Literature DB >> 21761919

Predicting pK(a) values of substituted phenols from atomic charges: comparison of different quantum mechanical methods and charge distribution schemes.

Radka Svobodová Vareková1, Stanislav Geidl, Crina-Maria Ionescu, Ondrej Skrehota, Michal Kudera, David Sehnal, Tomás Bouchal, Ruben Abagyan, Heinrich J Huber, Jaroslav Koca.   

Abstract

The acid dissociation (ionization) constant pK(a) is one of the fundamental properties of organic molecules. We have evaluated different computational strategies and models to predict the pK(a) values of substituted phenols using partial atomic charges. Partial atomic charges for 124 phenol molecules were calculated using 83 approaches containing seven theory levels (MP2, HF, B3LYP, BLYP, BP86, AM1, and PM3), three basis sets (6-31G*, 6-311G, STO-3G), and five population analyses (MPA, NPA, Hirshfeld, MK, and Löwdin). The correlations between pK(a) and various atomic charge descriptors were examined, and the best descriptors were selected for preparing the quantitative structure-property relationship (QSPR) models. One QSPR model was created for each of the 83 approaches to charge calculation, and then the accuracy of all these models was analyzed and compared. The pK(a)s predicted by most of the models correlate strongly with experimental pK(a) values. For example, more than 25% of the models have correlation coefficients (R²) greater than 0.95 and root-mean-square errors smaller than 0.49. All seven examined theory levels are applicable for pK(a) prediction from charges. The best results were obtained for the MP2 and HF level of theory. The most suitable basis set was found to be 6-31G*. The 6-311G basis set provided slightly weaker correlations, and unexpectedly also, the STO-3G basis set is applicable for the QSPR modeling of pK(a). The Mulliken, natural, and Löwdin population analyses provide accurate models for all tested theory levels and basis sets. The results provided by the Hirshfeld population analysis were also acceptable, but the QSPR models based on MK charges show only weak correlations.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21761919     DOI: 10.1021/ci200133w

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  13 in total

1.  Quantum Mechanics Approaches to Drug Research in the Era of Structural Chemogenomics.

Authors:  Andrey V Ilatovskiy; Ruben Abagyan; Irina Kufareva
Journal:  Int J Quantum Chem       Date:  2013-06-15       Impact factor: 2.444

2.  A single theoretical descriptor for the bond-dissociation energy of substituted phenols.

Authors:  Carolina Aliaga; Iriux Almodovar; Marcos Caroli Rezende
Journal:  J Mol Model       Date:  2015-01-24       Impact factor: 1.810

3.  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

4.  Systems analysis of cancer cell heterogeneity in caspase-dependent apoptosis subsequent to mitochondrial outer membrane permeabilization.

Authors:  Jasmin Schmid; Heiko Dussmann; Gerhardt J Boukes; Lorna Flanagan; Andreas U Lindner; Carla L O'Connor; Markus Rehm; Jochen H M Prehn; Heinrich J Huber
Journal:  J Biol Chem       Date:  2012-10-04       Impact factor: 5.157

5.  High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling.

Authors:  Cory L Strope; Kamel Mansouri; Harvey J Clewell; James R Rabinowitz; Caroline Stevens; John F Wambaugh
Journal:  Sci Total Environ       Date:  2017-09-29       Impact factor: 7.963

6.  A NON-LINEAR STRUCTURE-PROPERTY MODEL FOR OCTANOL-WATER PARTITION COEFFICIENT.

Authors:  Krishna M Yerramsetty; Brian J Neely; Khaled A M Gasem
Journal:  Fluid Phase Equilib       Date:  2012-07-09       Impact factor: 2.775

7.  General analytical procedure for determination of acidity parameters of weak acids and bases.

Authors:  Bogusław Pilarski; Roman Kaliszan; Dariusz Wyrzykowski; Janusz Młodzianowski; Agata Balińska
Journal:  J Anal Methods Chem       Date:  2015-01-26       Impact factor: 2.193

8.  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

9.  High-quality and universal empirical atomic charges for chemoinformatics applications.

Authors:  Stanislav Geidl; Tomáš Bouchal; Tomáš Raček; Radka Svobodová Vařeková; Václav Hejret; Aleš Křenek; Ruben Abagyan; Jaroslav Koča
Journal:  J Cheminform       Date:  2015-12-02       Impact factor: 5.514

10.  AtomicChargeCalculator: interactive web-based calculation of atomic charges in large biomolecular complexes and drug-like molecules.

Authors:  Crina-Maria Ionescu; David Sehnal; Francesco L Falginella; Purbaj Pant; Lukáš Pravda; Tomáš Bouchal; Radka Svobodová Vařeková; Stanislav Geidl; Jaroslav Koča
Journal:  J Cheminform       Date:  2015-10-22       Impact factor: 5.514

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.