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Molecular Docking: Shifting Paradigms in Drug Discovery
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Key Topics in Molecular Docking for Drug Design

1
Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
2
Department of Drugs and Medicines; School of Pharmacy; Federal University of Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil
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Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil
4
Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21949-900, RJ, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(18), 4574; https://doi.org/10.3390/ijms20184574
Received: 3 June 2019 / Revised: 9 July 2019 / Accepted: 10 July 2019 / Published: 15 September 2019
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design)
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area. View Full-Text
Keywords: computer-aided drug design; structure-based drug design; benchmarking sets; consensus methods; fragment-based; machine learning computer-aided drug design; structure-based drug design; benchmarking sets; consensus methods; fragment-based; machine learning
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Torres, P.H.M.; Sodero, A.C.R.; Jofily, P.; Silva-Jr, F.P. Key Topics in Molecular Docking for Drug Design. Int. J. Mol. Sci. 2019, 20, 4574.

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