Other formats:
BibTeX
LaTeX
RIS
@article{1110036, author = {Svobodová Vařeková, Radka and Geidl, Stanislav and Ionescu, CrinaandMaria and Skřehota, Ondřej and Bouchal, Tomáš and Sehnal, David and Abagyan, Ruben A. and Koča, Jaroslav}, article_location = {London}, article_number = {18}, doi = {http://dx.doi.org/10.1186/1758-2946-5-18}, keywords = {Dissociation constant; Quantitative structure-property relationship; QSPR; Partial atomic charges; Electronegativity equalization method; EEM; Quantum mechanics; QM}, language = {eng}, issn = {1758-2946}, journal = {Journal of Cheminformatics}, title = {Predicting pKa values from EEM atomic charges}, url = {http://www.jcheminf.com/content/5/1/18}, volume = {5}, year = {2013} }
TY - JOUR ID - 1110036 AU - Svobodová Vařeková, Radka - Geidl, Stanislav - Ionescu, Crina-Maria - Skřehota, Ondřej - Bouchal, Tomáš - Sehnal, David - Abagyan, Ruben A. - Koča, Jaroslav PY - 2013 TI - Predicting pKa values from EEM atomic charges JF - Journal of Cheminformatics VL - 5 IS - 18 SP - "nestránkováno" EP - "nestránkováno" PB - BIOMED CENTRAL LTD SN - 17582946 KW - Dissociation constant KW - Quantitative structure-property relationship KW - QSPR KW - Partial atomic charges KW - Electronegativity equalization method KW - EEM KW - Quantum mechanics KW - QM UR - http://www.jcheminf.com/content/5/1/18 L2 - http://www.jcheminf.com/content/5/1/18 N2 - The acid dissociation constant pKa is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for pKa prediction. We have evaluated the pKa 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 pKa (63% of these models had R2 > 0.9, while the best had R2 = 0.924). As expected, QM QSPR models provided more accurate pKa 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 pKa prediction approach that can be used in virtual screening. ER -
SVOBODOVÁ VAŘEKOVÁ, Radka, Stanislav GEIDL, Crina-Maria IONESCU, Ondřej SKŘEHOTA, Tomáš BOUCHAL, David SEHNAL, Ruben A. ABAGYAN and Jaroslav KOČA. Predicting pKa values from EEM atomic charges. \textit{Journal of Cheminformatics}. London: BIOMED CENTRAL LTD, vol.~5, No~18, p.~''nestránkováno'', 15 pp. ISSN~1758-2946. doi:10.1186/1758-2946-5-18. 2013.
|