Flexible protein-ligand docking using particle swarm optimization

2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

Volumn 1, Issue , 2005, Pages 251-258

  Liu, Bo Fu ^a   Chen, Hung Ming ^a   Huang, Hui Ling ^b   Hwang, Shiow Fen ^a   Ho, Shinn Ying ^c  

^a FENG CHIA UNIVERSITY   (Taiwan)

^b JINWEN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^c NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; MOLECULAR STRUCTURE; OPTIMIZATION; PROTEINS; ROBUSTNESS (CONTROL SYSTEMS);

DOCKING ENERGY; ENERGY FUNCTIONS; PARTICLE SWARM OPTIMIZATION (PSO); PROTEIN-LIGAND DOCKING PROBLEMS;

PROBLEM SOLVING;

EID: 27144459006     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. B. D. Bursulaya, M. Totrov, R. Abagyan, and C. L. Brooks. Comparative study of several algorithms for flexible ligand docking. Journal of Computer-Aided Molecular Design, 17(11):755-763, 2003.
2. R. C. Eberhart and Y. Shi. Comparison between genetic algorithms and particle swarm optimization. In The 7th International Conference on Evolutionary Programming, pages 611-616, San Diego, CA, USA, 1998.
3. R. C. Eberhart and Y. Shi. Comparing inertia weights and constriction factors in particle swarm optimization. In IEEE Int. Conf Evolutionary Computation, volume 1, pages 84-88, 2000.
4. T. J. A. Ewing, S. Makino, A. G. Skillman, and I. D. Kuntz. Dock 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design, 15(5):411-428, 2001.
5. B. C. Goldberg, D.E. An analysis of a reordering operator on a ga-hard problem. Biological Cybernetics, 62(8):397-405, 1990.
6. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addition-Wesley, Reading, MA, 1989.
7. D. S. Goodsell and A. J. Olson. Automated docking of substrates to proteins by simulated annealing. Proteins: Structure, Function, and Genetics, 8:195-202, 1990.
8. T. Hendtlass. Preserving diversity in particle swarm optimisation. In Developments in Applied Artificial Intelligence, volume 2718 of Lecture Notes in Artificial Intelligence, pages 31-40. 2003.
9. G. Jones, P. Willett, R. C. Glen, A. R. Leach, and R. Taylor. Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267(3):727-748, 1997.
10. J. Kennedy. The behavior of particles. In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming, pages 581-590. Springer-Verlag, Berlin, Germany, 1998.
11. J. Kennedy and R. C. Eberhart. Particle swarm optimization. In IEEE Int. Conf. Neural Networks, volume 4, pages 1942-1948, Perth, Australia, 1995.
12. M. Liu and S. M. Wang. Mcdock: A monte carlo simulation approach to the molecular docking problem. Journal of Computer-Aided Molecular Design, 13(5):435-451, 1999.
13. D. W. Miller and K. A. Dill. Ligand binding to proteins: The binding landscape model. Protein Science, 6(10):2166-2179, 1997.
14. G. M. Morris, D. S. Goodsell, R. S. Halliday, R. Huey, W. E. Hart, R. K. Belew, and A. J. Olson. Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry, 19(14): 1639-1662, 1998.
15. K. E. Parsopoulos and M. N. Vrahatis. Initializing the particle swarm optimizer using the nonlinear simplex method. In A. Grmela and N. Mastorakis, editors, Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pages 216-221. WSEAS press, Interlaken, Switzerland, 2002.
16. M. Rarey, B. Kramer, T. Lengauer, and G. Klebe. A fast flexible docking method using an incremental con struction algorithm. Journal of Molecular Biology, 261(3):470-489, 1996.
17. A. I. Salman A. and A.-M. S. Particle swarm optimization for task assignment problem. Microprocessors and Microsystems, 26(8):363-371, 2002.
18. Y. Shi and R. C. Eberhart. A modified particle swarm optimizer. In IEEE Conf. Evolutionary Computation, pages 69-73, Anchorage, AK, 1998.
19. Y. Shi and R. C. Eberhart. Parameter selection in particle swarm optimization. In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming, volume 7, pages 591-600. Springer-Verlag, Berlin, Germany, 1998.
20. A. Silva, A. Neves, and E. Costa. Sappo: A simple, adaptable, predator prey optimiser. In Progress in Artificial Intelligence, volume 2902 of Lecture Notes in Artificial Intelligence, pages 59-73. 2003.
21. F. van den Bergh and A. P. Engelbrecht. A cooperative approach to particle swarm optimization. leee Transactions on Evolutionary Computation, 8(3):225-239, 2004.
22. R. X. Wang, Y. P. Lu, and S. M. Wang. Comparative evaluation of 11 scoring functions for molecular docking. Journal of Medicinal Chemistry, 46(12):2287-2303, 2003.
23. J. M. Yang and C. C. Chen. Gemdock: A generic evolutionary method for molecular docking. Proteins-Structure Function and Bioinformatics, 55(2):288-304, 2004.