Correlations between quantum chemical indices and the p<i>K</i><sub>a</sub>s of a diverse set of organic phenols

Kreye, W. C.; Seybold, Paul G.

doi:10.1002/qua.22343

Cited by 20 publications

(20 citation statements)

References 16 publications

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“…QM QSPR models for p K a prediction in phenols, similar to those presented in this paper (i.e., employing similar charges) were previously published by Gross and Seybold [22], Kreye and Seybold [23] and Svobodova and Geidl [24]. Table 5 shows a comparison between these models and the models developed in this study.…”

Section: Resultsmentioning

confidence: 73%

Predicting pK a values from EEM atomic charges

et al. 2013

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The acid dissociation constant p Kais a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p Kaprediction. We have evaluated the p Kaprediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created for 8 different quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a training set of 74 substituted phenols. Additionally, for each molecule we generated its dissociated form by removing the phenolic hydrogen. For all the molecules in the training set, we then calculated EEM charges using the 18 parameter sets, and the QM charges using the 8 above mentioned charge calculation schemes. For each type of QM and EEM charges, we created one QSPR model employing charges from the non-dissociated molecules (three descriptor QSPR models), and one QSPR model based on charges from both dissociated and non-dissociated molecules (QSPR models with five descriptors). Afterwards, we calculated the quality criteria and evaluated all the QSPR models obtained. We found that QSPR models employing the EEM charges proved as a good approach for the prediction of p Ka(63% of these models had R2 > 0.9, while the best had R2 = 0.924). As expected, QM QSPR models provided more accurate p Kapredictions than the EEM QSPR models but the differences were not significant. Furthermore, a big advantage of the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly faster than the QM charge descriptors. Moreover, we found that the EEM QSPR models are not so strongly influenced by the selection of the charge calculation approach as the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by cross-validation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on carboxylic acids. In summary, EEM QSPR models constitute a fast and accurate p Kaprediction approach that can be used in virtual screening.Electronic supplementary materialThe online version of this article (doi:10.1186/1758-2946-5-18) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 73%

Predicting pK a values from EEM atomic charges

et al. 2013

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“…It is apparent from the ESP maps above and those in Figure 2 that the depiction of an atomic charge in a molecule as a single number, despite the usefulness of such numbers in numerous applications [10,[14][15][16], is clearly limited and fails to represent the complexity of the actual charge distribution. Note the positive regions (blue and green) on the bromine ESP surface and (green) on the chlorine surface.…”

Section: Gross Hadad and Seyboldmentioning

confidence: 93%

“…Nonetheless, the concept of an atomic charge in a molecule has historically been an especially important tool for chemists, and a variety of schemes, both quantum chemical and empirical, have been proposed to represent partial atomic charges [9,10]. Among the quantum chemical schemes, charges based on the natural population analysis (NPA) approach of Reed et al [11] and the atoms in molecules (AIM) approach of Bader [12] have been shown in earlier studies to correlate strongly with variations in a variety of molecular physical and chemical properties, such as inversion barriers and pK a s [13][14][15][16], and these measures of partial atomic charge have therefore been examined in the present work. Also, charges calculated by the empirical method of Gasteiger et al [17][18][19], which is based on Sanderson's principle of electronegativity equalization, were determined.…”

Section: Introductionmentioning

confidence: 99%

Charge competition in halogenated hydrocarbons

Gross¹,

Hadad²,

Seybold³

2011

Int J of Quantum Chemistry

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ABSTRACT:The distribution of electronic charge in a molecule plays a major role in determining the molecule's physical, chemical, and biological properties. For studies of charge distributions, halogenated hydrocarbons are especially informative prototype systems, because the overall charge distributions depend strongly on both the identities and relative positions of the halogen substituents. In this report, we examine how the placement and identities of the halogen substituents affect the natural population analysis, atoms in molecules, and Gasteiger partial atomic charge distributions in representative saturated (methanes and ethanes), unsaturated (ethylenes), and aromatic (benzenes) halogenated hydrocarbons, using density functional calculations. The results are interpreted in terms of the electronegativities and the charge capacities of the halogens. The relationships of these charge distributions to the electrostatic potential maps of the compounds are also explored.

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“…[84] For the two reactions in Scheme 17, two reactions are given without the corresponding reaction time, because the original publication only reported "monitored by TLC". In very few cases, the collection as precipitates from the reaction mixture was reported, which is the usual method for the isolation of squaraine dyes.…”

Section: Synthesismentioning

confidence: 99%

Croconaine Dyes – the Lesser Known Siblings of Squaraines

Lynch

Hamilton

2017

Eur J Org Chem

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In the 50+ years since the first squaraine dye, made by using 3,4‐dihydroxycyclobut‐3‐ene‐1,2‐dione (squaric acid), was reported, numerous analogues have not only been made and published, but similarly, a number of good review articles on squaraine dyes has also been published. In contrast, for the majority of this time, reports of croconaine dyes, made by using 4,5‐dihydroxy‐4‐cyclopentene‐1,2,3‐trione (croconic acid), have also been published, but they have not received the same attention as squaraine dyes. Furthermore, the only known review of croconaine dyes, which was recently published, is written in Chinese. This current review aims to document the history of croconaine dyes since their first report in a German patent in 1970. This review sources both the scientific literature and published patents, and attention is given to both synthesis and spectroscopic details.

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Correlations between quantum chemical indices and the pK_as of a diverse set of organic phenols

Cited by 20 publications

References 16 publications

Predicting pK a values from EEM atomic charges

Predicting pK a values from EEM atomic charges

Charge competition in halogenated hydrocarbons

Croconaine Dyes – the Lesser Known Siblings of Squaraines

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Correlations between quantum chemical indices and the pKas of a diverse set of organic phenols

Cited by 20 publications

References 16 publications

Predicting pK a values from EEM atomic charges

Predicting pK a values from EEM atomic charges

Charge competition in halogenated hydrocarbons

Croconaine Dyes – the Lesser Known Siblings of Squaraines

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Correlations between quantum chemical indices and the pK_as of a diverse set of organic phenols