QSPR Prediction of Henry’s Law Constant: Improved Correlation with New Parameters

Dearden, John C.; Ahmed, Shazia; Cronin, Mark; Sharra, Janeth A.

doi:10.1007/978-1-4615-4141-7_37

Cited by 7 publications

(4 citation statements)

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“…Dearden et al [47] extended their earlier log K aw prediction studies [26] by the inclusion of HYBOT quantitative hydrogen bonding descriptors and a count of the number of rotatable bonds in a molecule (a measure of conformational flexibility). Using the same data set as before, they developed an improved seven‐descriptor QSPR: where 4 χ pc = fourth‐order simple path‐cluster molecular connectivity, E LUMO = energy of the lowest unoccupied molecular orbital, α = HYBOT hydrogen bond donor ability, B R = number of rotatable bonds, and r

_{2}^{adj}

= r 2 adjusted for degrees of freedom.…”

Section: Methods Using Physicochemical and Structural Descriptorsmentioning

confidence: 99%

“…Dearden et al [47] extended their earlier log K aw prediction studies [26] by the inclusion of HYBOT quantitative hydrogen bonding descriptors and a count of the number of rotatable bonds in a molecule (a measure of conformational flexibility). Using the same data set as before, they developed an improved seven-descriptor QSPR: The term 4 pc probably models branching (an aspect of shape), whereby more spherical molecules have less surface area exposed to water and thus have less interaction with the solvent.…”

Section: Methods Using Physicochemical and Structural Descriptorsmentioning

confidence: 99%

“…It is interesting to note that Equation 17, developed by Dearden et al [47] using a very diverse data set, also includes the 4 χ pc term.…”

Section: Predictions Involving Specific Chemical Classesmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative structure‐property relationships for predicting henry's law constant from molecular structure

Dearden

Schüürmann

2003

Enviro Toxic and Chemistry

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Abstract-Various models are available for the prediction of Henry's law constant (H ) or the air-water partition coefficient (K aw ), its dimensionless counterpart. Incremental methods are based on structural features such as atom types, bond types, and local structural environments; other regression models employ physicochemical properties, structural descriptors such as connectivity indices, and descriptors reflecting the electronic structure. There are also methods to calculate H from the ratio of vapor pressure ( p v ) and water solubility (S w ) that in turn can be estimated from molecular structure, and quantum chemical continuum-solvation models to predict H via the solvation-free energy (⌬G s ). This review is confined to methods that calculate H from molecular structure without experimental information and covers more than 40 methods published in the last 26 years. For a subset of eight incremental methods and four continuum-solvation models, a comparative analysis of their prediction performance is made using a test set of 700 compounds that includes a significant number of more complex and drug-like chemical structures. The results reveal substantial differences in the application range as well as in the prediction capability, a general decrease in prediction performance with decreasing H, and surprisingly large individual prediction errors, which are particularly striking for some quantum chemical schemes. The overall best-performing method appears to be the bond contribution method as implemented in the HEN-RYWIN software package, yielding a predictive squared correlation coefficient (q 2 ) of 0.87 and a standard error of 1.03 log units for the test set.

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_{2}^{adj}

= r 2 adjusted for degrees of freedom.…”

Section: Methods Using Physicochemical and Structural Descriptorsmentioning

confidence: 99%

“…Dearden et al [47] extended their earlier log K aw prediction studies [26] by the inclusion of HYBOT quantitative hydrogen bonding descriptors and a count of the number of rotatable bonds in a molecule (a measure of conformational flexibility). Using the same data set as before, they developed an improved seven-descriptor QSPR: The term 4 pc probably models branching (an aspect of shape), whereby more spherical molecules have less surface area exposed to water and thus have less interaction with the solvent.…”

Section: Methods Using Physicochemical and Structural Descriptorsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative structure‐property relationships for predicting henry's law constant from molecular structure

Dearden

Schüürmann

2003

Enviro Toxic and Chemistry

Self Cite

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“…The class-2 contains those correlations called quantitative structure property relationships (QSPR) which use only molecular-based parameters to predict the H. The most well-known correlations of this class are those correlations presented by Hine and Mookerjee, 8 Meylan and Howard, 9,10 Abraham et al, 11 Katritzky et al, 12 Dearden et al, [13][14][15] English and Carrol, 16 Yao et al, 17 Lin and Sandler, 18 and Yafe et al 19 The most important disadvantage of the majority of these correlations is their complex procedure for computations of molecular-based parameters, so the majority of these correlations are not usually simple to apply. It seems the simplest type of the correlations of this class is those correlations called group contribution methods (GC).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Henry’s Law Constant of Organic Compounds in Water from a New Group-Contribution-Based Model

Gharagheizi¹,

Abbasi²,

Tirandazi³

2010

Ind. Eng. Chem. Res.

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In this work, a new model is presented for estimation of Henry’s law constant of pure compounds in water at 25 °C (H). This model is based on a combination between a group contribution method and neural networks. The needed parameters of the model are the occurrences of a new collection of 107 functional groups. On the basis of these 107 functional groups, a feed forward neural network is presented to estimate the H of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation error, and root-mean-square error of the model over a diverse set of 1940 pure compounds used are, respectively, 0.9981, 2.84%, 2.4, and 0.1 (all the values obtained using log H based data). Therefore, the model is a comprehensive and an accurate model and can be used to predict the H of a wide range of chemical families of pure compounds in water better than previously presented models.

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Prediction of Physicochemical Properties

Dearden

2012

Methods in Molecular Biology

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Physicochemical properties are key factors in controlling the interactions of xenobiotics with living organisms. Computational approaches to toxicity prediction therefore generally rely to a very large extent on the physicochemical properties of the query compounds. Consequently it is important that reliable in silico methods are available for the rapid calculation of physicochemical properties. The key properties are partition coefficient, aqueous solubility, and pKa and, to a lesser extent, melting point, boiling point, vapor pressure, and Henry's law constant (air-water partition coefficient). The calculation of each of these properties from quantitative structure-property relationships (QSPRs) and from available software is discussed in detail, and recommendations made. Finally, detailed consideration is given of guidelines for the development of QSPRs and QSARs.

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QSPR Prediction of Henry’s Law Constant: Improved Correlation with New Parameters

Cited by 7 publications

References 1 publication

Quantitative structure‐property relationships for predicting henry's law constant from molecular structure

Quantitative structure‐property relationships for predicting henry's law constant from molecular structure

Prediction of Henry’s Law Constant of Organic Compounds in Water from a New Group-Contribution-Based Model

Prediction of Physicochemical Properties

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