Drug discovery: Exploring the utility of cluster oriented genetic algorithms in virtual library design

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

Volumn 1, Issue , 2005, Pages 668-675

  Sharma, B ^a   Parmee, I ^a   Whittaker, M ^b   Sedwell, A ^b  

^a UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

^b EVOTEC UK LTD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER-ORIENTED GENETIC ALGORITHMS (COGA); DRUG DISCOVERY; REAGENT LIBRARIES; VIRTUAL LIBRARY;

BIOASSAY; BIOTECHNOLOGY; DRUG PRODUCTS; LEARNING SYSTEMS; LIBRARIES; MOLECULES; OPTIMIZATION; VIRTUAL REALITY;

GENETIC ALGORITHMS;

EID: 27144542065     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Parmee I.C. The Maintenance of Search Diversity for Effective Design Space Decomposition using Cluster Oriented Genetic Algorithms (COGAs) and MultiAgent Strategies (GAANT), in Proc. Adaptive Computing in Engineering Design and Control, 1996, University of Plymouth, UK, pp 128-138.
2. Tsinopoulos C., McCarthy I.P. An evolutionary classification of the strategies for drug discovery. Manufacturing Complexity Network Conference, Cambridge, 2002, pp 373-385.
3. Milne G. W. Mathematics as a basis for chemistry. J. Chem. Inf Comput Sci., 1997, 37(4), pp 639-44.
4. Parrill, A. Evolutionary and genetic methods in drug design, Drug Discovery Today 1996, 1, 514-521.
5. Brown, R.D. and Clark, D.E. Genetic Diversity: Application of evolutionary algorithms to combinatorial library design. Expert Opinion on Therapeutic Patents 1998, 8, pp 1447-1460.
6. Agrafiotis D, Lobanov V.S, Salemme F; Combinatorial Informatics in the post Genomic Era. Nat Rev Drug Discov. 2002 May, 1(5), pp 337-46.
7. Lobanov V. S, Agrafiotis D K. Scalable Methods for the Construction and Analysis of Virtual Combinatorial Libraries, Combin. Chem. and High-Throughput Screen., 2002, 5, pp 167-178
8. Sheridan R.P., Kearsley S.K. Using a genetic algorithm to suggest combinatorial libraries. J. Chem Informatics Comp. Sci., 1995, 35, pp 310-320
9. Singh J, Ator M.A., Jaeger E.P. et al. Application of genetic algorithms to combinatorial synthesis: A computational approach to lead identification, J. Am Chem. Soc., 1996, 118, pp 1669-1676
10. Weber L, Wallabaum S, Broger C, Gubernator K: Optimization of Biological Activity of combinatorial Libraries by a genetic algorithm. Angew. Chem. Int. Ed. Engl. 1995 34, pp 2280-2282
11. V. J. Gillet, P. Willett, J. Bradshaw, and D. V. S. Green: Selecting combinatorial libraries to optimize diversity and physical properties. J. Chem. Inf. Comput. Sci. 1999, 39, pp 169-177
12. Gillet V, Khatib, Willet, P. Combinatorial Library design using a multi-objective genetic algorithm. J. Chem. Inf. Comp. Sci., 2002, 42, pp 375-385
13. Wright T, Gillet V, Green D, et al. Optimising the size and configuration of Combinatorial Libraries. J. Chem. Inf. Comp. Sci. 2003, 43, pp 381-390
14. Brown, R. D., Martin, Y. C. Designing combinatorial library mixtures using a genetic algorithm. Journal of Medicinal Chemistry, 1997, 40, pp 2304-2313.
15. Pickett S. D, McLay I. M, Clark D.E. Enhancing the hit to lead properties of lead optimization libraries. J. Chem Inf. Comput. Sci. 2000, 40, pp 263-272
16. Parmee I.C., Cvetkovic D, Watson A, Bonham C. Multi-objective satisfaction within an interactivee evolutionary design environment. Evolutionary Computation, 2000, 8, MIT Press, pp 197-222.
17. Parmee. I.C., Bonham C. R. Towards the support of innovative conceptual design through interactive designer/evolutionary computing strategies. Artificial Intelligence for Engineering Design Analysis and Manufacturing, 2000, 14, pp 3-16.
18. Parmee I.C., Abraham J.A. User Centric Evolutionary Design. Procs Design 2004, Dubrovnik, 2004 May.
19. Abraham J.A., Parmee I.C. User Centric Evolutionary Design Systems - the visualisation of emerging multi-objective design information,. Xth International Conference on Computing in Civil and Building Engineering, Weimar. 2004, June 02-04.
20. Bonham C. R., Parmee I. C. Improving the peformance of cluster oriented genetic algorithms (COGAs). IEEE Congress on Evolutionary Computation, Washington D. C, 1996, pp 554-561
21. Parmee I.C., Abraham J.A. Supporting Implicit Learning via the Visualisation of COGA Multi-objective Data. IEEE Congress on Evolutionary Computation, Portland, USA, 2004, pp 395-402.
22. Parmee I. C. Improving problem definition through interactive evolutionary computation. Artificial Intelligence in Engineering Design, Analysis and Manufacture, 2002, 16(3).