Literature DB >> 10903100

Estimating the pKa of phenols, carboxylic acids and alcohols from semi-empirical quantum chemical methods

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Abstract

Quantitative structure property relationships (QSPR) for the pKa of phenols, carboxylic acids and alcohols were developed from descriptors derived from semi-empirical molecular orbital theory quantum chemical calculations. A training set of compounds were used to refine the models and a validation set of appropriate chemicals were chosen to test the models. Correlation coefficients for the estimated versus observed pKa values were 0.96 for phenols, 0.84 for non-aromatic carboxylic acids, 0.89 for benzoic acids and 0.89 for alcohols. The results obtained by the quantum chemical method are compared to results obtained from linear free energy relationships (LFER) and the merits of each approach are discussed.

Entities:  

Year:  1999        PMID: 10903100     DOI: 10.1016/s0045-6535(98)00172-6

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  11 in total

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Journal:  Pharm Res       Date:  2005-08-24       Impact factor: 4.200

2.  Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR.

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Journal:  J Mol Model       Date:  2005-12-13       Impact factor: 1.810

3.  Accurate gas phase acidities of carboxylic acids estimated by scaling the vibrational contribution of ab initio Gibbs free energies.

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Journal:  J Mol Model       Date:  2007-04-03       Impact factor: 1.810

4.  Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review.

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Journal:  Crit Rev Environ Sci Technol       Date:  2015-06-18       Impact factor: 12.561

5.  Correlation between molecular acidity (pKa) and vibrational spectroscopy.

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Journal:  J Mol Model       Date:  2019-01-30       Impact factor: 1.810

6.  SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model.

Authors:  Caitlin C Bannan; David L Mobley; A Geoffrey Skillman
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7.  How Does the Methodology of 3D Structure Preparation Influence the Quality of pKa Prediction?

Authors:  Stanislav Geidl; Radka Svobodová Vařeková; Veronika Bendová; Lukáš Petrusek; Crina-Maria Ionescu; Zdeněk Jurka; Ruben Abagyan; Jaroslav Koča
Journal:  J Chem Inf Model       Date:  2015-06-11       Impact factor: 4.956

8.  Characterizing Protein Protonation Microstates Using Monte Carlo Sampling.

Authors:  Umesh Khaniya; Junjun Mao; Rongmei Judy Wei; M R Gunner
Journal:  J Phys Chem B       Date:  2022-03-28       Impact factor: 2.991

9.  Comparison of logP and logD correction models trained with public and proprietary data sets.

Authors:  Ignacio Aliagas; Alberto Gobbi; Man-Ling Lee; Benjamin D Sellers
Journal:  J Comput Aided Mol Des       Date:  2022-04-01       Impact factor: 3.686

10.  Predicting p Ka values from EEM atomic charges.

Authors:  Radka Svobodová Vařeková; Stanislav Geidl; Crina-Maria Ionescu; Ondřej Skřehota; Tomáš Bouchal; David Sehnal; Ruben Abagyan; Jaroslav Koča
Journal:  J Cheminform       Date:  2013-04-10       Impact factor: 5.514

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