Journal of Computer-Aided Molecular Design

, Volume 30, Issue 11, pp 945–958 | Cite as

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge

  • Ariën S. Rustenburg
  • Justin Dancer
  • Baiwei Lin
  • Jianwen A. Feng
  • Daniel F. Ortwine
  • David L. Mobley
  • John D. ChoderaEmail author


Small molecule distribution coefficients between immiscible nonaqueuous and aqueous phases—such as cyclohexane and water—measure the degree to which small molecules prefer one phase over another at a given pH. As distribution coefficients capture both thermodynamic effects (the free energy of transfer between phases) and chemical effects (protonation state and tautomer effects in aqueous solution), they provide an exacting test of the thermodynamic and chemical accuracy of physical models without the long correlation times inherent to the prediction of more complex properties of relevance to drug discovery, such as protein-ligand binding affinities. For the SAMPL5 challenge, we carried out a blind prediction exercise in which participants were tasked with the prediction of distribution coefficients to assess its potential as a new route for the evaluation and systematic improvement of predictive physical models. These measurements are typically performed for octanol-water, but we opted to utilize cyclohexane for the nonpolar phase. Cyclohexane was suggested to avoid issues with the high water content and persistent heterogeneous structure of water-saturated octanol phases, since it has greatly reduced water content and a homogeneous liquid structure. Using a modified shake-flask LC-MS/MS protocol, we collected cyclohexane/water distribution coefficients for a set of 53 druglike compounds at pH 7.4. These measurements were used as the basis for the SAMPL5 Distribution Coefficient Challenge, where 18 research groups predicted these measurements before the experimental values reported here were released. In this work, we describe the experimental protocol we utilized for measurement of cyclohexane-water distribution coefficients, report the measured data, propose a new bootstrap-based data analysis procedure to incorporate multiple sources of experimental error, and provide insights to help guide future iterations of this valuable exercise in predictive modeling.


Partition coefficients Distribution coefficients Blind challenge Predictive modeling SAMPL 



Statistical assessment of the modeling of proteins and ligands

log P

\(\log _{10}\) partition coefficient

log D

\(\log _{10}\) distribution coefficient


Liquid chromatography - tandem mass spectrometry


High-pressure liquid chromatography


Multiple reaction monitoring




Dimethyl sulfoxide


Phosphate buffered saline


Revolutions per minute


Coefficient of variation


Maximum a posteriori


Markov chain Monte Carlo



The authors acknowledge Christopher Bayly (OpenEye Scientific) and Robert Abel (Schrödinger) for their contributions to discussions on compound selection; Joseph Pease (Genentech) for discussions of the experimental approach and aid in compound selection; Delia Li (Genentech) for her assistance in performing experimental work; Alberto Gobbi (Genentech), Man-Ling Lee (Genentech), and Ignacio Aliagas (Genentech) for helpful feedback on experimental issues; Andreas Klamt (Cosmologic) and Jens Reinisch (Cosmologic) for invigorating discussions regarding experimental data; Patrick Grinaway (MSKCC) for helpful discussions on analysis procedures; and Anthony Nicholls (OpenEye) for originating and supporting earlier iterations of SAMPL challenges.


This work was performed as part of an internship by ASR sponsored by Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States. JDC acknowledges support from the Sloan Kettering Institute and NIH grant P30 CA008748. DLM appreciates financial support from National Science Foundation (CHE 1352608).

Compliance with ethical standard

Conflict of interest statement

D. L. M. and J. D. C. are members of the Scientific Advisory Board for Schrödinger, LLC.

Supplementary material

10822_2016_9971_MOESM1_ESM.pdf (115 kb)
Supplementary material 1 (PDF 115 kb)
10822_2016_9971_MOESM2_ESM.xlsx (10 kb)
Supplementary material 2 (XLSX 10 kb)
10822_2016_9971_MOESM3_ESM.xlsx (183 kb)
Supplementary material 3 (XLSX 182 kb)
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Supplementary material 4 (PDF 772 kb)
10822_2016_9971_MOESM5_ESM.csv (5 kb)
Supplementary material 5 (CSV 4 kb)
10822_2016_9971_MOESM6_ESM.xlsx (25 kb)
Supplementary material 6 (XLSX 25 kb)
10822_2016_9971_MOESM7_ESM.sdf (110 kb)
Supplementary material 7 (SDF 109 kb)


