Journal of Computer-Aided Molecular Design

, Volume 32, Issue 1, pp 129–142 | Cite as

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

  • Ying-Duo GaoEmail author
  • Yuan HuEmail author
  • Alejandro CrespoEmail author
  • Deping Wang
  • Kira A. Armacost
  • James I. Fells
  • Xavier Fradera
  • Hongwu Wang
  • Huijun Wang
  • Brad Sherborne
  • Andreas Verras
  • Zhengwei Peng


The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.


Affinity prediction 2016 D3R Grand Challenge QM/MM MMGBSA FXR MacroModel interaction energy Glide X-score 



The authors would like to thank the following people for efforts, expertise and helpful discussions: Symon Gathiaka and Robert P. Sheridan. We are grateful to Merck & Co., Inc., Kenilworth, NJ USA Postdoctoral Research Fellows Program for financial support to Y. H. and the technical support from the High Performance Computing (HPC) group at Merck & Co., Inc., Kenilworth, NJ USA.

Supplementary material

10822_2017_72_MOESM1_ESM.docx (207 kb)
Supplementary material 1 (DOCX 207 KB)


  1. 1.
    Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Nat Rev Drug Discov 3(11):935CrossRefGoogle Scholar
  2. 2.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) J Med Chem 47(7):1750CrossRefGoogle Scholar
  3. 3.
    Schneider G, Böhm H-J (2002) Drug Discov Today 7(1):64CrossRefGoogle Scholar
  4. 4.
    Hawkins PCD, Skillman AG, Nicholls A (2007) J Med Chem 50(1):74CrossRefGoogle Scholar
  5. 5.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK (2004) J Med Chem 47(7):1739CrossRefGoogle Scholar
  6. 6.
    Brown N, Jacoby E (2006) Mini Rev Med Chem 6(11):1217CrossRefGoogle Scholar
  7. 7.
    Mauser H, Guba W (2008) Curr Top Med Chem 11(3):365Google Scholar
  8. 8.
    Wang L, Deng Y, Wu Y, Kim B, LeBard DN, Wandschneider D, Beachy M, Friesner RA, Abel R (2016) J Chem Theory Comput 13(1):42CrossRefGoogle Scholar
  9. 9.
    Hu Y, Stumpfe D, Bajorath Jr (2017) J Med Chem 60(4):1238CrossRefGoogle Scholar
  10. 10.
    Jasial S, Hu Y, Bajorath J (2016) J Chem Inf Model 56(2):300CrossRefGoogle Scholar
  11. 11.
    Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL (2015) J Chem Theory Comput 12(1):281CrossRefGoogle Scholar
  12. 12.
    Vanommeslaeghe K, Raman EP, MacKerell AD Jr (2012) J Chem Inf Model 52(12):3155CrossRefGoogle Scholar
  13. 13.
    Sherborne B, Shanmugasundaram V, Cheng AC, Christ CD, DesJarlais RL, Duca JS, Lewis RA, Loughney DA, Manas ES, McGaughey GB (2016) J Comp-Aided Mol Design 30(12):1139CrossRefGoogle Scholar
  14. 14.
    Hu Y, Sherborne B, Lee T-S, Case DA, York DM, Guo Z (2016) J Comp-Aided Mol Design 30(7):533CrossRefGoogle Scholar
  15. 15.
    Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Curr Opin Struct Biol 21(2):150CrossRefGoogle Scholar
  16. 16.
    Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J (2015) J Am Chem Soc 137(7):2695CrossRefGoogle Scholar
  17. 17.
    Wan S, Knapp B, Wright DW, Deane CM, Coveney PV (2015) J Chem Theory Comput 11(7):3346CrossRefGoogle Scholar
