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Docking-undocking combination applied to the D3R Grand Challenge 2015

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Abstract

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.

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Authors and Affiliations

  1. Departament de Fisicoquímica, Facultat de Farmàcia, Institut de Biomedicina de la Universitat de Barcelona (IBUB), Universitat de Barcelona, Av. Joan XXIII s/n, 08028, Barcelona, Spain

    Sergio Ruiz-Carmona & Xavier Barril

  2. Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain

    Xavier Barril

Authors
  1. Sergio Ruiz-Carmona
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  2. Xavier Barril
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Correspondence to Xavier Barril.

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Ruiz-Carmona, S., Barril, X. Docking-undocking combination applied to the D3R Grand Challenge 2015. J Comput Aided Mol Des 30, 805–815 (2016). https://doi.org/10.1007/s10822-016-9979-z

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