Evolutionary computation and multimodal search: A good combination to tackle molecular diversity in the field of peptide design |
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Authors: | Ignasi Belda Sergio Madurga Teresa Tarragó Xavier Llorà Ernest Giralt |
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Affiliation: | (1) Institut de Recerca Biomèdica, Parc Científic de Barcelona, Universitat de Barcelona, Josep Samitier, 1-5, E 08028 Barcelona, Spain;(2) Department de Química Física, Universitat de Barcelona, E 08028 Barcelona, Spain;(3) Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois, Urbana, IL 61801, USA;(4) Department de Química Orgànica, Universitat de Barcelona, E 08028 Barcelona, Spain |
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Abstract: | Summary The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space. |
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Keywords: | evolutionary algorithms molecular diversity multimodal optimization peptide design |
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