Abstract
Evolutionary approaches to molecular docking typically hybridize with local search methods, more specifically, the Solis-Wet method. However, some studies indicated that local search methods might not be very helpful in the context of molecular docking. An evolutionary algorithm with proper genetic operators can perform equally well or even outperform hybrid evolutionary approaches. We show that this is dependent on the type of local search method. We also propose an evolutionary algorithm which uses the L-BFGS method as local search. Results demonstrate that this hybrid evolutionary outperforms previous approaches and is better suited to serve as a basis for evolutionary docking methods.
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References
Neumaier, A.: Molecular modeling of proteins and mathematical prediction of protein structure. SIAM Review 39, 407–460 (1997)
Thomsen, R.: Protein-ligand docking with evolutionary algorithms. In: Fogel, G.B., Corne, D.W., Pan, Y. (eds.) Computational Intelligence in Bioinformatics, pp. 169–195. Wiley-IEEE Press (2008)
Thomsen, R.: Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. Biosystems 72, 57–73 (2003)
Tavares, J., Tantar, A.A., Melab, N., Talbi, E.G.: The influence of mutation on protein-ligand docking optimization: a locality analysis. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199. Springer, Heidelberg (2008)
Tavares, J., Melab, N., Talbi, E.G.: An empirical study on the influence of genetic operators for molecular docking optimization. Technical Report RR-6660, INRIA Lille - Nord Europe research Centre (2008)
Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated docking using a lamarckian genetic algorithm and and empirical binding free energy function. Journal of Computational Chemistry 19, 1639–1662 (1998)
Dixon, J.S.: Flexible docking of ligands to receptor sites using genetic algorithms. In: Proc. of the 9th European Symposium on Structure-Activity Relationships, Leiden, The Netherlands, pp. 412–413. ESCOM Science Publishers (1993)
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., Corbeil, C.: Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. British Journal of Pharmacology 153, 1–20 (2007)
Liu, D.C., Nocedal, J.: On the limited memory method for large scale optimization. Mathematical Programming B, 503–528 (1989)
Tavares, J., Tantar, A.A., Melab, N., Talbi, E.G.: The impact of local search on protein-ligand docking optimization. In: Proceedings of the 8th International Conference on Hybrid Intelligent Systems (2008)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)
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Tavares, J., Mesmoudi, S., Talbi, EG. (2009). On the Efficiency of Local Search Methods for the Molecular Docking Problem. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2009. Lecture Notes in Computer Science, vol 5483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01184-9_10
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DOI: https://doi.org/10.1007/978-3-642-01184-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01183-2
Online ISBN: 978-3-642-01184-9
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