Journal
JOURNAL OF CHEMINFORMATICS
Volume 5, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1758-2946-5-18
Keywords
Dissociation constant; Quantitative structure-property relationship; QSPR; Partial atomic charges; Electronegativity equalization method; EEM; Quantum mechanics; QM
Categories
Funding
- Ministry of Education of the Czech Republic [LH13055]
- European Regional Development Fund [CZ.1.05/1.1.00/02.0068]
- EU Seventh Framework Programme under the Capacities specific programme [286154]
- Brno City Municipality
- NIH [R01 GM071872, U01 GM094612, U54 GM094618]
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The acid dissociation constant pK(a) is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for pK(a) prediction. We have evaluated the pK(a) prediction 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 pK(a) (63% of these models had R-2 > 0.9, while the best had R-2 = 0.924). As expected, QM QSPR models provided more accurate pK(a) predictions 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 pK(a) prediction approach that can be used in virtual screening.
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