Skip to main content
SpringerLink
Log in

The SAMPL6 challenge on predicting aqueous pKa values from EC-RISM theory

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The “embedded cluster reference interaction site model” (EC-RISM) integral equation theory is applied to the problem of predicting aqueous pKa values for drug-like molecules based on an ensemble of tautomers. EC-RISM is based on self-consistent calculations of a solute’s electronic structure and the distribution function of surrounding water. Following-up on the workflow developed after the SAMPL5 challenge on cyclohexane-water distribution coefficients we extended and improved the methodology by taking into account exact electrostatic solute–solvent interactions taken from the wave function in solution. As before, the model is calibrated against Gibbs energies of hydration from the “Minnesota Solvation Database” and a public dataset of acidity constants of organic acids and bases by adjusting in total 4 parameters, among which only 3 are relevant for predicting pKa values. While the best-performing training model yields a root-mean-square error (RMSE) of 1 pK unit, the corresponding test set prediction on the full SAMPL6 dataset of macroscopic pKa values using the same level of theory exhibits slightly larger error (1.7 pK units) than the best test set model submitted (1.7 pK units for corresponding training set vs. test set performance of 1.6). Post-submission analysis revealed a number of physical optimization options regarding the numerical treatment of electrostatic interactions and conformational sampling. While the experimental test set data revealed after submission was not used for reparametrizing the methodology, the best physically optimized models consequentially result in RMSEs of 1.5 if only improved electrostatic interactions are considered and of 1.1 if, in addition, conformational sampling accounts for quantum-chemically derived rankings. We conclude that these numbers are probably near the ultimate accuracy achievable with the simple 3-parameter model using a single or the two best-ranking conformations per tautomer or microstate. Finally, relations of the present macrostate approach to microstate pKa results are discussed and some illustrative results for microstate populations are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. http://www.drugdesigndata.org/about/sampl6. Accessed 29 May 2018

  2. Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput-Aid Mol Des 24:259–279

    Article  CAS  Google Scholar 

  3. Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) J Comput-Aid Mol Des 30:927–944

    Article  CAS  Google Scholar 

  4. Kast SM, Heil J, Güssregen S, Schmidt KF (2010) J Comput-Aid Mol Des 24:343–353

    Article  CAS  Google Scholar 

  5. Tielker N, Tomazic D, Heil J, Kloss T, Ehrhart S, Güssregen S, Schmidt KF, Kast SM (2016) J Comput-Aid Mol Des 30:1035–1044

    Article  CAS  Google Scholar 

  6. Kloss T, Heil J, Kast SM (2008) J Phys Chem B 112:4337–4343

    Article  CAS  Google Scholar 

  7. Beglov D, Roux B (1997) J Phys Chem 101:7821–7826

    Article  CAS  Google Scholar 

  8. Kovalenko A, Hirata F (1998) Chem Phys Lett 290:237–244

    Article  CAS  Google Scholar 

  9. Sato H (2013) Phys Chem Chem Phys 15:7450–7465

    Article  CAS  Google Scholar 

  10. Kast SM, Kloss T (2008) J Chem Phys 129:236101

    Article  Google Scholar 

  11. Heil J, Kast SM (2015) J Chem Phys 142:114107

    Article  Google Scholar 

  12. Heil J, Tomazic D, Egbers S, Kast SM (2014) J Mol Model 20:2161

    Article  Google Scholar 

  13. Frach R, Kast SM (2014) J Phys Chem A 118:11620–11628

    Article  CAS  Google Scholar 

  14. Hoffgaard F, Heil J, Kast SM (2013) J Chem Theory Comput 9:4718–4726

    Article  CAS  Google Scholar 

  15. Frach R, Kibies P, Böttcher S, Pongratz T, Strohfeldt S, Kurrmann S, Koehler J, Hofmann M, Kremer W, Kalbitzer HR, Reiser O, Horinek D, Kast SM (2016) Angew Chem Int Ed 55:8757–8760

