title: Predicting pK(a) values of substituted phenols from atomic charges: comparison of different quantum mechanical methods and charge distribution schemes.
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.
authors.affiliation: National Centre for Biomolecular Research, Faculty of Science and CEITEC - Central European Institute of Technology, Masaryk University Brno, Brno-Bohunice, Czech Republic.
authors.forename: Radka
authors.initials: R
authors.lastname: Svobodová Vareková
authors.forename: Stanislav
authors.initials: S
authors.lastname: Geidl
authors.forename: Crina-Maria
authors.initials: CM
authors.lastname: Ionescu
authors.forename: Ondrej
authors.initials: O
authors.lastname: Skrehota
authors.forename: Michal
authors.initials: M
authors.lastname: Kudera
authors.forename: David
authors.initials: D
authors.lastname: Sehnal
authors.forename: Tomás
authors.initials: T
authors.lastname: Bouchal
authors.forename: Ruben
authors.initials: R
authors.lastname: Abagyan
authors.forename: Heinrich J
authors.initials: HJ
authors.lastname: Huber
authors.forename: Jaroslav
authors.initials: J
authors.lastname: Koca
mesh_terms: D002626:Chemistry, Pharmaceutical / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D007700:Kinetics; D008956:Models, Chemical; D015233:Models, Statistical; D008968:Molecular Conformation; D004364:Pharmaceutical Preparations / Q000032:analysis* / Q000737:chemistry; D010636:Phenols / Q000032:analysis* / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D011789:Quantum Theory; D055672:Static Electricity
chemical_list: D004364:Pharmaceutical Preparations; D010636:Phenols