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. Journal of Cheminformatics. London: BIOMED CENTRAL LTD, 2013, vol. 5, No 18, p. "nestránkováno", 15 pp. ISSN 1758-2946. doi:10.1186/1758-2946-5-18.
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Basic information
Original name Predicting pKa values from EEM atomic charges
Authors SVOBODOVÁ VAŘEKOVÁ, Radka (203 Czech Republic, belonging to the institution), Stanislav GEIDL (203 Czech Republic, belonging to the institution), Crina-Maria IONESCU (642 Romania, belonging to the institution), Ondřej SKŘEHOTA (203 Czech Republic, belonging to the institution), Tomáš BOUCHAL (203 Czech Republic, belonging to the institution), David SEHNAL (203 Czech Republic, belonging to the institution), Ruben A. ABAGYAN (840 United States of America) and Jaroslav KOČA (203 Czech Republic, guarantor, belonging to the institution).
Edition Journal of Cheminformatics, London, BIOMED CENTRAL LTD, 2013, 1758-2946.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10600 1.6 Biological sciences
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 4.540
RIV identification code RIV/00216224:14310/13:00068473
Organization unit Faculty of Science
UT WoS 000319440100001
Keywords in English Dissociation constant; Quantitative structure-property relationship; QSPR; Partial atomic charges; Electronegativity equalization method; EEM; Quantum mechanics; QM
Tags AKR, ok, rivok
Tags International impact, Reviewed
Changed by Changed by: RNDr. Stanislav Geidl, Ph.D., učo 327887. Changed: 27. 7. 2014 18:59.
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.
ED1.1.00/02.0068, research and development projectName: CEITEC - central european institute of technology
LH13055, research and development projectName: Multidisciplinární přístup k návrhu léčiv - Inhibice proteinů s návazností na cukry (Acronym: MADICA)
Investor: Ministry of Education, Youth and Sports of the CR
MUNI/A/0760/2012, interní kód MUName: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace II. (Acronym: FI MAV II.)
Investor: Masaryk University, Category A
286154, interní kód MUName: SYLICA - Synergies of Life and Material Sciences to Create a New Future (Acronym: SYLICA)
Investor: European Union, Capacities
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