Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge


In the context of the SAMPL5 blinded challenge standard free energies of binding were predicted for a dataset of 22 small guest molecules and three different host molecules octa-acids (OAH and OAMe) and a cucurbituril (CBC). Three sets of predictions were submitted, each based on different variations of classical molecular dynamics alchemical free energy calculation protocols based on the double annihilation method. The first model (model A) yields a free energy of binding based on computed free energy changes in solvated and host-guest complex phases; the second (model B) adds long range dispersion corrections to the previous result; the third (model C) uses an additional standard state correction term to account for the use of distance restraints during the molecular dynamics simulations. Model C performs the best in terms of mean unsigned error for all guests (MUE \(3.2\,<\,3.4\,<\,3.6\,\text{kcal}\,\text{mol}^{-1}\)—95 % confidence interval) for the whole data set and in particular for the octa-acid systems (MUE \(1.7\,<\,1.9\,<\,2.1\,\text{kcal}\,\text{mol}^{-1}\)). The overall correlation with experimental data for all models is encouraging (\(R^2\, 0.65\,<\,0.70<0.75\)). The correlation between experimental and computational free energy of binding ranks as one of the highest with respect to other entries in the challenge. Nonetheless the large MUE for the best performing model highlights systematic errors, and submissions from other groups fared better with respect to this metric.

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    A fourth model that corrects for finite-effects on electrostatic interactions was also submitted by our group, but the results are not discussed here as it was subsequently established that a coding error led to incorrect evaluation of the correction terms.


  1. 1.

    Michel J (2014) Phys Chem Chem Phys 16(10):4465–4477

    CAS  Article  Google Scholar 

  2. 2.

    Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24(4):259–279

    CAS  Article  Google Scholar 

  3. 3.

    Skillman AG (2012) J Comput Aided Mol Des 26(5):473–474

    CAS  Article  Google Scholar 

  4. 4.

    Guthrie JP (2009) J Phys Chem B 113(14):4501–4507

    CAS  Article  Google Scholar 

  5. 5.

    Michel J, Henchman RH, Gerogiokas G, Southey MWY, Mazanetz MP, Law RJ (2014) J Chem Theory Comput 10(9):4055–4068

    CAS  Article  Google Scholar 

  6. 6.

    Jorgensen LW, Thomas LL (2008) J Chem Theory Comput 4(6):869–876

    CAS  Article  Google Scholar 

  7. 7.

    Woods CJ, Malaisree M, Hannongbua S, Mulholland AJ (2011) J Chem Phys. doi:10.1063/1.3519057

    Google Scholar 

  8. 8.

    Chang C-E, Gilson MK (2004) J Am Chem Soc 126(40):13156–13164

    CAS  Article  Google Scholar 

  9. 9.

    Muddana SH, Gilson MK (2012) J Chem Theory Comput 8(6):2023–2033

    CAS  Article  Google Scholar 

  10. 10.

    Mikulskis P, Cioloboc D, Andrejić M, Khare S, Brorsson J, Genheden S, Mata RA, Söderhjelm P, Ryde U (2014) J Comput Aided Mol Des 28(4):375–400

    CAS  Article  Google Scholar 

  11. 11.

    König G, Pickard IV FC, Mei Y, Brooks BR (2014) J Comput Aided Mol Des 28(3):245–257

    Article  Google Scholar 

  12. 12.

    Beckstein O, Fourrier A, Iorga BI (2014) J Comput Aided Mol Des 28(3):265–276

    CAS  Article  Google Scholar 

  13. 13.

    Monroe JI, Shirts MR (2014) J Comput Aided Mol Des 28(4):401–415

    CAS  Article  Google Scholar 

  14. 14.

    Chen I-J, Foloppe N (2011) Drug Develop Res 72(1):85–94

    CAS  Article  Google Scholar 

  15. 15.

    Halgren TA, Damm W (2001) Curr Opin Struct Biol 11(2):236–242

    CAS  Article  Google Scholar 

  16. 16.

    Kastenholz MA, Hnenberger PH (2004) J Phys Chem B 108(2):774–788

    CAS  Article  Google Scholar 

  17. 17.

    Jorgensen LW, Ravimohan C (1985) J Chem Phys 83(6):3050–3054

    CAS  Article  Google Scholar 

  18. 18.

    Mezei M (1987) J Chem Phys 86(12):7084–7088

    CAS  Article  Google Scholar 

  19. 19.

    Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Acc Chem Res 33(12):889–897

    CAS  Article  Google Scholar 

  20. 20.

    Gibb CLD, Gibb CB (2013) J Comput Aided Mol Des 28(4):319–325

    Article  Google Scholar 

  21. 21.

    Gilberg L, Zhang B, Zavalij PY, Sindelar V, Isaacs L (2015) Org Biomol Chem 13:4041–4050

    CAS  Article  Google Scholar 

  22. 22.

    Zhang B, Isaacs L (2014) J Med Chem 57(22):9554–9563

    CAS  Article  Google Scholar 

  23. 23.

    Sure R, Antony J, Grimme S (2014) J Phys Chem B 118(12):3431–3440

    CAS  Article  Google Scholar 

  24. 24.

    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25(9):1157–1174

    CAS  Article  Google Scholar 

  25. 25.

