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J Comput Aided Mol Des. 2018 Oct;32(10):1191-1201. doi: 10.1007/s10822-018-0167-1. Epub 2018 Oct 1.

An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge.

Author information

1
Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA. samar.samarjeet@nih.gov.
2
Biophysics and Biophysical Chemistry, The Johns Hopkins University, School of Medicine, Baltimore, MD, 21205, USA. samar.samarjeet@nih.gov.
3
School of Life Sciences, Westlake University, 18 Shilongshan Street, Xihu District, Hangzhou, Zhejiang, China.
4
Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20814, USA.

Abstract

In this work we have developed a hybrid QM and MM approach to predict pKa of small drug-like molecules in explicit solvent. The gas phase free energy of deprotonation is calculated using the M06-2X density functional theory level with Pople basis sets. The solvation free energy difference of the acid and its conjugate base is calculated at MD level using thermodynamic integration. We applied this method to the 24 drug-like molecules in the SAMPL6 blind pKa prediction challenge. We achieved an overall RMSE of 2.4 pKa units in our prediction. Our results show that further optimization of the protocol needs to be done before this method can be used as an alternative approach to the well established approaches of a full quantum level or empirical pKa prediction methods.

KEYWORDS:

Explicit solvent; Hybrid QM and MM; SAMPL6; pKa prediction

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