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Molecules 2015, 20(6), 10154-10183;

Solving Molecular Docking Problems with Multi-Objective Metaheuristics

Khaos Research Group, Departament of Computer Sciences, University of Málaga (UMA), ETSI Informática, Campus de Teatinos, Málaga 29071, Spain
Author to whom correspondence should be addressed.
Academic Editor: Rino Ragno
Received: 30 March 2015 / Accepted: 21 May 2015 / Published: 2 June 2015
(This article belongs to the Special Issue Molecular Docking in Drug Design)
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Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II), speed modulation multi-objective particle swarm optimization (SMPSO), third evolution step of generalized differential evolution (GDE3), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and S-metric evolutionary multi-objective optimization (SMS-EMOA). We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA) provided by the AutoDock tool). Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery. View Full-Text
Keywords: molecular docking; multi-objective optimization; nature-inspired metaheuristics;algorithm comparison molecular docking; multi-objective optimization; nature-inspired metaheuristics;algorithm comparison

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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García-Godoy, M.J.; López-Camacho, E.; García-Nieto, J.; Nebro, A.J.; Aldana-Montes, J.F. Solving Molecular Docking Problems with Multi-Objective Metaheuristics. Molecules 2015, 20, 10154-10183.

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