Prediction of pKa values using the PM6 semiempirical method

Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
DOI
10.7287/peerj.preprints.2075v1
Subject Areas
Biophysics, Computational Biology
Keywords
electronic structure, pKa prediction, semiempirical methods, drug design
Copyright
© 2016 Kromann et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Kromann JC, Larsen F, Moustafa H, Jensen JH. 2016. Prediction of pKa values using the PM6 semiempirical method. PeerJ Preprints 4:e2075v1

Abstract

The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences of 0.6 - 0.7 pH units. For carboxylic acids the accuracy (0.7 - 1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5 - 0.6), slightly worse (0.5 - 1.0), and worse (1.0 - 2.5), provided that di- and triethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 minutes per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

Author Comment

This is a preprint submission to PeerJ Preprints.