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DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015

Abstract

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

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Acknowledgments

The computational work coordinated by S.M. has been supported with financial support from the University of Padova, Italy. MMS lab is also very grateful to Chemical Computing Group, OpenEye and Acellera for the scientific and technical partnership. S.M. participates in the European COST Action CM1207 (GLISTEN). The work of M.S. has been supported by University of Padova, Italy (UNIPD, Progetto Giovani Studiosi 2013: Protocol number 79122). Finally, MMS lab is extremely grateful to the organizers of the D3R Grand Challenge for the perfect organization of the event.

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Correspondence to Stefano Moro.

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Veronica Salmaso and Mattia Sturlese have contributed equally to this work.

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Salmaso, V., Sturlese, M., Cuzzolin, A. et al. DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015. J Comput Aided Mol Des 30, 773–789 (2016). https://doi.org/10.1007/s10822-016-9966-4

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Keywords

  • DockBench
  • D3R Grand Challenge 2015
  • Blind prediction
  • Molecular docking
  • Docking benchmark