Minimum Population Search, an Application to Molecular Docking

Antonio Bolufé-Röhler; Alex Coto Santiesteban; Marta Rosa Soto; Stephen Chen

Minimum Population Search, an Application to Molecular Docking

Antonio Bolufé-Röhler ^[1] ; Alex Coto-Santiesteban ^[1] ; Marta Rosa Soto ^[2] ; Stephen Chen ^[3]
1. [1] Universidad de La Habana
  
  Universidad de La Habana
  
  Cuba
2. [2] Instituto de Cibernética Matemática y Física
  
  Instituto de Cibernética Matemática y Física
  
  Cuba
3. [3] York University (Canadá)
  
  York University (Canadá)
  
  Canadá
Mostrar afiliaciones +
Localización: GECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología, ISSN-e 2255-5684, Vol. 2, Nº. 3, 2014, págs. 1-16
Idioma: inglés
Enlaces
- Texto completo
Resumen
- Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi-modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance.
Referencias bibliográficas
- Audet, Charles and J. E., Dennis (2003). Analysis of Generalized Pattern Searches. SIAM Journal on Optimization, Vol. 13(3): 889–903.
- Bellman, R. E. (1957). Dynamic Programming. Princeton University Press.
- Bolufé-Röhler, A. and Chen, S. (2011). An analysis of sub-swarms in multi-swarm systems. AUS-AI, p.271–280.
- Bolufé-Röhler, A. and Chen, S. (2012). Multi-swarm hybrid for multi-modal optimization. IEEE CEC, p. 1759–1766.
- Bolufé-Röhler, A. and Chen, S. (2013). Minimum Population Search – Lessons from building a heuristic technique with two population members....
- Bolufé-Röhler, A., Estévez-Velarde, S., Piad-Morffis, A., Chen, S. and Montgomery, J. (2013). Differential evolution with thresheld convergence....
- Bolufé-Röhler, A., and Chen, S. (2014). Extending Minimum Population Search towards Large Scale Global Optimization. IEEE CEC. In Press.
- Bratton, D. and Kennedy, J. (2007). Defining a standard for particle swarm optimization. IEEE SIS, p. 120–127.
- Brest, J., Zamuda, A., Bošković, B., Maučec, M. S. and Žumer, V. (2008). Highdimensional real-parameter optimization using self-adaptive differential...
- Brits, R., Engelbrecht, A. P. and Van den Bergh, F. (2002). A niching particle swarm optimizer. SEAL, p. 692–696.
- Brooijmans N. and Kuntz, I. (2003). Molecular Recognition and Docking Algorithms. Annual Review of Biophysics and Biomolecular Structure,...
- Cabrera, F. J. and Coello, C. C. (2010). Micro-MOPSO: A multi-objective particle swarm optimizer that uses a very small population size. Multi-Objective...
- Caraffini, F., Neri, F. and Poikolainen, I. (2013). Micro-differential evolution with extra moves along the axes. IEEE Symposium on Differential...
- Chen, S. and Montgomery, J. (2011). A simple strategy to maintain diversity and reduce crowding in particle swarm optimization. Australasian...
- Chen, S., Montgomery, J. and Bolufé-Röhler, A. (2014). Some Measurements on the Effects of the Curse of Dimensionality. ACM GECCO, in Press.
- Chu, W., Gao, X., and Sorooshian, S. (2011). A new evolutionary search strategy for global optimization of high-dimensional problems. Information...
- Engelbrecht, A. (2012). Particle swarm optimization: velocity initialization. IEEE CEC, p. 70–77.
- Gao, Z., Xiao, T. and Fan, W. (2011). Hybrid differential evolution and Nelder–Mead algorithm with re-optimization. Soft Computing, Vol. 15...
- Goodsell, D. and Olson, A. (1990). Automated Docking of Substrates to Proteins by Simulated Annealing. Proteins: Structure, Function and Genetics,...
- Grefenstette, J.J. (1986). Optimization of control parameters for genetic algorithms. IEEE SMC: 16(1): 122–128.
- Haile J. M. (1992). Molecular Dynamics Simulations: Elementary Methods. Wiley, New York, p. 11–18.
- Halperin I., Ma, B., Wolfson, H. and Nussinov, R. (2002). Principles of Docking: An Overview of Search Algorithms and a Guide to Scoring Functions....
- Hansen, N., Muller, S. D. and Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance...
- Helwig, S., Branke, J. and Mostaghim, S. (2013). Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization. IEEE Trans....
- Hong, Y., Kwong, S., Ren, Q. and Wang, X. (2007). Over-Selection: An attempt to boost EDA under small population size. IEEE CEC, p. 1075–1082.
- Hou , T.J., Wang, J.M. and Xu, X. J. (1999). A Comparison of Three Heuristic Algorithms for Molecular Docking. Chinese Chemical Letters, Vol....
- Kitchen, D.B., Decornez, H., Furr, J.R. and Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: methods and...
- Kroemer R. (2007). Structure-Based Drug Design: Docking and Scoring. Current Protein and Peptide Science, Vol. 8: 312–328.
- Lagarias, J. C., Reeds, J. A., Wright, M. H. and Wright, P. E. (1998). Convergence properties of the Nelder-Mead simplex method in low dimensions....
- Larrañaga P. and Lozano, J. A. (2011). Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers,...
- Liu, A. and Yang, M. (2012). A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent...
- Magalhães, C. S., Barbosa, J.C. and Laurent, E. (2004). A genetic algorithm for the ligandprotein docking problem.Genetics and Molecular Biology,...
- Mallipeddi, R. and Suganthan, P. N. (2008). Empirical study on the effect of population size on differential evolution algorithm. IEEE CEC,...
- Molina, D., Lozano, M., García-Martínez, C. and Herrera, F. (2010). Memetic Algorithms for Continuous Optimisation Based on Local Search Chains....
- Montgomery, J. and Chen, S. (2012). A simple strategy for maintaining diversity and reducing crowding in differential evolution. IEEE CEC,...
- Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S. and Olson, A. J. (2009). Autodock4 and AutoDockTools4:...
- Namasivayam, V. and Günther R. (2007). PSO@AUTODOCK: A fast flexible molecular docking program based on swarm intelligence. Chem. Biol....
- Payne, J. L. and Eppstein, M. J. (2005). A hybrid genetic algorithm with pattern search for finding heavy atoms in protein crystals. ACM GECCO,...
- Reeves, C. (1993). Using genetic algorithms with small populations. ICGA, p. 92–99.
- Storn, R. and Price, H. (1997). Differential Evolution – A simple and efficient heuristic for global optimization over continuous spaces....
- Takagi, H. (2001). Interactive Evolutionary Computation: Fusion of the capabilities of ECO optimization and human evaluation. IEEE PIEEE,...
- Tavares, J., Melab N. and Talbi, E. (2002). An Empirical Study on the Influence of Genetic Operators for Molecular Docking Optimization. PROTEINS:...
- Thomsen, R. (2003). Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search...
- Thomsen, R. (2004). Multi-modal optimization using crowding-based differential evolution. IEEE CEC, p. 1382–1389.
- Trott O., Olson A.J. (2009). Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.Journal...
- DE code (2014) http://www.icsi.berkeley.edu/~storn/code.html
- CMA-ES code (2014) https://www.lri.fr/~hansen/cmaes_inmatlab.html