  18. 18.
    Loeffler HH, Michel J, Woods C (2015) J Chem Inf Model 2485Google Scholar
  19. 19.
    Gapsys V, Michielssens S, Seeliger D, de Groot BL (2015) J Comput Chem 36(5):348CrossRefGoogle Scholar
  20. 20.
    Homeyer N, Gohlke H (2013) J Comput Chem 34(11):965CrossRefGoogle Scholar
  21. 21.
    Lee T, Hu Y, Sherborne B, Guo Z, York DM (2017) J Chem Theory Comput 13(7):3077CrossRefGoogle Scholar
  22. 22.
    Crespo A, Rodriguez-Granillo A, Lim VT (2017) Curr Top Med Chem 17(23):2663CrossRefGoogle Scholar
  23. 23.
    Huang M, Giese TJ, York DM (2015) J Comput Chem 36(18):1370CrossRefGoogle Scholar
  24. 24.
    Giese TJ, Huang M, Chen H, York DM (2014) Acc Chem Res 47(9):2812CrossRefGoogle Scholar
  25. 25.
    Richter HGF, Benson GM, Blum D, Chaput E, Feng S, Gardes C, Grether U, Hartman P, Kuhn B, Martin RE (2011) Bioorg Med Chem Lett 21(1):191CrossRefGoogle Scholar
  26. 26.
    Richter HGF, Benson GM, Bleicher KH, Blum D, Chaput E, Clemann N, Feng S, Gardes C, Grether U, Hartman P (2011) Bioorg Med Chem Lett 21(4):1134CrossRefGoogle Scholar
  27. 27.
    Feng S, Yang M, Zhang Z, Wang Z, Hong D, Richter H, Benson GM, Bleicher K, Grether U, Martin RE (2009) Bioorg Med Chem Lett 19(9):2595CrossRefGoogle Scholar
  28. 28.
    Halgren TA (1999) J Comput Chem 20(7):720CrossRefGoogle Scholar
  29. 29.
    Schrödinger (2014) Release 2014-1: MacroModel. Schrödinger, LLC, New YorkGoogle Scholar
  30. 30.
    Fradera X, Verras A, Hu Y, Wang D, Wang H, Fells J, Armacost K, Crespo A, Sherborne B, Wang H, Peng Z, Gao Y-D (2017) J Comp-Aided Mol Design. doi: 10.1007/s10822-017-0053-2 Google Scholar
  31. 31.
    Molecular Operating Environment (MOE). Chemical Computing Group Inc., Sherbooke St. West, Suite #910. MontrealGoogle Scholar
  32. 32.
    OpenEye Scientific Software, Inc. Fe Santa (2015) NM,
  33. 33.
    Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) J Mol Biol 267(3):727CrossRefGoogle Scholar
  34. 34.
    Wang R, Lai L, Wang S (2002) J Comp-Aided Mol Design 16(1):11CrossRefGoogle Scholar
  35. 35.
    Wang R, Lu Y, Wang S (2003) J Med Chem 46(12):2287CrossRefGoogle Scholar
  36. 36.
    Liu J, Wang R (2015) J Chem Inf Model 55(3):475CrossRefGoogle Scholar
  37. 37.
    Li Y, Liu Z, Li J, Han L, Liu J, Zhao Z, Wang R (2014) J Chem Inf Model 54(6):1700CrossRefGoogle Scholar
  38. 38.
    POSIT OpenEye Scientific Software, Santa Fe, NM,
  39. 39.
    OMEGA OpenEye Scientific Software, Santa Fe, NM, Hawkins, P.C.D.; Skillman, A.G.; Warren, G.L.; Ellingson, B.A.; Stahl, M.T.
  40. 40.
    ROCS OpenEye Scientific Software, Santa Fe, NM,
  41. 41.
    Schrödinger Release 2016-3: Jaguar, version 8.6, Schrödinger. LLC, New York, 2016Google Scholar
  42. 42.
    Crespo A, Scherlis DA, Marti MA, Ordejon P, Roitberg AE, Estrin DA (2003) J Phys Chem B 107(49):13728CrossRefGoogle Scholar
  43. 43.
    Warshel A, Levitt M (1976) J Mol Biol 103(2):227CrossRefGoogle Scholar
