Abstract
Computer-aided drug design is an approach to effectively identify and analyse molecules for therapeutic and diagnostic interventions. Generally, libraries with a broad range of compounds revealing a high genetic diversity with an at most similar behavior in bioactivity have to be created. For this purpose, an evolutionary process for multi- and many-objective Molecular Optimization (MO) has been designed and improved during the past decade. Diversity plays a central role in Evolutionary Algorithms (EAs) to prevent premature convergence to suboptimal solutions and several methods to promote diversity on different levels of an EA have been proposed. The aspect of genetic diversity in MO is a further challenge that has to be controlled and promoted by different strategies on various stages of a problem-specific EA. This work presents an application-specific re-interpretation of different diversity aspects on various stages of an EA for MO. A sophisticated survival selection strategy combining a specific ranking method with application-specific diversity promoting technologies is introduced and benchmarked to the recently proposed many-objective evolutionary algorithm AnD on four molecular optimization problems with 3 up to 6 objectives.
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References
Zhou, S.F., Zhong, W.: Drug design and discovery: principles and applications. Molecules 22(2), 279 (2017)
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
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
Rosenthal, S., El-Sourani, N., Borschbach, M.: Impact of different recombination methods in a mutation-specific MOEA for a biochemical application. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds.) EvoBIO 2013. LNCS, vol. 7833, pp. 188–199. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37189-9_17
Rosenthal, S., Borschbach, M.: Impact of population size, selection and multi-parent recombination within a customized NSGA-II for biochemical optimization. Int. J. Adv. Life Sci. IARIA 6(3&4), 310–324 (2014)
Rosenthal, S., Borschbach, M.: A winning score-based evolutionary process for multi-and many-objective peptide optimization. IJCC I, 49–58 (2019)
Squillero, G., Tonda, A.: Divergence of character and premature convergence: a survey of methodologies for promoting diversity in evolutionary optimization. Inf. Sci. 329, 782–799 (2015)
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
Sneath, P.: Relations between chemical structure and biological activity in peptides. J. Theor. Biol. 12(2), 157–195 (1966)
Liu, Z.Z., Wang, Y., Huang, P.Q.: AnD: a many-objective evolutionary algorithm with angle-based selection and shift-based density estimation. Inf. Sci. 509, 400–419 (2018)
Rupakheti, C., Virshup, A., Yang, W., Beratan, D.: Strategy to discover diverse optimal molecules in the small molecule universe. J. Chem. Inf. Model 55(3), 529–537 (2015)
Emmerich, M., Lee, B., Render, A.: Analyzing molecular landscapes using random walks and information theory. Chem. Central J. 3, P20 (2009). https://doi.org/10.1186/1752-153X-3-S1-P20
Wang, H., Jin, Y., Yao, X.: Diversity assessment in many-objective optimization. IEEE Trans. Cybern. 47, 1510–1522 (2017)
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
Nebro, A., Durillo, J.: jmetal: Metaheuristic Algorithms in Java (2019)
BioJava: CookBook, r. http://www.biojava.org/wiki/BioJava
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
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Rosenthal, S. (2020). Diversity Promoting Strategies in a Multi- and Many-Objective Evolutionary Algorithm for Molecular Optimization. In: Filipič, B., Minisci, E., Vasile, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science(), vol 12438. Springer, Cham. https://doi.org/10.1007/978-3-030-63710-1_23
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