A Multi-objective Genetic Algorithm for Peptide Optimization
The peptide-based drug design process requires the identification of a wide range of candidate molecules with specific biological, chemical and physical properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate...
|Online Access:||PDF Full Text|
No Tags, Be the first to tag this record!
|Summary:||The peptide-based drug design process requires the identification of a wide
range of candidate molecules with specific biological, chemical and physical
properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate molecules is time- and cost-intensive. Hence, in silico methods are required for this purpose. Metaheuristics like evolutionary algorithms are considered to be adequate in silico methods providing good approximate solutions to the underlying multiobjective optimization problems. The general issue in this area is the design of a multi-objective evolutionary algorithm to achieve a maximum number of high-quality candidate peptides that differ in their genetic material, in a minimum number of generations.
A multi-objective evolutionary algorithm as an in silico method of discovering
a large number of high-quality peptides within a low number of generations
for a broad class of molecular optimization problems of different dimensions is
challenging, and the development of such a promising multi-objective evolutionary
algorithm based on theoretical considerations is the major contribution of this thesis. The design of this algorithm is based on a qualitative landscape
analysis applied on a three- and four-dimensional biochemical optimization
The conclusions drawn from the empirical landscape analysis of the
three- and four-dimensional optimization problem result in the formulation of hypotheses regarding the types of evolutionary algorithm components which
lead to an optimized search performance for the purpose of peptide optimization.
Starting from the established types of variation operators and selection strategies,
different variation operators and selection strategies are proposed and
empirically verified on the three- and four-dimensional molecular optimization
problem with regard to an optimized interaction and the identification of
potential interdependences as well as a fine-tuning of the parameters. Moreover,
traditional issues in the field of evolutionary algorithms such as selection
pressure and the influence of multi-parent recombination are investigated.|
|Physical Description:||287 Pages|