  44. 44.
    Tannor DJ, Marten B, Murphy R, Friesner RA, Sitkoff D, Nicholls A, Ringnalda M, Goddard WA, Honig B (1994) J Am Chem Soc 116(26):11875CrossRefGoogle Scholar
  45. 45.
    Marten B, Kim K, Cortis C, Friesner RA, Murphy RB, Ringnalda MN, Sitkoff D, Honig B (1996) J Phys Chem 100(28):11775CrossRefGoogle Scholar
  46. 46.
    Kojetin DJ, Burris TP (2013) Mol Pharmacol 83(1):1CrossRefGoogle Scholar
  47. 47.
    Nettles KW, Bruning JB, Gil G, O’Neill EE, Nowak J, Hughs A, Kim Y, DeSombre ER, Dilis R, Hanson RN (2007) EMBO Rep 8(6):563CrossRefGoogle Scholar
  48. 48.
    Jasial S, Hu Y, Bajorath Jr (2014.; 2016) Small-molecule drug discovery suite 2014-4: QSite, version 6.5, Schrödinger. LLC, New YorkGoogle Scholar
  49. 49.
    Murphy RB, Philipp DM, Friesner RA (2000) J Comput Chem 21(16):1442CrossRefGoogle Scholar
  50. 50.
    Philipp DM, Friesner RA (1999) J Comput Chem 20(14):1468CrossRefGoogle Scholar
  51. 51.
    Becke AD (1993) J Chem Phys 98(2):1372CrossRefGoogle Scholar
  52. 52.
    Johnson BG, Gill PMW, Pople JA (1993) J Chem Phys 98(7):5612CrossRefGoogle Scholar
  53. 53.
    Lee CT, Yang WT, Parr RG (1988) Phys Rev B 37(2):785CrossRefGoogle Scholar
  54. 54.
    Banks JL, Beard HS, Cao YX, Cho AE, Damm W, Farid R, Felts AK, Halgren TA, Mainz DT, Maple JR, Murphy R, Philipp DM, Repasky MP, Zhang LY, Berne BJ, Friesner RA, Gallicchio E, Levy RM (2005) J Comput Chem 26(16):1752CrossRefGoogle Scholar
  55. 55.
    Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, Philipp DM, Rinaldo D, Halls MD, Zhang J, Friesner RA (2013) Int J Quantum Chem 113(18):2110CrossRefGoogle Scholar
  56. 56.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132CrossRefGoogle Scholar
  57. 57.
    Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23(16):1623CrossRefGoogle Scholar
  58. 58.
    Miller MD, Kearsley SK, Underwood DJ, Sheridan RP (1994) J Comp-Aided Mol Design 8(2):153CrossRefGoogle Scholar
  59. 59.
    Biggadike K, Bledsoe RK, Coe DM, Cooper TWJ, House D, Iannone MA, Macdonald SJF, Madauss KP, McLay IM, Shipley TJ (2009) Proc Natl Acad Sci USA 106(43):18114CrossRefGoogle Scholar
  60. 60.
    Luchko T, Gusarov S, Roe DR, Simmerling C, Case DA, Tuszynski J, Kovalenko A (2010) J Chem Theory Comput 6(3):607CrossRefGoogle Scholar
  61. 61.
    Kovalenko A, Hirata F (1999) J Chem Phys 110(20):10095CrossRefGoogle Scholar
  62. 62.
    Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) J Am Chem Soc 130(9):2817CrossRefGoogle Scholar
  63. 63.
    Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Proc Natl Acad Sci USA 104(3):808CrossRefGoogle Scholar
  64. 64.
    Mi L-Z, Devarakonda S, Harp JM, Han Q, Pellicciari R, Willson TM, Khorasanizadeh S, Rastinejad F (2003) Mol Cell 11(4):1093CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Merck & Co., Inc.KenilworthUSA
  2. 2.Merck & Co., Inc.West PointUSA
  3. 3.Merck & Co., Inc.BostonUSA

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