Journal of Computer-Aided Molecular Design

, Volume 30, Issue 11, pp 1035–1044 | Cite as

The SAMPL5 challenge for embedded-cluster integral equation theory: solvation free energies, aqueous pK a, and cyclohexane–water log D

  • Nicolas Tielker
  • Daniel Tomazic
  • Jochen Heil
  • Thomas Kloss
  • Sebastian Ehrhart
  • Stefan Güssregen
  • K. Friedemann Schmidt
  • Stefan M. KastEmail author


We predict cyclohexane–water distribution coefficients (log D 7.4) for drug-like molecules taken from the SAMPL5 blind prediction challenge by the “embedded cluster reference interaction site model” (EC-RISM) integral equation theory. This task involves the coupled problem of predicting both partition coefficients (log P) of neutral species between the solvents and aqueous acidity constants (pK a) in order to account for a change of protonation states. The first issue is addressed by calibrating an EC-RISM-based model for solvation free energies derived from the “Minnesota Solvation Database” (MNSOL) for both water and cyclohexane utilizing a correction based on the partial molar volume, yielding a root mean square error (RMSE) of 2.4 kcal mol−1 for water and 0.8–0.9 kcal mol−1 for cyclohexane depending on the parametrization. The second one is treated by employing on one hand an empirical pK a model (MoKa) and, on the other hand, an EC-RISM-derived regression of published acidity constants (RMSE of 1.5 for a single model covering acids and bases). In total, at most 8 adjustable parameters are necessary (2–3 for each solvent and two for the pK a) for training solvation and acidity models. Applying the final models to the log D 7.4 dataset corresponds to evaluating an independent test set comprising other, composite observables, yielding, for different cyclohexane parametrizations, 2.0–2.1 for the RMSE with the first and 2.2–2.8 with the combined first and second SAMPL5 data set batches. Notably, a pure log P model (assuming neutral species only) performs statistically similarly for these particular compounds. The nature of the approximations and possible perspectives for future developments are discussed.


Solvation model Quantum chemistry Integral equation theory EC-RISM Distribution coefficients 



We thank the Deutsche Forschungsgemeinschaft (DFG, Grant No. KA 1381/5-1) as well as the Bundesministerium für Bildung und Forschung (BMBF, Grant No. 01IH11002B) for their financial, and the IT and Media Center (ITMC) of the TU Dortmund for computational support.

Supplementary material

10822_2016_9939_MOESM1_ESM.pdf (412 kb)
Supplementary material 1 (PDF 412 kb) (487 kb)
Supplementary material 2 (ZIP 487 kb) (98 kb)
Supplementary material 3 (ZIP 97 kb) (89 kb)
Supplementary material 4 (ZIP 88 kb)


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nicolas Tielker
    • 1
  • Daniel Tomazic
    • 1
  • Jochen Heil
    • 1
  • Thomas Kloss
    • 2
  • Sebastian Ehrhart
    • 3
  • Stefan Güssregen
    • 4
  • K. Friedemann Schmidt
    • 4
  • Stefan M. Kast
    • 1
    Email author
  1. 1.Physikalische Chemie IIITechnische Universität DortmundDortmundGermany
  2. 2.IPhT, L’Orme des MerisiersCEA-SaclayGif-sur-YvetteFrance
  3. 3.CERNGenevaSwitzerland
  4. 4.Sanofi-Aventis Deutschland GmbHIndustriepark HöchstFrankfurt am MainGermany

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