Abstract
The guided local search method has been successfully applied to a significant number of NP-hard optimization problems, producing results of similar caliber, if not better, compared to those obtained from algorithms specially designed for each singular optimization problem. Ranging from the familiar TSP and QAP to general function optimization problems, GLS sits atop many well-known algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). With lesser parameters to adjust to, GLS is relatively simple to implement and apply in many problems. This paper focuses on the potential applications of GLS in ligand docking problems via drug design. Over the years, computer aided drug design (CADD) has spearheaded the drug design process, whereby much focus has been trained on efficient searching in de novo drug design. Previous and ongoing approaches of meta heuristic methods such as GA, SA & TS have proven feasible, but not without problems. Inspired by the huge success of Guided Local Search (GLS) in solving optimization problems, we incorporated it into the drug design problem in protein ligand docking and have found it to be effective.
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References
Huang, S.Y., Zou, X.: Advances and challenges in protein-ligand docking. Int. J. Mol. Sci. 11, 3016–3034 (2010)
Schmitt, S., Kuhn, D., Klebe, G.: A new method to detect related function among proteins independent of sequence and fold homology. J. Mol. Biol. 323, 387–406 (2002)
Taylor, R.D., Jewsbury, P.J., Essex, J.W.: A review of protein small-molecule docking methods. J. Comput. Aided Mol. Des. 16, 151–166 (2002)
An, J., Totrov, M., Abagyan, R.: Comprehensive identification of “druggable” protein ligand binding sites. Genome Inform. 15, 31–41 (2004)
Devi, R.V., Sathya, S.S., Coumar, M.S.: Appl. Soft Comput. 27, 543–552 (2015)
Cecchini, M., Kolb, P., Majeux, N., et al.: Automated docking of highly flexible ligands by genetic algorithms: a critical assessment. J. Comput. Chem. 25, 412–422 (2003)
Jones, G., Willett, P., Glen, R.C., et al.: Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748 (1997)
Ross, B.J.: A Lamarckian evolution strategy for genetic algorithms. In: The Practical Handbook of Genetic Algorithms, p. 16 (1999)
Willett, P.: Genetic algorithms in molecular recognition and design. Trends Biotechnol. 13, 516–521 (1995)
López-Camacho, E., García Godoy, M.J., García-Nieto, J., et al.: Solving molecular flexible docking problems with metaheuristics: a comparative study. Appl. Soft Comput. 28, 379–393 (2015). doi:10.1016/j.asoc.2014.10.049
Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Stat. Sci. 8, 10–15 (1993)
Suman, B., Kumar, P.: A survey of simulated annealing as a tool for single and multiobjective optimization. J. Oper. Res. Soc. 10, 1143 (2006)
Liu, M., Wang, S.: MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J. Comput. Aided Mol. Des. 13, 435–451 (1999)
Friesner, R.A., Banks, J.L., Murphy, R.B., et al.: Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004). doi:10.1021/jm0306430
Baxter, C.A., Murray, C.W., Clark, D.E., et al.: Flexible docking using tabu search and an empirical estimate of binding affinity. Proteins 15, 367–382 (1998)
Bai, R., Kendall, G., Qu, R., Atkin, J.A.D.: Tabu assisted guided local search approaches for freight service network design. Inf. Sci. (NY) 189, 266–281 (2012). doi:10.1016/j.ins.2011.11.028
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26(1), 1–13 (1996)
Voudouris, C.: Guided local search: an illustrative example in function optimisation. BT Technol. J. 16, 46–50 (1998)
Tsang, E., Voudouris, C.: Fast local search and guided local search and their application to British Telecom’s workforce scheduling problem. Oper. Res. Lett. 20, 119–127 (1997). doi:10.1016/S0167-6377(96)00042-9
Barbucha, D.: Agent-based guided local search. Expert Syst. Appl. 39, 12032–12045 (2012). doi:10.1016/j.eswa.2012.03.074
Vansteenwegen, P., Souffriau, W., Berghe, G.V., Van Oudheusdena, D.: A guided local search metaheuristic for the team orienteering problem. Eur. J. Oper. Res. 196, 118–127 (2009). doi:10.1016/j.ejor.2008.02.037
Acknowledgement
This work is carried out within the framework of a research grant funded by Ministry of Higher Education (MOHE) Fundamental Research Grant Scheme (Project Code: FRGS/1/2014/ICT1/TAYLOR/03/1).
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Peh, S.C.W., Hong, J.L. (2016). GLSDock – Drug Design Using Guided Local Search. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_2
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