Skip to main content

Ranking docking poses by graph matching of protein–ligand interactions: lessons learned from the D3R Grand Challenge 2


A novel docking challenge has been set by the Drug Design Data Resource (D3R) in order to predict the pose and affinity ranking of a set of Farnesoid X receptor (FXR) agonists, prior to the public release of their bound X-ray structures and potencies. In a first phase, 36 agonists were docked to 26 Protein Data Bank (PDB) structures of the FXR receptor, and next rescored using the in-house developed GRIM method. GRIM aligns protein–ligand interaction patterns of docked poses to those of available PDB templates for the target protein, and rescore poses by a graph matching method. In agreement with results obtained during the previous 2015 docking challenge, we clearly show that GRIM rescoring improves the overall quality of top-ranked poses by prioritizing interaction patterns already visited in the PDB. Importantly, this challenge enables us to refine the applicability domain of the method by better defining the conditions of its success. We notably show that rescoring apolar ligands in hydrophobic pockets leads to frequent GRIM failures. In the second phase, 102 FXR agonists were ranked by decreasing affinity according to the Gibbs free energy of the corresponding GRIM-selected poses, computed by the HYDE scoring function. Interestingly, this fast and simple rescoring scheme provided the third most accurate ranking method among 57 contributions. Although the obtained ranking is still unsuitable for hit to lead optimization, the GRIM–HYDE scoring scheme is accurate and fast enough to post-process virtual screening data.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Previous experiences suggest that GRIMscore values above 0.70 correspond to a statistically significant similarity of the two interaction patterns under comparison.


  1. Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32:335–373

    CAS  Article  Google Scholar 

  2. Rognan D (2017) The impact of in silico screening in the discovery of novel and safer drug candidates. Pharmacol Ther 175:47–66

    CAS  Article  Google Scholar 

  3. Chen YC (2015) Beware of docking! Trends Pharmacol Sci 36:78–95

    Article  Google Scholar 

  4. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    CAS  Article  Google Scholar 

  5. Smith RD, Dunbar JB Jr, Ung PM, Esposito EX, Yang CY, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51:2115–2131

    CAS  Article  Google Scholar 

  6. Community Structure Activity resource. Accessed 30 July 2010

  7. Drug Design Data Resource. Accessed 30 July 2010

  8. Dunbar JB Jr, Smith RD, Yang CY, Ung PM, Lexa KW, Khazanov NA, Stuckey JA, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: selection of the protein–ligand complexes. J Chem Inf Model 51:2036–2046

    CAS  Article  Google Scholar 

  9. Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA (2013) CSAR benchmark exercise 2011–2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model 53:1853–1870

    CAS  Article  Google Scholar 

  10. Dunbar JB Jr, Smith RD, Damm-Ganamet KL, Ahmed A, Esposito EX, Delproposto J, Chinnaswamy K, Kang YN, Kubish G, Gestwicki JE, Stuckey JA, Carlson HA (2013) CSAR data set release 2012: ligands, affinities, complexes, and docking decoys. J Chem Inf Model 53:1842–1852

    CAS  Article  Google Scholar 

  11. Carlson HA (2016) Lessons learned over four benchmark exercises from the Community Structure-Activity Resource. J Chem Inf Model 56:951–954

    Article  Google Scholar 

  12. Carlson HA, Smith RD, Damm-Ganamet KL, Stuckey JA, Ahmed A, Convery MA, Somers DO, Kranz M, Elkins PA, Cui G, Peishoff CE, Lambert MH, Dunbar JB Jr (2016) CSAR 2014: a benchmark exercise using unpublished data from pharma. J Chem Inf Model 56:1063–1077

    CAS  Article  Google Scholar 

  13. Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB Jr, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK (2016) D3R Grand Challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 30:651–668

    CAS  Article  Google Scholar 

  14. Smith RD, Damm-Ganamet KL, Dunbar JB Jr, Ahmed A, Chinnaswamy K, Delproposto JE, Kubish GM, Tinberg CE, Khare SD, Dou J, Doyle L, Stuckey JA, Baker D, Carlson HA (2016) CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model 56:1022–1031

    CAS  Article  Google Scholar 

  15. Desaphy J, Raimbaud E, Ducrot P, Rognan D (2013) Encoding protein–ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 53:623–637

    CAS  Article  Google Scholar 

  16. Slynko I, Da Silva F, Bret G, Rognan D (2016) Docking pose selection by interaction pattern graph similarity: application to the D3R Grand Challenge 2015. J Comput Aided Mol Des 30:669–683