  1. 1.
    Guthrie JP (2009) J Phys Chem B 113:4501CrossRefGoogle Scholar
  2. 2.
    Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24:259CrossRefGoogle Scholar
  3. 3.
    Skillman AG (2012) J Comput Aided Mol Des 26:473CrossRefGoogle Scholar
  4. 4.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) J Comput Aided Mol Des 28:305CrossRefGoogle Scholar
  5. 5.
    Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) J Comput Aided Mol Des 28:135CrossRefGoogle Scholar
  6. 6.
    Czodrowski P, Sotriffer CA, Klebe G (2007) J Mol Biol 367:1347CrossRefGoogle Scholar
  7. 7.
    Steuber H, Czodrowski P, Sotriffer CA, Klebe G (2007) J Mol Biol 373:1305CrossRefGoogle Scholar
  8. 8.
    Martin YC (2009) J Comput Aid Mol Des 23:693CrossRefGoogle Scholar
  9. 9.
    Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) J Pharm Sci 98:861CrossRefGoogle Scholar
  10. 10.
    Kollman PA (1996) Acc Chem Res 29:461CrossRefGoogle Scholar
  11. 11.
    Best SA, Merz KM, Reynolds CH (1999) J Phys Chem B 103:714CrossRefGoogle Scholar
  12. 12.
    Chen B, Siepmann JI (2006) J Phys Chem B 110:3555CrossRefGoogle Scholar
  13. 13.
    Lyubartsev AP, Jacobsson SP, Sundholm G, Laaksonen A (2001) J Phys Chem B 105:7775CrossRefGoogle Scholar
  14. 14.
    Bhatnagar N, Kamath G, Chelst I, Potoff JJ (2012) J Pharm Sci 137:014502Google Scholar
  15. 15.
    Margolis SA, Levenson M (2000) Fresenius’ J Anal Chem 367:1CrossRefGoogle Scholar
  16. 16.
    Stephenson R, Stuart J, Tabak M (1984) J Chem Eng Data 29:287CrossRefGoogle Scholar
  17. 17.
    Black C, Joris GG, Taylor HS (1948) J Chem Phys 16:537CrossRefGoogle Scholar
  18. 18.
    Yalkowsky SH, He Y, Jain P (2010) Handbook of aqueous solubility data. CRC Press, Boca RatonCrossRefGoogle Scholar
  19. 19.
    Harris JG, Stillinger FH (1991) J Chem Phys 95:5953CrossRefGoogle Scholar
  20. 20.
    Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9954-8 Google Scholar
  21. 21.
    Lin B, Pease JH (2013) Comb Chem High Throughput Screen 16:817CrossRefGoogle Scholar
  22. 22.
    Haynes WM (2014) CRC handbook of chemistry and physics. CRC Press, Boca RatonGoogle Scholar
  23. 23.
    Leo A, Hansch C, Elkins D (1971) Chem Rev 71:525CrossRefGoogle Scholar
  24. 24.
    Milletti F, Storchi L, Sforna G, Cruciani G (2007) J Chem Inf Model 47:2172CrossRefGoogle Scholar
  25. 25.
    Milletti F, Storchi L, Goracci L, Bendels S, Wagner B, Kansy M, Cruciani G (2010) Eur J Med Chem 45:4270CrossRefGoogle Scholar
  26. 26.
    Efron B (1979) Ann Stat 7:1CrossRefGoogle Scholar
  27. 27.
    Hanson SM, Ekins S, Chodera JD (2015) J Comput-Aided Mol Des 29:1073CrossRefGoogle Scholar
  28. 28.
    Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. CRC Press, Boca RatonGoogle Scholar
  29. 29.
  30. 30.
  31. 31.
    Rosenblatt M (1956) Ann Math Stat 27:832CrossRefGoogle Scholar
  32. 32.
    Drewokane MWOB, Hobson P, Halchenko Y, Lukauskas S, Warmenhoven J, Cole JB, Hoyer S, Vanderplas J, Villalba S, Quintero E, Martin M, Miles A, Meyer K, Augspurger T, Yarkoni T, Bachant P, Evans C, Fitzgerald C, Nagy T, Ziegler E, Megies T, Wehner D, St-Jean S, Coelho LP, Hitz G, Lee A, Rocher L (2016) seaborn: v0.7.0 (January 2016)Google Scholar
  33. 33.
    Nicholls A (2014) J Comput-Aided Mol Des 28:887CrossRefGoogle Scholar
  34. 34.
    J Chem Eng Data 12, 326 (1967)Google Scholar
  35. 35.
    Speight JG et al (2005) Lange’s handbook of chemistry, vol 1. McGraw-Hill, New YorkGoogle Scholar
  36. 36.
    Klamt A, Eckert F, Reinisch J, Wichmann K (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9927-y Google Scholar
  37. 37.
    Hastings WK (1970) Biometrika 57:97CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Graduate Program in Physiology, Biophysics, and Systems BiologyWeill Cornell Medical CollegeNew YorkUSA
  2. 2.Computational Biology Program, Sloan Kettering InstituteMemorial Sloan Kettering Cancer CenterNew YorkUSA
  3. 3.Genentech, Inc.South San FranciscoUSA
  4. 4.Theravance BiopharmaSouth San FranciscoUSA
  5. 5.Denali TherapeuticsSouth San FranciscoUSA
  6. 6.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of California, IrvineIrvineUSA

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