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Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge
Qiao Zeng1, Michael R Jones2, Bernard R Brooks2
1Laboratory 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.
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.
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