Journal of Computer-Aided Molecular Design

, Volume 28, Issue 3, pp 201–209 | Cite as

SAMPL4 & DOCK3.7: lessons for automated docking procedures

  • Ryan G. Coleman
  • Teague Sterling
  • Dahlia R. Weiss


The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.


Molecular docking Solvation SAMPL First-order models 



We acknowledge financial support from National Research Service Award-Kirschstein fellowships F32GM096544 (to RGC) and F32GM093580 (to DRW), and US National Institutes of Health grants GM59957 and GM71630 (to Brian K. Shoichet). We would also like to thank the SAMPL4 organizers, as well as ChemAxon for Marvin & cxcalc, OpenEye for OMEGA and OEtools. Final acknowledgements to Paul Hawkins for motivation and guidance on statistical methods.

Supplementary material

10822_2014_9722_MOESM1_ESM.xlsx (69 kb)
Supplementary material 1 (XLSX 69 kb)


  1. 1.
    Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J Med Chem 51(4):769–779. doi: 10.1021/jm070549+ CrossRefGoogle Scholar
  2. 2.
    Skillman AG, Geballe M, Nicholls A (2010) SAMPL2 challenge: prediction of solvation energies and tautomer ratios. J Comput Aided Mol Des 24(4):257–258. doi: 10.1007/s10822-010-9358-0 CrossRefGoogle Scholar
  3. 3.
    Skillman AG (2012) SAMPL3: blinded prediction of host-guest binding affinities, hydration free energies, and trypsin inhibitors. J Comput Aided Mol Des 26(5):473–474. doi: 10.1007/s10822-012-9580-z CrossRefGoogle Scholar
  4. 4.
    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507. doi: 10.1021/jp806724u CrossRefGoogle Scholar
  5. 5.
    Kryshtafovych A, Fidelis K, Moult J (2013) CASP10 results compared to those of previous CASP experiments. Proteins Struct Func Bioinf n/a–n/a. doi: 10.1002/prot.24448
  6. 6.
    Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des. doi: 10.1007/s10822-014-9718-2
  7. 7.
    Guthrie JP (2014) SAMPL4, a blind challenge for computational solvation free energies: the compounds considered. J Comput Aided Mol Des 28 (in press)Google Scholar
  8. 8.
    Cao L, Isaacs L (2013) Absolute and relative binding affinity of cucurbit[7]uril toward a series of cationic guests. Supramol Chem 1:1–9Google Scholar
  9. 9.
    Gibb CLD, Gibb BC (2014) Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J Comput Aided Mol Des. doi: 10.1007/s10822-013-9690-2
  10. 10.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 28 (in press)Google Scholar
  11. 11.
    Peat TS, Dolezal O, Newman J, Mobley D, Deadman JJ (2014) Interrogating HIV integrase for compounds that bind- a SAMPL challenge. J Comput Aided Mol Des. doi: 10.1007/s10822-014-9721-7
  12. 12.
    Mobley DL, Liu S, Lim NM, Deng N, Branson K, Perryman SF, Levy RM, Gallicchio E, Olson AS (2014) Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des. doi: 10.1007/s10822-014-9723-5
  13. 13.
    Coleman RG, Carchia M, Sterling T, Irwin JJ, Shoichet BK (2013) Ligand pose and orientational sampling in molecular docking. PLoS ONE 8(10):e75992. doi: 10.1371/journal.pone.0075992 CrossRefGoogle Scholar
  14. 14.
    Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model. doi: 10.1021/ci3001277 Google Scholar
  15. 15.
    Hawkins GD, Giesen DJ, Lynch GC, Chambers CC, Rossi I, Storer JW, Li J, Zhu T, Thompson JD, Winget P, Lynch BJ, Rinaldi D, Liotard DA, Cramer CJ, Truhlar DG (2004) AMSOL. 7.1 edn. doi:
  16. 16.
    Csizmadia F, Tsantili-Kakoulidou A, Panderi I, Darvas F (1997) Prediction of distribution coefficient from structure. 1. Estimation method. J Pharm Sci 86(7):865–871. doi: 10.1021/js960177k CrossRefGoogle Scholar
  17. 17.
    Szegezdi S, Csizmadia F (2004) Prediction of dissociation constant using microconstants. In: Paper presented at the 227th American Chemical Society National Meeting, Anaheim, CAGoogle Scholar
  18. 18.
    Szegezdi S, Csizmadia F (2007) Calculating pKa values of small and large molecules. In: Paper presented at the 233rd American Chemical Society National Meeting, Chicago, ILGoogle Scholar
  19. 19.
    Sadowski J, Gasteiger J, Klebe G (1994) Comparison of automatic three-dimensional model builders using 639 X-ray structures. J Chem Inf Comput Sci 34(4):1000–1008. doi: 10.1021/ci00020a039 CrossRefGoogle Scholar
  20. 20.
    Tetko I, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin V, Radchenko E, Zefirov N, Makarenko A, Tanchuk V, Prokopenko V (2005) Virtual computational chemistry laboratory, design and description. J Comput Aided Mol Des 19(6):453–463. doi: 10.1007/s10822-005-8694-y CrossRefGoogle Scholar
  21. 21.
    OMEGA (2013) OpenEye software. Santa Fe, NMGoogle Scholar
  22. 22.
    Toolkits OpenEye (2013) OpenEye scientific software. Santa Fe, NMGoogle Scholar
  23. 23.
