Advertisement

Journal of Computer-Aided Molecular Design

, Volume 30, Issue 11, pp 927–944 | Cite as

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

  • Caitlin C. Bannan
  • Kalistyn H. Burley
  • Michael Chiu
  • Michael R. Shirts
  • Michael K. Gilson
  • David L. Mobley
Article

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.

Keywords

SAMPL Distribution coefficient Blind challenge Free energy Alchemical Molecular simulation 

Notes

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).

References

  1. 1.
    Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) J Comput Aided Mol Des 28(3):135CrossRefGoogle Scholar
  2. 2.
    Geballe MT, Guthrie JP (2012) J Comput Aided Mol Des 26(5):489CrossRefGoogle Scholar
  3. 3.
    Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24(4):259CrossRefGoogle Scholar
  4. 4.
    Klimovich PV, Mobley DL (2010) J Comput Aided Mol Des 24(4):307CrossRefGoogle Scholar
  5. 5.
    Mobley DL, Bayly CI, Cooper MD, Dill KA (2009) J Phys Chem B 113(14):4533CrossRefGoogle Scholar
  6. 6.
    Mobley DL, Liu S, Cerutti DS, Swope WC, Rice JE (2012) J Comput Aided Mol Des 26(5):551CrossRefGoogle Scholar
  7. 7.
    Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) J Med Chem 51(4):769CrossRefGoogle Scholar
  8. 8.
    Rustenburg AS, Dancer J, Lin B, Feng JA, Ortwine DF, Mobley DL, Chodera JD (2016) J Comput Aided Mol DesGoogle Scholar
  9. 9.
    Leo A, Hansch C, Elkins D (1971) Chem Rev 71(6):525CrossRefGoogle Scholar
  10. 10.
    Young RJ, Green DVS, Luscombe CN, Hill AP (2011) Drug Discov Today 16(17–18):822CrossRefGoogle Scholar
  11. 11.
    Essex JW, Reynolds CA, Richards WG (1992) J Am Chem Soc 114(10):3634CrossRefGoogle Scholar
  12. 12.
    Best SA, Merz KM Jr, Reynolds CH (1999) J Phys Chem B 103(4):714CrossRefGoogle Scholar
  13. 13.
    Eksterowicz JE, Miller JL, Kollman PA (1997) J Phys Chem B 101(50):10971CrossRefGoogle Scholar
  14. 14.
    Jorgensen WL (1989) Acc Chem Res 22:187CrossRefGoogle Scholar
  15. 15.
    Jorgensen WL, Briggs JM, Contreras L (1990) J Phys 94(4):1683Google Scholar
  16. 16.
    Garrido NM, Queimada AJ, Jorge M, Macedo EA, Economou IG (2009) J Chem Theory Comput 5(9):2436CrossRefGoogle Scholar
  17. 17.
    Garrido NM, Jorge M, Queimada AJ, Gomes JRB, Economou IG, Macedo EA (2011) Phys Chem Chem Phys 13(38):17384CrossRefGoogle Scholar
  18. 18.
    Garrido NM, Economou IG, Queimada AJ, Jorge M, Macedo EA (2012) AIChE J 58(6):1929CrossRefGoogle Scholar
  19. 19.
    Yang L, Ahmed A, Sandler SI (2013) J Comput Chem 34(4):284CrossRefGoogle Scholar
  20. 20.
    Michel J, Orsi M, Essex JW (2007) J Phys Chem B 112(3):657CrossRefGoogle Scholar
  21. 21.
    Genheden S (2016) J Chem Theory Comput 12(1):297CrossRefGoogle Scholar
  22. 22.
    I. OpenEye Scientific Software. Oechem (2010). www.eyesopen.com
  23. 23.
    Bannan CC, Calabró G, Kyu DY, Mobley DL (2016) J Chem Theory Comput 12(8):4015CrossRefGoogle Scholar
  24. 24.
    Wilk MB, Gnanadesikan R (1968) Biometrika 55(1):1Google Scholar
  25. 25.
    Berendsen HJC, Van Der Spoel D, van Drunen R (1995) Comput Phys Commun 91(1–3):43CrossRefGoogle Scholar
  26. 26.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) J Chem Theory Comput 4(3):435CrossRefGoogle Scholar
  27. 27.
    Lindahl E, Hess B, van der Spoel D (2001) J Mol Model 7(8):306CrossRefGoogle Scholar
  28. 28.
    van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) J Comput Chem 26(16):1701CrossRefGoogle Scholar
  29. 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):845CrossRefGoogle Scholar
  30. 30.
    Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E (2014) Solving software challenges for exascale, vol 8759. Springer, StockholmGoogle Scholar
  31. 31.
    Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) SoftwareX 1–2:19CrossRefGoogle Scholar
  32. 32.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25(9):1157CrossRefGoogle Scholar
  33. 33.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132CrossRefGoogle Scholar
  34. 34.
    Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23(16):1623CrossRefGoogle Scholar
  35. 35.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) J Chem Phys 79(2):926CrossRefGoogle Scholar
  36. 36.
    Liu S, Cao S, Hoang K, Young KL, Paluch AS, Mobley DL (2016) J Chem Theory Comput 12(4):1930CrossRefGoogle Scholar
  37. 37.
    Klimovich PV, Shirts MR, Mobley DL (2015) J Comput Aided Mol Des 29(5):397CrossRefGoogle Scholar
  38. 38.
    Parameswaran S, Mobley DL (2014) J Comput Aided Mol Des 28(8):825CrossRefGoogle Scholar
  39. 39.
    Lide DR (ed) (1996) CRC handbook of chemistry and physics, 76th edn. CRC Press, Boca RatonGoogle Scholar
  40. 40.
    Sangster J (1989) J Phys Chem Ref Data 18:1111CrossRefGoogle Scholar
  41. 41.
    Schrödinger Release 2014-4: Epik, version 3.0, Schrödinger, LLC, New York, NY, (2014)Google Scholar
  42. 42.
    Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) J Comput Aided Mol Des 21(12):681CrossRefGoogle Scholar
  43. 43.
    Greenwood JR, Calkins D, Sullivan AP, Shelley JC (2010) J Comput Aided Mol Des 24(6–7):591CrossRefGoogle Scholar
  44. 44.
    Schrödinger Release 2014-4: Ligprep, version 3.2, Schrödinger, LLC, New York, NY, (2014)Google Scholar
  45. 45.
    Wang R, Fu Y, Lai L (1997) J Chem Inf Model 37(3):615Google Scholar
  46. 46.
    Wang R, Gao Y, Lai L (2000) Perspect Drug Discov Des 19(1):47CrossRefGoogle Scholar
  47. 47.
    Black C, Joris GG, Taylor HS (1948) J Chem Phys 16(5):538CrossRefGoogle Scholar
  48. 48.
    Humphrey W, Dalke A, Schulten K (1996) J Mol Graph 14(1):33CrossRefGoogle Scholar
  49. 49.
    Paranahewage SS, Gierhart CS, Fennell CJ (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9950-z Google Scholar
  50. 50.
    Iorga B, Kenney IM, Beckstein O (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9949-5 Google Scholar
  51. 51.
    Bosisio S, Mey ASJS, Michel J (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9933-0 Google Scholar
  52. 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. 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. 54.
    Genheden S, Essex J (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9926-z Google Scholar
  55. 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. 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. 57.
    Jones MR, Brooks BR, Wilson AK (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9964-6 Google Scholar
  58. 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. 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. 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. 61.
    Park H, Chung KC (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9928-x Google Scholar
  62. 62.
    Santos-Martins D, Fernandes PA, Ramos MJa (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9951-y Google Scholar
  63. 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. 64.
    Fennell CJ (2016) Personal CommunicationGoogle Scholar
  65. 65.
    Klamt A (2016) Personal CommunicationGoogle Scholar
  66. 66.
    Pickard IV FC (2016) Personal CommunicationGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of CaliforniaIrvineUSA
  2. 2.Department of Pharmaceutical SciencesUniversity of CaliforniaIrvineUSA
  3. 3.Qualcomm InstituteUniversity of CaliforniaSan DiegoUSA
  4. 4.Chemical and Biological EngineeringUniversity of ColoradoBoulderUSA
  5. 5.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of CaliforniaSan DiegoUSA

Personalised recommendations