Self-adaptive differential evolution algorithm incorporating local search for protein-ligand docking

Title
Self-adaptive differential evolution algorithm incorporating local search for protein-ligand docking
Authors
정환원조승주이광렬이규환
Keywords
Differential Evolution; Self-adaptive; Protein-Ligand Docking; Local Search
Issue Date
2012-09
Publisher
IC-MSQUARE 2012
Abstract
Di erential Evolution (DE) algorithm is powerful in optimization problems over several real parameters. DE depends on strategies to generate new trial solutions and the associated parameter values for searching performance. In self-adaptive DE, the automatic learning about previous evolution was used to determine the best mutation strategy and its parameter settings. By combining the self-adaptive DE and Hooke Jeeves local search, we developed a new docking method named SADock (Strategy Adaptation Dock) with the help of AutoDock4 scoring function. As the accuracy and performance of SADock was evaluated in self-docking using the Astex diverse set, the introduced SADock showed better success ratio (89%) than the success ratio (60%) of the Lamarckian genetic algorithm (LGA) of AutoDock4. The self-adapting scheme enabled our new docking method to converge fast and to be robust through the various docking problems.
URI
http://pubs.kist.re.kr/handle/201004/43122
Appears in Collections:
KIST Publication > Conference Paper
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