    Rhodes D, Peat T, Vandegraaff N, Jeevarajah D, Le G, Jones E, Smith J, Coates J, Winfield L, Thienthong N, Newman J, Lucent D, Ryan J, Savage G, Francis C, Deadman J (2011) Structural basis for a new mechanism of inhibition of HIV-1 integrase identified by fragment screening and structure-based design. Antivir Chem Chemother 21(4):155–168CrossRefGoogle Scholar
  24. 24.
    Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) The maximal affinity of ligands. Proc Natl Acad Sci U S A 96(18):9997–10002CrossRefGoogle Scholar
  25. 25.
    Wei BQ, Baase WA, Weaver LH, Matthews BW, Shoichet BK (2002) A model binding site for testing scoring functions in molecular docking. J Mol Biol 322(2):339–355CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50(9):1561–1573CrossRefGoogle Scholar
  28. 28.
    Yang Q, Sharp KA (2006) Atomic charge parameters for the finite difference poisson-boltzmann method using electronegativity neutralization. J Chem Theory Comput 2(4):1152–1167. doi: 10.1021/ct060009c CrossRefGoogle Scholar
  29. 29.
    Fennell CJ, Kehoe CW, Dill KA (2011) Modeling aqueous solvation with semi-explicit assembly. Proc Natl Acad Sci 108(8):3234–3239CrossRefGoogle Scholar
  30. 30.
    Grant JA, Pickup BT, Nicholls A (2001) A smooth permittivity function for Poisson-Boltzmann solvation methods. J Comput Chem 22(6):608–640. doi: 10.1002/jcc.1032 CrossRefGoogle Scholar
  31. 31.
    Ellingson B, Skillman AG, Nicholls A (2010) Analysis of SM8 and Zap TK calculations and their geometric sensitivity. J Comput Aided Mol Des 24(4):335–342. doi: 10.1007/s10822-010-9355-3 CrossRefGoogle Scholar
  32. 32.
    Ellingson BA, Bayly CI, Wlodek S, Geballe MT, Skillman AG, Nicholls A (2014) Placeholder for Ellingson/ZAP solvation paperGoogle Scholar
  33. 33.
    Park H (2014) Extended solvent-contact model approach to SAMPL4 blind prediction challenge for hydration free energies. J Comput Aided Mol Des 28 (in press)Google Scholar
  34. 34.
    Choi H, Kang H, Park H (2013) New solvation free energy function comprising intermolecular solvation and intramolecular self-solvation terms. J Cheminform 5(1):1–13CrossRefGoogle Scholar
  35. 35.
    Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594.
  36. 36.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2011) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(D1):D1100–D1107Google Scholar
  37. 37.
    Meng EC, Shoichet B, Kuntz ID (1992) Automated docking with grid-based energy evaluation. J Comp Chem 13:505–524CrossRefGoogle Scholar
  38. 38.
    Cohen J (1988) Statistical power analysis for the behavioral sciences. Routledge, LondonGoogle Scholar
  39. 39.
    Hawkins PC (2014) Searching with statistics: is pose prediction perfectible? Abstracts of Papers of the American Chemical Society 247. (ACS Meeting Indianapolis, Indiana 2013)Google Scholar
  40. 40.
    Voet ARD, Kumar A, Berenger F, Zhang KYJ (2014) Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des. doi: 10.1007/s10822-013-9702-2
  41. 41.
    Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotech 25(1):71–75CrossRefGoogle Scholar
  42. 42.
    Chen Y, Shoichet BK (2009) Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol 5(5):358–364. Google Scholar
  43. 43.
    Teotico DG, Babaoglu K, Rocklin GJ, Ferreira R, Giannetti AM, Shoichet BK (2009) Docking for fragment inhibitors of AmpC β-lactamase. Proc Natl Acad Sci USA 106(18):7455–7460CrossRefGoogle Scholar
  44. 44.
    Carlsson J, Coleman RG, Setola V, Irwin JJ, Fan H, Schlessinger A, Sali A, Roth BL, Shoichet BK (2011) Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nat Chem Biol 7(11):769–778.
  45. 45.
    Carlsson J, Yoo L, Gao ZG, Irwin JJ, Shoichet BK, Jacobson KA (2010) Structure-based discovery of A2A adenosine receptor ligands. J Med Chem 53(9):3748–3755. doi: 10.1021/jm100240h CrossRefGoogle Scholar
  46. 46.
    Kolb P, Rosenbaum DM, Irwin JJ, Fung JJ, Kobilka BK, Shoichet BK (2009) Structure-based discovery of β2-adrenergic receptor ligands. Proc Natl Acad Sci USA 106(16):6843–6848. Google Scholar
  47. 47.
    Kruse AC, Weiss DR, Rossi M, Hu J, Hu K, Eitel K, Gmeiner P, Wess Jr, Kobilka BK, Shoichet BK (2013) Muscarinic receptors as model targets and antitargets for structure-based ligand discovery. Mol Pharmacol 84(4):528–540CrossRefGoogle Scholar
  48. 48.
    Weiss DR, Ahn S, Sassano MF, Kleist A, Zhu X, Strachan R, Roth BL, Lefkowitz RJ, Shoichet BK (2013) Conformation guides molecular efficacy in docking screens of activated Œ ≤ -2 adrenergic G protein coupled receptor. ACS Chem Biol 8(5):1018–1026. doi: 10.1021/cb400103f CrossRefGoogle Scholar
  49. 49.
    Coleman RG, Sharp KA (2006) Travel depth, a new shape descriptor for macromolecules: application to ligand binding. J Mol Biol 362(3):441–458CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ryan G. Coleman
    • 1
  • Teague Sterling
    • 1
  • Dahlia R. Weiss
    • 1
  1. 1.Department of Pharmaceutical ChemistryUniversity of California, San FranciscoSan FranciscoUSA

Personalised recommendations