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Massive docking of flexible ligands using environmental niches in parallelized genetic algorithms
Author(s) -
Thormann Michael,
Pons Miquel
Publication year - 2001
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.1146
Subject(s) - computer science , curse of dimensionality , workload , genetic algorithm , algorithm , autodock , artificial intelligence , machine learning , in silico , chemistry , biochemistry , gene , operating system
Abstract Virtual screening of large libraries of small compounds requires fast and reliable automatic docking methods. In this article we present a parallel implementation of a genetic algorithm (GA) and the implementation of an enhanced genetic algorithm (EGA) with niching that lead to remarkable speedups compared to the original version AutoDock 3.0. The niching concept is introduced naturally by sharing genetic information between evolutions of subpopulations that run independently, each on one CPU. A unique set of additionally introduced search parameters that control this information flow has been obtained for drug‐like molecules based on the detailed study of three test cases of different complexity. The average docking time for one compound is of 8.6 s using eight R10,000 processors running at 200 MHz in an Origin 2000 computer. Different genetic algorithms with and without local search (LS) have been compared on an equal workload basis showing EGA/LS to be superior over all alternatives because it finds lower energy solutions faster and more often, particularly for high dimensionality problems. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1971–1982, 2001