Predicting pKa values from EEM atomic charges

J 2013

Predicting pKa values from EEM atomic charges

SVOBODOVÁ VAŘEKOVÁ, Radka; Stanislav GEIDL; Crina-Maria IONESCU; Ondřej SKŘEHOTA; Tomáš BOUCHAL et al.

Základní údaje

Originální název

Predicting pKa values from EEM atomic charges

Autoři

SVOBODOVÁ VAŘEKOVÁ, Radka; Stanislav GEIDL; Crina-Maria IONESCU; Ondřej SKŘEHOTA; Tomáš BOUCHAL; David SEHNAL; Ruben A. ABAGYAN a Jaroslav KOČA

Vydání

Journal of Cheminformatics, London, BIOMED CENTRAL LTD, 2013, 1758-2946

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10600 1.6 Biological sciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.540

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/13:00068473

Organizační jednotka

Přírodovědecká fakulta

DOI

https://doi.org/10.1186/1758-2946-5-18

UT WoS

000319440100001

Klíčová slova anglicky

Dissociation constant; Quantitative structure-property relationship; QSPR; Partial atomic charges; Electronegativity equalization method; EEM; Quantum mechanics; QM

Štítky

AKR, ok, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 27. 7. 2014 18:59, RNDr. Stanislav Geidl, Ph.D.

Anotace

V originále

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.

Návaznosti

ED1.1.00/02.0068, projekt VaV

Název: CEITEC - central european institute of technology

LH13055, projekt VaV

Název: Multidisciplinární přístup k návrhu léčiv - Inhibice proteinů s návazností na cukry (Akronym: MADICA)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Multidisciplinární přístup k návrhu léčiv - Inhibice proteinů s návazností na sacharidy

MUNI/A/0760/2012, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace II. (Akronym: FI MAV II.)

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace II., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

286154, interní kód MU

Název: SYLICA - Synergies of Life and Material Sciences to Create a New Future (Akronym: SYLICA)

Investor: Evropská unie, SYLICA - Synergies of Life and Material Sciences to Create a New Future, Kapacity

Přiložené soubory

J_Chemoinf_2013_EEM_pKa.pdf

Požádat o autorskou verzi souboru

Citovat

SVOBODOVÁ VAŘEKOVÁ, Radka; Stanislav GEIDL; Crina-Maria IONESCU; Ondřej SKŘEHOTA; Tomáš BOUCHAL; David SEHNAL; Ruben A. ABAGYAN a Jaroslav KOČA. Predicting pKa values from EEM atomic charges. Journal of Cheminformatics. London: BIOMED CENTRAL LTD, 2013, roč. 5, č. 18, s. "nestránkováno", 15 s. ISSN 1758-2946. Dostupné z: https://doi.org/10.1186/1758-2946-5-18.

@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 = {https://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}
}

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AU  - Svobodová Vařeková, Radka - Geidl, Stanislav - Ionescu, Crina-Maria - Skřehota, Ondřej - Bouchal, Tomáš - Sehnal, David - Abagyan, Ruben A. - Koča, Jaroslav
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VL  - 5
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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 a Jaroslav KOČA. Predicting pKa values from EEM atomic charges. \textit{Journal of Cheminformatics}. London: BIOMED CENTRAL LTD, 2013, roč.~5, č.~18, s.~''nestránkováno'', 15 s. ISSN~1758-2946. Dostupné z: https://doi.org/10.1186/1758-2946-5-18.

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