Skip to main content

Advertisement

SpringerLink
Log in

Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge

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

Abstract

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty—how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) J Comput Aided Mol Des 28(3):135

    Article  CAS  Google Scholar 

  2. Geballe MT, Guthrie JP (2012) J Comput Aided Mol Des 26(5):489

    Article  CAS  Google Scholar 

  3. Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24(4):259

    Article  CAS  Google Scholar 

  4. Klimovich PV, Mobley DL (2010) J Comput Aided Mol Des 24(4):307

    Article  CAS  Google Scholar 

  5. Mobley DL, Bayly CI, Cooper MD, Dill KA (2009) J Phys Chem B 113(14):4533

    Article  CAS  Google Scholar 

  6. Mobley DL, Liu S, Cerutti DS, Swope WC, Rice JE (2012) J Comput Aided Mol Des 26(5):551

    Article  CAS  Google Scholar 

  7. Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) J Med Chem 51(4):769

    Article  CAS  Google Scholar 

  8. Rustenburg AS, Dancer J, Lin B, Feng JA, Ortwine DF, Mobley DL, Chodera JD (2016) J Comput Aided Mol Des

  9. Leo A, Hansch C, Elkins D (1971) Chem Rev 71(6):525

    Article  CAS  Google Scholar 

  10. Young RJ, Green DVS, Luscombe CN, Hill AP (2011) Drug Discov Today 16(17–18):822

    Article  CAS  Google Scholar 

  11. Essex JW, Reynolds CA, Richards WG (1992) J Am Chem Soc 114(10):3634

    Article  CAS  Google Scholar 

  12. Best SA, Merz KM Jr, Reynolds CH (1999) J Phys Chem B 103(4):714

    Article  CAS  Google Scholar 

  13. Eksterowicz JE, Miller JL, Kollman PA (1997) J Phys Chem B 101(50):10971

    Article  CAS  Google Scholar 

  14. Jorgensen WL (1989) Acc Chem Res 22:187

    Article  Google Scholar 

  15. Jorgensen WL, Briggs JM, Contreras L (1990) J Phys 94(4):1683

    CAS  Google Scholar 

  16. Garrido NM, Queimada AJ, Jorge M, Macedo EA, Economou IG (2009) J Chem Theory Comput 5(9):2436

    Article  CAS  Google Scholar 

  17. Garrido NM, Jorge M, Queimada AJ, Gomes JRB, Economou IG, Macedo EA (2011) Phys Chem Chem Phys 13(38):17384

    Article  CAS  Google Scholar 

  18. Garrido NM, Economou IG, Queimada AJ, Jorge M, Macedo EA (2012) AIChE J 58(6):1929

    Article  CAS  Google Scholar 

  19. Yang L, Ahmed A, Sandler SI (2013) J Comput Chem 34(4):284

    Article  Google Scholar 

  20. Michel J, Orsi M, Essex JW (2007) J Phys Chem B 112(3):657

    Article  Google Scholar 

  21. Genheden S (2016) J Chem Theory Comput 12(1):297

    Article  CAS  Google Scholar 

  22. I. OpenEye Scientific Software. Oechem (2010). www.eyesopen.com

  23. Bannan CC, Calabró G, Kyu DY, Mobley DL (2016) J Chem Theory Comput 12(8):4015

    Article  Google Scholar 

  24. Wilk MB, Gnanadesikan R (1968) Biometrika 55(1):1

    CAS  Google Scholar 

  25. Berendsen HJC, Van Der Spoel D, van Drunen R (1995) Comput Phys Commun 91(1–3):43

    Article  CAS  Google Scholar 

  26. Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) J Chem Theory Comput 4(3):435

    Article  CAS  Google Scholar 

  27. Lindahl E, Hess B, van der Spoel D (2001) J Mol Model 7(8):306

    Article  CAS  Google Scholar 

  28. van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) J Comput Chem 26(16):1701

    Article  Google Scholar 

  29. Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) Bioinformatics (Oxford, England) 29(7):845

    Article  CAS  Google Scholar 

  30. Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E (2014) Solving software challenges for exascale, vol 8759. Springer, Stockholm

    Google Scholar 

  31. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) SoftwareX 1–2:19

    Article  Google Scholar 

  32. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25(9):1157

    Article  CAS  Google Scholar 

  33. Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132

    Article  CAS  Google Scholar 

  34. Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23(16):1623

    Article  CAS  Google Scholar 

  35. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) J Chem Phys 79(2):926

    Article  CAS  Google Scholar 

  36. Liu S, Cao S, Hoang K, Young KL, Paluch AS, Mobley DL (2016) J Chem Theory Comput 12(4):1930

    Article  CAS  Google Scholar 

  37. Klimovich PV, Shirts MR, Mobley DL (2015) J Comput Aided Mol Des 29(5):397

    Article  CAS  Google Scholar 

  38. Parameswaran S, Mobley DL (2014) J Comput Aided Mol Des 28(8):825

    Article  CAS  Google Scholar 

  39. Lide DR (ed) (1996) CRC handbook of chemistry and physics, 76th edn. CRC Press, Boca Raton

    Google Scholar 

  40. Sangster J (1989) J Phys Chem Ref Data 18:1111

    Article  CAS  Google Scholar 

  41. Schrödinger Release 2014-4: Epik, version 3.0, Schrödinger, LLC, New York, NY, (2014)

  42. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) J Comput Aided Mol Des 21(12):681

