Next Article in Journal
A New and Efficient Synthesis of 6-O-Methylscutellarein, the Major Metabolite of the Natural Medicine Scutellarin
Next Article in Special Issue
Computational Studies of Benzoxazinone Derivatives as Antiviral Agents against Herpes Virus Type 1 Protease
Previous Article in Journal
Isolation of an Angiotensin I-Converting Enzyme Inhibitory Protein with Antihypertensive Effect in Spontaneously Hypertensive Rats from the Edible Wild Mushroom Leucopaxillus tricolor
Previous Article in Special Issue
DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations
Article Menu

Export Article

Open AccessArticle
Molecules 2015, 20(6), 10154-10183; doi:10.3390/molecules200610154

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)
View Full-Text   |   Download PDF [3354 KB, uploaded 2 June 2015]   |  

Abstract

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
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]

Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top