SAMPL6: calculation of macroscopic pK a values from ab initio quantum mechanical free energies

Edithe Selwa, Ian M. Kenney, Oliver Beckstein, Bogdan I. Iorga

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Macroscopic pKa values were calculated for all compounds in the SAMPL6 blind prediction challenge, based on quantum chemical calculations with a continuum solvation model and a linear correction derived from a small training set. Microscopic pKa values were derived from the gas-phase free energy difference between protonated and deprotonated forms together with the Conductor-like Polarizable Continuum Solvation Model and the experimental solvation free energy of the proton. pH-dependent microstate free energies were obtained from the microscopic pKas with a maximum likelihood estimator and appropriately summed to yield macroscopic pKa values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pKas: direct use of the quantum mechanical free energy differences and correction of the direct values for short-comings in the QM solvation model with two different linear models that we independently derived from a small training set of 38 compounds with known pKa. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pKa units] than the direct calculation (RMSE 3.74). Statistical measures indicate that some systematic errors remain, likely due to differences in the SAMPL6 data set and the small training set with respect to their interactions with water. Overall, the current approach provides a viable physics-based route to estimate macroscopic pKa values for novel compounds with reasonable accuracy.

Original languageEnglish (US)
Pages (from-to)1203-1216
Number of pages14
JournalJournal of Computer-Aided Molecular Design
Issue number10
StatePublished - Oct 1 2018


  • Quantum chemistry
  • SAMPL challenge
  • pH
  • pK

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry


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