Combining Aggregation with Pareto Optimization: A Case Study in Evolutionary Molecular Design

  • Johannes W. Kruisselbrink
  • Michael T. M. Emmerich
  • Thomas Bäck
  • Andreas Bender
  • Ad P. IJzerman
  • Eelke van der Horst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5467)


This paper is motivated by problem scenarios in automated drug design. It discusses a modeling approach for design optimization problems with many criteria that can be partitioned into objectives and fuzzy constraints. The purpose of this remodeling is to transform the original criteria such that, when using them in an evolutionary search method, a good view on the trade-off between the different objectives and the satisfaction of constraints is obtained.

Instead of reducing a many objective problem to a single-objective problem, it is proposed to reduce it to a multi-objective optimization problem with a low number of objectives, for which the visualization of the Pareto front is still possible and the size of a high-resolution approximation set is affordable. For design problems where it is reasonable to combine certain objectives and/or constraints into logical groups by means of desirability indexes, this method will yield good trade-off results with reduced computational effort. The proposed methodology is evaluated in a case-study on automated drug design where we aim to find molecular structures that could serve as estrogen receptor antagonists.


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  1. 1.
    Bender, A., Glen, R.C.: Molecular similarity: a key technique in molecular informatics. Organic and Biomolecular Chemistry 2, 3204–3218 (2004)CrossRefGoogle Scholar
  2. 2.
    Branke, J., Deb, K.: Integrating user-preferences into evolutionary multi-objective optimization. In: Jin, Y. (ed.) Knowledge integration into evolutionary multiobjective optimization, Berlin, pp. 461–478 (2004)Google Scholar
  3. 3.
    Cvetkovic, D., Parmee, I.C.: Preferences and their application in evolutionary multiobjective optimization. IEEE TEC (6), 1, 42–57 (2002)Google Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Derringer, G., Suich, R.: Simultaneous optimization of several response variables. Journal of Quality Technology 12, 214–219 (1980)Google Scholar
  6. 6.
    Harrington, E.C.: The desirability function. Industrial Quality Control 21, 494–498 (1965)Google Scholar
  7. 7.
    Inuiguchi, M., Ichihashi, H., Tanaka, H.: Fuzzy programming: A survey of Recent Developments. Stochastic versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty, 45–68 (1990)Google Scholar
  8. 8.
    Lameijer, E.-W., Kok, J.N., Bäck, T., IJzerman, A.P.: The molecule evoluator: An interactive evolutionary algorithm for the design of drug-like molecules. Journal of Chemical Information and Modeling 46(2), 545–552 (2006)CrossRefGoogle Scholar
  9. 9.
    Laumanns, M., Thiele, L., Zitzler, E.: Archiving with guaranteed convergence and diversity in multi-objective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 439–447 (2002)Google Scholar
  10. 10.
    Li, Q., Bender, A., Pei, J., Lai, L.: A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification. Journal of Chemical Information and Modeling 47(5), 1776–1786 (2007)CrossRefGoogle Scholar
  11. 11.
    Lipinski, C., Lombardo, F., Dominy, B., Feeney, P.: Experimental and computational approaches to estimate solubility and permeability in drug discovery and developments settings. Advanced Drug Delivery Reviews 46(1-3), 3–26 (2001)CrossRefGoogle Scholar
  12. 12.
    Keeney, R.L., Raiffa, H.: Decision with Multiple Objectives. Wiley, NY (1976)MATHGoogle Scholar
  13. 13.
    Kruisselbrink, J.W., Bäck, T., van der Horst, E., IJzerman, A.P.: Evolutionary algorithms for automated drug design towards target molecule properties. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 1555–1562 (2008)Google Scholar
  14. 14.
    Trautmann, H., Mehnen, J.: A method for including a-priori preferences in multicriteria optimization. TR 49/2005, University of Dortmund, Germany, SFB 475 (2005)Google Scholar
  15. 15.
    Trautmann, H., Mehnen, J.: Integration of Expert’s Preferences in Pareto Optimization by Desirability Function Techniques. In: CIRP ICME 2006, Ischia, Italy, pp. 293–298 (2006)Google Scholar
  16. 16.
    Nicolaou, C.A., Pattichis, C.S.: Multi-objective de novo drug design using evolutionary graphs. Chemistry Central Journal 2008 2(suppl. 1), 7 (2007)Google Scholar
  17. 17.
    Trautmann, H., Weihs, C.: Pareto-Optimality and Desirability Indices. Department of Statistics, University of Dortmund (2004)Google Scholar
  18. 18.
    SciTegic, Inc. 10188 Telesis Court, Suite 100, San Diego, CA 92121, USA.,
  19. 19.
    Sharma, B., Parmee, I.C., Whittaker, M., Sedwell, A.: Drug discovery: exploring the utility of cluster oriented genetic algorithms in virtual library design. In: Congress on Evolutionary Computation, pp. 668–675 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Johannes W. Kruisselbrink
    • 1
  • Michael T. M. Emmerich
    • 1
  • Thomas Bäck
    • 1
  • Andreas Bender
    • 2
  • Ad P. IJzerman
    • 2
  • Eelke van der Horst
    • 2
  1. 1.LIACSLeiden UniversityLeidenNetherlands
  2. 2.LACDRLeiden UniversityLeidenNetherlands

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