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J Comput Aided Mol Des. 2018 Oct;32(10):1179-1189. doi: 10.1007/s10822-018-0150-x. Epub 2018 Aug 20.

Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge.

Author information

1
Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, 12 South Drive, Building 12A Room 3053, Bethesda, MD, 20814, USA. qiao.zeng@nih.gov.
2
Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, 12 South Drive, Building 12A Room 3053, Bethesda, MD, 20814, USA.

Abstract

In this work, quantum mechanical methods were used to predict the microscopic and macroscopic pKa values for a set of 24 molecules as a part of the SAMPL6 blind challenge. The SMD solvation model was employed with M06-2X and different basis sets to evaluate three pKa calculation schemes (direct, vertical, and adiabatic). The adiabatic scheme is the most accurate approach (RMSE = 1.40 pKa units) and has high correlation (R2 = 0.93), with respect to experiment. This approach can be improved by applying a linear correction to yield an RMSE of 0.73 pKa units. Additionally, we consider including explicit solvent representation and multiple lower-energy conformations to improve the predictions for outliers. Adding three water molecules explicitly can reduce the error by 2-4 pKa units, with respect to experiment, whereas including multiple local minima conformations does not necessarily improve the pKa prediction.

KEYWORDS:

Implicit solvent; Quantum chemistry; SAMPL6; pK a

PMID:
30128926
DOI:
10.1007/s10822-018-0150-x

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