagenicity of nitronaphthalenes and methylnitronaphthalenes was developed using some fundamental quantum chemical descriptors. The cumulative cross-validated regression coefficient value for the optimal quantitative structure-activity relationship model is 0.711, showing a good predictive capability for mutagenicity of nitronaphthalenes and methylnitronaphthalenes. Results from this study indicate that mutagenicity of nitronaphthalenes and methylnitronaphthalenes increases with increasing frontier molecular orbital energy value, i.e., the sum of the energy of the highest occupied molecular orbital and the energy of the lowest unoccupied molecular orbital, or decreasing the energy of the second highest occupied molecular orbital, final heat of formation, and core-core repulsion energy values.

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A QSAR for the hydroxyl radical reaction rate constant: validation, domain of application, and prediction

Cited by 78 publications

References 34 publications

Prediction of Reaction Rate Constants of Hydroxyl Radical With Organic Compounds

Prediction of Reaction Rate Constants of Hydroxyl Radical With Organic Compounds

Global and Local PLS Regression Models to Predict Vapor Pressure

A QSAR Model for Predicting Mutagenicity of Nitronaphthalenes and Methylnitronaphthalenes

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