    Mishra SK, Calabr G, Loeffler HH, Michel J, Koa J (2015) J Chem Theory Comput 11(7):3333–3345

    CAS  Article  Google Scholar 

  26. 26.

    Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC (2016) Chem Sci 7(1):207–218

    CAS  Article  Google Scholar 

  27. 27.

    Jorgensen WL, Buckner JK, Boudon S, Tirado-Rives J (1988) J Chem Phys 89(6):3742–3746

    CAS  Article  Google Scholar 

  28. 28.

    Gilson MK, Given JA, Bush BL, McCammon JA (1997) Biophys J 72(3):1047

    CAS  Article  Google Scholar 

  29. 29.

    Michel J, Essex JW (2010) J Comput Aided Mol Des 24(8):639–658

    CAS  Article  Google Scholar 

  30. 30.

    Shirts MR, Mobley DL, Chodera JD, Pande VS (2007) J Phys Chem B 111(45):13052–13063

    CAS  Article  Google Scholar 

  31. 31.

    Zwanzig WR (1954) J Chem Phys 22(8):1420–1426

    CAS  Article  Google Scholar 

  32. 32.

    Frenkel D, Smit B (2001) Understanding molecular simulation, 2nd edn. Academic Press Inc, Orlando

    Google Scholar 

  33. 33.

    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) J Chem Phys 79(2):926–935

    CAS  Article  Google Scholar 

  34. 34.

    Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, Cheatham III DS, Darden TA, Duke TA, Gohlke H, Goetz AW, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossvary I, Kovalenko A, Lee TS, LeGrand S, Luchko T, Luo R, Madej B, Merz KM, Paesani F, Roe DR, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Kollman PA (2014) AMBER 14, University of California, San Francisco

  35. 35.

    Woods C, Mey ASJ, Calabro G, Michel J (2016) Sire molecular simulations framework. Accessed May 31

  36. 36.

    Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang L-P, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS (2013) J Chem Theory Comput 9(1):461–469

    CAS  Article  Google Scholar 

  37. 37.

    Roe RD, Cheatham TE III (2013) J Chem Theory Comput 9(7):3084–3095

    CAS  Article  Google Scholar 

  38. 38.

    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) J Comput Aided Mol Des 28(4):305–317

    CAS  Article  Google Scholar 

  39. 39.

    Schrödinger release 2015-2: Maestro, version 10.2, schrödinger, llc, New York, NY, 2015

  40. 40.

    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132–146 (cited By 552)

    CAS  Article  Google Scholar 

  41. 41.

    Shirts MR, Chodera JD (2008) J Chem Phys. doi:10.1063/1.2978177

    Google Scholar 

  42. 42.

    Hopkins CW, Le Grand S, Walker RC, Roitberg AE (2015) J Chem Theory Comput 11(4):1864–1874

    CAS  Article  Google Scholar 

  43. 43.

    Andersen HC (1980) J Chem Phys 72:2384–2393

    CAS  Article  Google Scholar 

  44. 44.

    Tironi IG, Sperb R, Smith PE, van Gunsteren WF (1995) J Chem Phys 102(13):5451–5459

    CAS  Article  Google Scholar 

  45. 45.

    Gan H, Benjamin CJ, Gibb BC (2011) J Am Chem Soc 133(13):4770–4773

    CAS  Article  Google Scholar 

  46. 46.

    Yin J, Henriksen NM, Slochower DR, Chiu MW, Mobley DL, Gilson MK (2016) Overview of the SAMPL5 host-guest challenge: are we doing better? J Comput Aided Mol Des (under review)

  47. 47.

    Bosisio S, Mey ASJS, Michel J (2016) Blinded predictions of distribution coefficients in the SAMPL5 challenge. J Comput Aided Mol Des (under review)

  48. 48.


  49. 49.

    Yin J, Henriksen NM, Slochower DR, Gilson MK (2016) The SAMPL5 host-guest challenge: binding free energies and enthalpies from explicit solvent simulations. J Comput Aided Mol Des (under review)

  50. 50.

    Goetz AW, Poole D, Le Grand S, Walker RC, Salomon-Ferrer R (2013) J Chem Theory Comput 9:3878–3888

    Article  Google Scholar 

  51. 51.

    Velez-Vega C, Gilso MK (2013) J Comput Chem 34(27):2360–2371

    CAS  Google Scholar 

  52. 52.

    Kastenholz MA, Hünenberger PH (2006) J Chem Phys 124(12):124106

    CAS  Article  Google Scholar 

  53. 53.

    Kastenholz MA, Hünenberger PH (2006) J Chem Phys 124(22):224501

    Article  Google Scholar 

  54. 54.

    Reif MM, Oostenbrink C (2014) J Comput Chem 35(3):227–243

    CAS  Article  Google Scholar 

  55. 55.

    Rocklin GJ, Boyce SE, Fischer M, Fish I, Mobley DL, Shoichet BK, Dill KA (2013) J Mol Biol 425(22):4569–4583

    CAS  Article  Google Scholar 

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J. M. is supported by a Royal Society University Research Fellowship. The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/ 2007-2013)/ERC Grant Agreement No. 336289.

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Correspondence to Julien Michel.

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Bosisio, S., Mey, A.S.J.S. & Michel, J. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge. J Comput Aided Mol Des 31, 61–70 (2017).

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  • SAMPL5
  • Binding free energies
  • Host-guest systems