Abstract
The aim of this work is to present a new hybrid algorithm for the Molecular Docking problem: Variable Genetic Operator Search (VGOS). The proposed method combines an Evolutionary Algorithm with Variable Neighborhood Search. Experimental results show that the algorithm is able to achieve good results, in terms of energy optimization and RMSD values for several molecules when compared with previous approaches. In addition, when hybridized with the L-BFGS local search method it attains very competitive results.
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References
Diller, R.M., Verlind, L.M.J.: A critical evaluation of several global optimization algorithms for the purpose of molecular docking. J. Comput. Chem. 20, 1740–1751 (1999)
McConkey, B., Sobelove, V., Edlman, M.: The performance of current methods in ligand-protein docking. Current Science 83, 845–855 (2002)
Oduguawa, A., Tiwari, A., Fiorentino, S., Roy, R.: Multi-objective optimisation of the protein-ligand docking problem in drug discovery. In: ACM, GECCO 2006 (2006)
Maldenovic, N., Hansen, P.: Variable neighborhood search. Computers in Operations Reasearch 24, 1097–1100 (1997)
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)
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)
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., Corbeil, C.: Towards the development of univeral, fast and highly accurate docking/scoring methods: a long way to go. British Journal of Pharmacology 153, 1–20 (2007)
Thomsen, R.: Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. Biosystems 72, 57–73 (2003)
Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated docking using a lamarkian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998)
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, pp. 589–598. 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)
Tavares, J., Mesmoudi, S., Talbi, E.G.: On the efficiency of local search methods for the molecular docking problem. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 104–115. Springer, Heidelberg (2009)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing (Spring 2003)
Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation 12, 273–302 (2004)
Thomsen, R.: Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. Biosystems 72, 57–73 (2003)
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 empirical binding free energy function. Journal of Computational Chemistry 19, 1639–1662 (1998)
Liu, D.C., Nocedal, J.: On the limited memory method for large scale optimization. Mathematical Programming B, 503–528 (1989)
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Mesmoudi, S., Tavares, J., Jourdan, L., Talbi, EG. (2010). Variable Genetic Operator Search for the Molecular Docking Problem. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_1
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DOI: https://doi.org/10.1007/978-3-642-12211-8_1
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