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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
Article

Abstract

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.

Keywords

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

Notes

Acknowledgements

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)

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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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