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Selection Strategies for a Balanced Multi- or Many-Objective Molecular Optimization and Genetic Diversity: A Comparative Study

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Evolutionary Multi-Criterion Optimization (EMO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13970))

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Abstract

The first step in drug design is the identification and optimization of lead molecules for therapeutic and diagnostic interventions. The analysis of molecular properties requires high laboratory evaluation costs. Computer-aided drug design provides effective approaches to optimize molecules with two general aims: firstly, the identification of candidate targets with several optimized physiochemical properties. Secondly, lead libraries have to be build with a broad range of compounds revealing a high genetic diversity among themselves with an at most similar behavior in bioactivity. MOEAs are nowadays established in vitro processes for molecular optimization problems with a continuous complexity increase. Therefore, MOEAs solving multi- and many-objective optimization problems with a suitable balance of convergence and genetic dissimilarity are challenging. For this purpose, a MOEA especially evolved for molecular optimization is enhanced by optionally two balancing survival selection strategies: a Pareto-based strategy is applied on a two-dimensional indicator problem consisting of a convergence and genetic diversity measure. The second strategy uses truncation selection based on a ranking measure referring to the convergence and genetic diversity measure. These configurations are compared to the recently proposed ad-MOEA with a specific environmental survival selection for multi- and many-objective optimization on four molecular optimization problems from 3 up to 6 objectives.

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References

  1. Lansdowne, L.E.: Target identification & validation in drug discovery. Technol. Netw. (2018). https://www.technologynetworks.com/drug-discovery/articles/target-identification-validation-in-drug-discovery-312290

  2. Arya, H., Coumar, M.S.: Lead identification and optimization. Design and Development of Novel Drugs and Vaccines, pp. 31–63 (2021)

    Google Scholar 

  3. Röckendorf, N., Borschbach, M.: Molecular evolution of peptide ligands with custom-tailored characteristics. PLOS Comput. Biol. 8(12) (2012). https://doi.org/10.1371/journal.pcbi.1002800

  4. Rosenthal, S., Borschbach, M.: Design perspectives of an evolutionary process for multi-objective molecular optimization. In: Trautmann, H., et al. (eds.) EMO 2017. LNCS, vol. 10173, pp. 529–544. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54157-0_36

    Chapter  Google Scholar 

  5. Rosenthal, S., Borschbach, M.: A winning score-based evolutionary process for multi-and many-objective peptide optimization. In: Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI), pp. 49–58 (2019)

    Google Scholar 

  6. Rosenthal, S.: Diversity promoting strategies in a multi- and many-objective evolutionary algorithm for molecular optimization. In: Filipič, B., Minisci, E., Vasile, M. (eds.) BIOMA 2020. LNCS, vol. 12438, pp. 294–307. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63710-1_23

    Chapter  Google Scholar 

  7. Palakonda, V., Mallipeddi, R.: An evolutionary algorithm for multi and many-objective optimization with adaptive mating and environmental selection. IEEE Access 8, 82781–82796 (2020)

    Google Scholar 

  8. Li, H., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1), 13 (2015)

    Article  MathSciNet  Google Scholar 

  9. Palakonda, V., Mallipeddi, R.: Pareto dominance-based algorithm with ranking methods for many-objective optimization. IEEE Access 5, 11043–11053 (2017)

    Article  Google Scholar 

  10. Batista, L., Campelo, F., Guimaraes, F. and Ramirez, J.: A comparison of dominance criteria in many-objective optimization problems. In: IEEE Congress of Evolutionary Computation (CEC), pp. 2359–2366 (2011)

    Google Scholar 

  11. Tian, Y., Cheng, R., Zhang, X., Su, Y., Jin, Y.: A strengthened dominance relation considering convergence and diversity for evolutionary many-objectvie optimization. IEEE Trans. Evol. Comput. 23(2), 331–345 (2019)

    Article  Google Scholar 

  12. Ishibuchi, H., Setoguchi, Y., Masuda, H., Nojima, Y.: Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes. IEEE Trans. Neural Netw. 21(2), 169–190 (2016). https://doi.org/10.1109/TEVC.2016.2587749

  13. Li, M., Yang, S., Liu, X.: Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Trans. Evol. Comput. 18(3), 248–365 (2014)

    Article  Google Scholar 

  14. Xiang, Y., Zhou, Y., Liu, M., Chen, Z.: A vector angle-based evolutionary algorithm for unconstrained many-objective optimization. IEEE Trans. Evol. Comput. 21(1), 131–152 (2017)

    Article  Google Scholar 

  15. Maneeratana, K., Boonlong, K., Chaiyaratana, N.: Compressed-objective genetic algorithm. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 473–482. Springer, Heidelberg (2006). https://doi.org/10.1007/11844297_48

    Chapter  Google Scholar 

  16. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  17. Prlic, A., Yates, A., Spencer, E., et al.: BioJava: an open-source framework for bioinformatics (2018)

    Google Scholar 

  18. Hopp, T., Woods, K.: A computer program for predicting protein antigenic determinants. Mol. Immunol. 20(4), 483–489 (1983)

    Article  Google Scholar 

  19. Guruprasad, K., Reddy, B., Pandit, M.: Correlation between stability of a protein and its dipeptidecomposition: a novel approach for predicting in vivo stability of a protein from its primary structure. Protein Eng. 4(2), 155–161 (1990)

    Article  Google Scholar 

  20. Wang, H., Jin, Y., Yao, X.: Diversity assessment in many-objective optimization. IEEE Trans. Cybern. 47, 1510–1522 (2017)

    Article  Google Scholar 

  21. Sneath, P.: Relations between chemical structure and biological activity in peptides. J. Theor. Biol. 12(2), 157–195 (1966)

    Article  Google Scholar 

  22. Bäck, T., Schütz, M.: Intelligent mutation rate control in canonical genetic algorithms. In: Raś, Z.W., Michalewicz, M. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 158–167. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61286-6_141

    Chapter  Google Scholar 

  23. Nebro, A., Durillo, J.: jMetal: Metaheuristic Algorithms in Java (2019). http://jmetal.sourceforge.net/

  24. BioJava: CookBook4.0. https://biojava.org/wiki/BioJava%3ACookBook4.0/

  25. Rosenthal, S., Borschbach, M.: Average cuboid volume as a convergence indicator and selection criterion for multi-objective biochemical optimization. In: Emmerich, M., Deutz, A., Schütze, O., Legrand, P., Tantar, E., Tantar, A.-A. (eds.) EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII. SCI, vol. 662, pp. 185–210. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49325-1_9

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, RFH - University of Applied Sciences, Cologne, Germany

    Susanne Rosenthal

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  1. Susanne Rosenthal
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Correspondence to Susanne Rosenthal .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  2. Leiden University, Leiden, The Netherlands

    André Deutz

  3. Leiden University, Leiden, The Netherlands

    Hao Wang

  4. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  5. TH Köln, Gummersbach, Germany

    Boris Naujoks

  6. University of Exeter, Exeter, UK

    Ke Li

  7. University of Jyvaskyla, Jyvaskyla, Finland

    Kaisa Miettinen

  8. De Montfort University, Leicester, UK

    Iryna Yevseyeva

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Rosenthal, S. (2023). Selection Strategies for a Balanced Multi- or Many-Objective Molecular Optimization and Genetic Diversity: A Comparative Study. In: Emmerich, M., et al. Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol 13970. Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_35

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27249-3

  • Online ISBN: 978-3-031-27250-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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