    Article  CAS  Google Scholar 

  16. Frach R, Heil J, Kast SM (2016) Mol Phys 114:2461–2476

    Article  CAS  Google Scholar 

  17. Hölzl C, Kibies P, Imoto S, Frach R, Suladze S, Winter R, Marx D, Horinek D, Kast SM (2016) J Chem Phys 144:144104

    Article  Google Scholar 

  18. Imoto S, Kibies P, Rosin C, Winter R, Kast SM, Marx D (2016) Angew Chem Int Ed 55:9534–9538

    Article  CAS  Google Scholar 

  19. Ratkova EL, Palmer DS, Fedorov MV (2015) Chem Rev 115:6312–6356

    Article  CAS  Google Scholar 

  20. Sergiievskyi V, Jeanmairet G, Levesque M, Borgis D (2015) J Chem Phys 143:184116

    Article  Google Scholar 

  21. Misin M, Fedorov MV, Palmer DS (2016) J Phys Chem B 120:975–983

    Article  CAS  Google Scholar 

  22. Klicić JJ, Friesner RA, Liu SY, Guida WC (2002) J Phys Chem A 106:1327–1335

    Article  Google Scholar 

  23. Klamt A. Eckert F, Diedenhofen M, Beck ME (2003) J Phys Chem A 107:9380–9386

    Article  CAS  Google Scholar 

  24. Eckert F, Klamt A (2005) J Comput Chem 27:11–19

    Article  Google Scholar 

  25. Marenich AV, Kelly CP, Thompson JD, Hawkins GD, Chambers CC, Giesen DK, Winget P, Cramer CJ, Truhlar DG (2012) Minnesoate solvation database—version 2012. University of Minnesota, Minneapolis

    Google Scholar 

  26. Kelly CP, Cramer CJ, Truhlar DG (2005) J Chem Theory Comput 1:1133–1152

    Article  CAS  Google Scholar 

  27. Marenich AV, Olson RM, Kelly CP, Cramer CJ, Truhlar DG (2007) J Chem Theory Comput 3:2011–2033

    Article  CAS  Google Scholar 

  28. Marenich AV, Cramer CJ, Truhlar DG (2009) J Phys Chem B 113:6378–6396

    Article  CAS  Google Scholar 

  29. Heil J (2016) PhD dissertation. https://eldorado.tu-dortmund.de/handle/2003/35930

  30. Imai T, Kinoshita M, Hirata F (2000) J Chem Phys 112:9469–9478

    Article  CAS  Google Scholar 

  31. Imai T (2007) Cond Matter Phys 10:343–361

    Article  Google Scholar 

  32. Frisch MJ et al (2009) Gaussian 09, Rev E.01. Gaussian, Inc., Wallingford

    Google Scholar 

  33. Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT, OMEGA 2.6.7: OpenEye Scientific Software, Santa Fe

  34. 3D Structure Generator CORINA Classic, version 4.1.0, Molecular Networks GmbH, Nuremberg, Germany

  35. Small-Molecule Drug Discovery Suite 2017-2 (2017) Schrödinger, LLC, New York

  36. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25:1157–1174

    Article  CAS  Google Scholar 

  37. Chirlian LE, Francl MM (1987) J Comput Chem 8:894–905

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Cluster of Excellence RESOLV (EXC 1069) and the Research Unit FOR 1979, funded by the Deutsche Forschungsgemeinschaft. We also thank the IT and Media Center (ITMC) of the TU Dortmund for computational support.

Author information

Authors and Affiliations

  1. Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany

    Nicolas Tielker, Lukas Eberlein & Stefan M. Kast

  2. Sanofi-Aventis Deutschland GmbH, R&D Integrated Drug Discovery, 65926, Frankfurt am Main, Germany

    Stefan Güssregen

Authors
  1. Nicolas Tielker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lukas Eberlein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Güssregen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefan M. Kast
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan M. Kast.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (ZIP 2285 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tielker, N., Eberlein, L., Güssregen, S. et al. The SAMPL6 challenge on predicting aqueous pKa values from EC-RISM theory. J Comput Aided Mol Des 32, 1151–1163 (2018). https://doi.org/10.1007/s10822-018-0140-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-018-0140-z

Keywords

Search

Navigation