    Article  CAS  Google Scholar 

  43. Greenwood JR, Calkins D, Sullivan AP, Shelley JC (2010) J Comput Aided Mol Des 24(6–7):591

    Article  CAS  Google Scholar 

  44. Schrödinger Release 2014-4: Ligprep, version 3.2, Schrödinger, LLC, New York, NY, (2014)

  45. Wang R, Fu Y, Lai L (1997) J Chem Inf Model 37(3):615

    CAS  Google Scholar 

  46. Wang R, Gao Y, Lai L (2000) Perspect Drug Discov Des 19(1):47

    Article  CAS  Google Scholar 

  47. Black C, Joris GG, Taylor HS (1948) J Chem Phys 16(5):538

    Article  Google Scholar 

  48. Humphrey W, Dalke A, Schulten K (1996) J Mol Graph 14(1):33

    Article  CAS  Google Scholar 

  49. Paranahewage SS, Gierhart CS, Fennell CJ (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9950-z

    Google Scholar 

  50. Iorga B, Kenney IM, Beckstein O (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9949-5

    Google Scholar 

  51. Bosisio S, Mey ASJS, Michel J (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9933-0

    Google Scholar 

  52. Pickard F, König G, Tofoleanu F, Lee J, Simmonett A, Shao Y, Ponder J, Brooks BR (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9955-7

    Google Scholar 

  53. König G, Pickard FC, Huang J, Simmonett AC, Tofoleanu F, Lee J, Dral PO, Samarjeet FNU, Jones M, Shao Y, Thiel W, Brooks BR (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9936-x

    Google Scholar 

  54. Genheden S, Essex J (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9926-z

    Google Scholar 

  55. Kamath G, Kurnikov I, Fain B, Leontyev I, Illarionov A, Butin O, Olevanov M, Pereyaslavets L (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9958-4

    Google Scholar 

  56. Brini E, Paranahewage SS, Fennell CJ, Dill KA (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9961-9

    Google Scholar 

  57. Jones MR, Brooks BR, Wilson AK (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9964-6

    Google Scholar 

  58. Tielker N, Tomazic D, Heil J, Kloss T, Ehrhart S, Güssregen S, Schmidt KF, Kast S (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9939-7

    Google Scholar 

  59. Luchko T, Blinov N, Limon GC, Joyce KP, Kovalenko A (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9947-7

    Google Scholar 

  60. Diaz-Rodriguez S, Bozada SM, Phifer JR, Paluch AS (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9945-9

    Google Scholar 

  61. Park H, Chung KC (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9928-x

    Google Scholar 

  62. Santos-Martins D, Fernandes PA, Ramos MJa (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9951-y

    Google Scholar 

  63. Klamt A, Eckert F, Reinisch J, Wichmann K (2016) J Comput Aided Mol Des. doi:10.1007/s10822-016-9927-y

    Google Scholar 

  64. Fennell CJ (2016) Personal Communication

  65. Klamt A (2016) Personal Communication

  66. Pickard IV FC (2016) Personal Communication

Download references

Acknowledgments

D.L.M. and C.C.B. appreciate financial support from the National Institutes of Health (1R01GM108889-01) and the National Science Foundation (CHE 1352608), and computing support from the UCI GreenPlanet cluster, supported in part by NSF Grant CHE-0840513. This work was made possible in part by NIH grant U01 GM111528 for the Drug Design Data Resource, which supported the SAMPL workshop. M.K.G. thanks the National Institutes of Health for Grant GM061300. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. M.K.G. has an equity interest in and is a cofounder and scientific advisor of VeraChem LLC. We would also like to acknowledge John Shelley, Art Bochevarov, Robert Abel, and Mats Svensson from Schrödinger for their help with pKa and tautomer enumeration calculations. We also thank all the SAMPL5 participants and D3R Workshop attendees, and we especially appreciate valuable discussions with John Chodera (MSKCC), Ariën Rustenburg (MSKCC), Andreas Klamt (COSMOLogic), Christopher Fennell (Oklahoma State University), Samuel Genheden (Gothenburg University), and Frank Pickard (National Institute of Health).

Author information

Authors and Affiliations

  1. Department of Chemistry, University of California, 147 Bison Modular, Irvine, CA, 92697, USA

    Caitlin C. Bannan & David L. Mobley

  2. Department of Pharmaceutical Sciences, University of California, 147 Bison Modular, Irvine, CA, 92697, USA

    Kalistyn H. Burley & David L. Mobley

  3. Qualcomm Institute, University of California, San Diego, CA, USA

    Michael Chiu

  4. Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA

    Michael R. Shirts

  5. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA

    Michael K. Gilson

Authors
  1. Caitlin C. Bannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kalistyn H. Burley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael R. Shirts
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael K. Gilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David L. Mobley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David L. Mobley.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bannan, C.C., Burley, K.H., Chiu, M. et al. Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30, 927–944 (2016). https://doi.org/10.1007/s10822-016-9954-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-9954-8

Keywords

Search

Navigation