Abstract
Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands. In this context, the SMPSO multi-objective swarm-intelligence algorithm has shown a remarkable performance. SMPSO is characterized by having an external archive used to store the non-dominated solutions and also as the basis of the leader selection strategy. In this paper, we analyze several SMPSO variants based on different archiving strategies in the scope of a benchmark of molecular docking instances. Our study reveals that the SMPSOhv, which uses an hypervolume contribution based archive, shows the overall best performance.
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References
Coello, C.A., Toscano, G., Lechuga, M.S.: Handling Multiple objectives with Particle Swarm Optimization. IEEE Trans. Evol. Comp. 8(3), 3 (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Durillo, J.J., García-Nieto, J., Nebro, A.J., Coello, C.A.C., Luna, F., Alba, E.: Multi-objective particle swarm optimizers: an experimental comparison. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 495–509. Springer, Heidelberg (2009)
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 20(6), 10154–10183 (2015)
Gu, J., Yang, X., Kang, L., Wu, J., Wang, X.: MoDock: a multi-objective strategy improves the accuracy for molecular docking. Algs. Mol. Bio. 10, 8 (2015)
Janson, S., Merkle, D., Middendorf, M.: Molecular docking with multi-objective particle swarm optimization. Appl. Soft Comput. 8(1), 666–675 (2008)
López-Camacho, E., García-Godoy, M.J., Nebro, A.J., Aldana-Montes, J.F.: jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework. Bioinformatics 30(3), 437–438 (2014)
López-Camacho, E., García-Godoy, M.J., García-Nieto, J., Nebro, A.J., Aldana-Montes, J.F.: Solving molecular flexible docking problems with metaheuristics: a comparative study. Appl. Soft Comput. 28, 379–393 (2015)
López-Camacho, E., García-Godoy, M.J., García-Nieto, J., Nebro, A.J., Aldana-Montes, J.F.: A new multi-objective approach for molecular docking based on RMSD and binding energy. In: 3rd International Conference on Algorithm for Computational Biology (2016, in-Press)
Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J.: AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30(16), 2785–2791 (2009)
Nebro, A., Durillo, J., Garcia-Nieto, J., Coello Coello, C.A., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE Symposium on Computational Intelligence in Multi-criteria Decision-Making, pp. 66–73 (2009)
Nebro, A., Durillo, J., Coello Coello, C.A.: Analysis of leader selection strategies in a MOPSO. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 3153–3160, June 2013
Norgan, A.P., Coffman, P.K., Kocher, J.P.A., Katzmann, D.J., Sosa, C.P.: Multilevel parallelization of AutoDock 4.2. J. Cheminform. 3(1), 12 (2011)
Sierra, M.R., Coello Coello, C.A.: Improving PSO-based multi-objective optimization using crowding, mutation and \(\epsilon \)-dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)
Sandoval-Perez, A., Becerra, D., Vanegas, D., Restrepo-Montoya, D., Nino, F.: A multi-objective optimization energy approach to predict the ligand conformation in a docking process. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 181–192. Springer, Heidelberg (2013)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, Boca Raton (2007)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comp. 11(6), 712–731 (2007)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comp. 3(4), 257–271 (1999)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comp. 7(2), 117–132 (2003)
Acknowledgments
This work is partially funded by Grants TIN2011-25840 (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz I+D+I). This article is based upon work from COST Action CA15140, supported by COST (European Cooperation in Science and Technology).
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García-Nieto, J., López-Camacho, E., Godoy, M.J.G., Nebro, A.J., Durillo, J.J., Aldana-Montes, J.F. (2016). A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_4
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