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Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

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Abstract

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

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Authors and Affiliations

  1. Merck & Co., Inc., 33 Avenue Louis Pasteur, Boston, MA, 02215, USA

    Xavier Fradera

  2. Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ, 07033-1310, USA

    Andreas Verras, Yuan Hu, Hongwu Wang, James I. Fells, Alejandro Crespo, Brad Sherborne, Huijun Wang, Zhengwei Peng & Ying-Duo Gao

  3. Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA, 19486, USA

    Deping Wang & Kira A. Armacost

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  1. Xavier Fradera
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  2. Andreas Verras
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  3. Yuan Hu
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  4. Deping Wang
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  5. Hongwu Wang
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  9. Brad Sherborne
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  10. Huijun Wang
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  11. Zhengwei Peng
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  12. Ying-Duo Gao
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Correspondence to Xavier Fradera or Andreas Verras.

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Fradera, X., Verras, A., Hu, Y. et al. Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016. J Comput Aided Mol Des 32, 113–127 (2018). https://doi.org/10.1007/s10822-017-0053-2

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  • DOI: https://doi.org/10.1007/s10822-017-0053-2

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