    CAS  Article  Google Scholar 

  17. Richter HG, Benson GM, Bleicher KH, Blum D, Chaput E, Clemann N, Feng S, Gardes C, Grether U, Hartman P, Kuhn B, Martin RE, Plancher JM, Rudolph MG, Schuler F, Taylor S (2011) Optimization of a novel class of benzimidazole-based farnesoid X receptor (FXR) agonists to improve physicochemical and ADME properties. Bioorg Med Chem Lett 21:1134–1140

    CAS  Article  Google Scholar 

  18. Bass JY, Caravella JA, Chen L, Creech KL, Deaton DN, Madauss KP, Marr HB, McFadyen RB, Miller AB, Mills WY, Navas F 3rd, Parks DJ, Smalley TL Jr, Spearing PK, Todd D, Williams SP, Wisely GB (2011) Conformationally constrained farnesoid X receptor (FXR) agonists: heteroaryl replacements of the naphthalene. Bioorg Med Chem Lett 21:1206–1213

    CAS  Article  Google Scholar 

  19. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    CAS  Article  Google Scholar 

  20. Bietz S, Urbaczek S, Schulz B, Rarey M (2014) Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. J Cheminform 6:12

    Article  Google Scholar 

  21. Jain AN (2007) Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput Aided Mol Des 21:281–306

    CAS  Article  Google Scholar 

  22. Desaphy J, Da Silva F, Rognan D (2014) IChem v.5.2.6.

  23. Schneider N, Lange G, Hindle S, Klein R, Rarey M (2013) A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function. J Comput Aided Mol Des 27:15–29

    CAS  Article  Google Scholar 

  24. Xu X, Xu X, Liu P, Zhu ZY, Chen J, Fu HA, Chen LL, Hu LH, Shen X (2015) Structural basis for small molecule NDB (N-Benzyl-N-(3-(tert-butyl)-4-hydroxyphenyl)-2,6-dichloro-4-(dimethylamino) Benzamide) as a selective antagonist of farnesoid X receptor alpha (FXRalpha) in stabilizing the homodimerization of the receptor. J Biol Chem 290:19888–19899

    CAS  Article  Google Scholar 

  25. Jin L, Feng X, Rong H, Pan Z, Inaba Yu, Qiu L, Zheng W, Lin S, Wang R, Wang Z, Wang S, Liu H, Li S, Xie W, Li Y (2013) The antiparasitic drug ivermectin is a novel FXR ligand that regulates metabolism. Nat Commun 4:1937

    Google Scholar 

  26. Kim R, Skolnick J (2008) Assessment of programs for ligand binding affinity prediction. J Comput Chem 29:1316–1331

    CAS  Article  Google Scholar 

  27. Gao C, Thorsteinson N, Watson I, Wang J, Vieth M (2015) Knowledge-based strategy to improve ligand pose prediction accuracy for lead optimization. J Chem Inf Model 55:1460–1468

    CAS  Article  Google Scholar 

  28. Kelley BP, Brown SP, Warren GL, Muchmore SW (2015) POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model 55:1771–1780

    CAS  Article  Google Scholar 

  29. Anighoro A, Bajorath J (2016) Three-dimensional similarity in molecular docking: prioritizing ligand poses on the basis of experimental binding modes. J Chem Inf Model 56:580–587

    CAS  Article  Google Scholar 

  30. Kumar A, Zhang KY (2016) Application of shape similarity in pose selection and virtual screening in CSARdock2014 exercise. J Chem Inf Model 56:965–973

    CAS  Article  Google Scholar 

Download references


We thank the CAPES foundation and the Ministry of Education of Brazil for a postdoctoral fellowship to P.S.F.C.G (Computational Biology program Grant, Issue No. 51/2013). The Computing Center of the IN2P3 (CNRS, Villeurbanne, France) is acknowledged for allocation of computing time and excellent support.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Didier Rognan.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 14 KB)

Supplementary material 2 (XLSX 11 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

da Silva Figueiredo Celestino Gomes, P., Da Silva, F., Bret, G. et al. Ranking docking poses by graph matching of protein–ligand interactions: lessons learned from the D3R Grand Challenge 2. J Comput Aided Mol Des 32, 75–87 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Docking
  • D3R
  • Drug discovery data resource
  • Grand Challenge