Minimum Population Search, an Application to Molecular Docking

Antonio Bolufe-Rohler (), Alex Coto-Santiesteban (), Marta Rosa Soto () and Stephen Chen ()
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Antonio Bolufe-Rohler: Universidad de La Habana
Alex Coto-Santiesteban: Universidad de La Habana
Marta Rosa Soto: ICIMAF
Stephen Chen: York University

Revista Internacional de Gestión del Conocimiento y la Tecnología (GECONTEC), 2014, vol. 2, issue 3, 1-16

Abstract: Computer modeling of protein ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance.

Keywords: Minimun Population Search; Molecular Docking; Heuristic Algorithms; Optimization; Multi-modality (search for similar items in EconPapers)
JEL-codes: D8 D81 M1 M15 O3 O31 O32 (search for similar items in EconPapers)
Date: 2014
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