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J Comput Aided Mol Des. 2018 Jan;32(1):251-264. doi: 10.1007/s10822-017-0051-4. Epub 2017 Aug 24.

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2.

Author information

1
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padova, Italy.
2
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padova, Italy. stefano.moro@unipd.it.

Abstract

Molecular docking is a powerful tool in the field of computer-aided molecular design. In particular, it is the technique of choice for the prediction of a ligand pose within its target binding site. A multitude of docking methods is available nowadays, whose performance may vary depending on the data set. Therefore, some non-trivial choices should be made before starting a docking simulation. In the same framework, the selection of the target structure to use could be challenging, since the number of available experimental structures is increasing. Both issues have been explored within this work. The pose prediction of a pool of 36 compounds provided by D3R Grand Challenge 2 organizers was preceded by a pipeline to choose the best protein/docking-method couple for each blind ligand. An integrated benchmark approach including ligand shape comparison and cross-docking evaluations was implemented inside our DockBench software. The results are encouraging and show that bringing attention to the choice of the docking simulation fundamental components improves the results of the binding mode predictions.

KEYWORDS:

Cross-docking; D3R Grand Challenge 2; DockBench; Docking benchmark; Molecular docking; Self-docking

PMID:
28840418
DOI:
10.1007/s10822-017-0051-4

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