Comparison of commercially available genetic algorithms: gas as variable selection tool.
Many commercially available software programs claim similar efficiency and accuracy as variable selection tools. Genetic algorithms are commonly used variable selection methods where most relevant variables can be differentiated from 'less important' variables using evolutionary computing techniques. However, different vendors offer several algorithms, and the puzzling question is: which one is the appropriate method of choice? In this study, several genetic algorithm tools (e.g. GFA from Cerius2, QuaSAR-Evolution from MOE and Partek's genetic algorithm) were compared. Stepwise multiple linear regression models were generated using the most relevant variables identified by the above genetic algorithms. This procedure led to the successful generation of Quantitative Structure activity Relationship (QSAR) models for (a) proprietary datasets and (b) the Selwood dataset.
Schefzick
Sabine
S
Pfizer Global Research and Development, Discovery Technologies, Ann Arbor Laboratories, 2800 Plymouth Road, Ann Arbor, MI 48105, USA. sabine.schefzick@pfizer.com
Bradley
Mary
M
D000465:Algorithms; D002851:Chromatography, High Pressure Liquid; D005740:Gases / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D005740:Gases
J Chem Inf Comput Sci. 2002 Jul-Aug;42(4):927-36
12132894
J Chem Inf Comput Sci. 2000 Sep-Oct;40(5):1160-8
11045809
J Chem Inf Comput Sci. 2004 Jan-Feb;44(1):1-12
14741005
J Med Chem. 2002 Aug 29;45(18):3844-53
12190308
J Theor Biol. 1990 Nov 7;147(1):59-84
2277505
J Med Chem. 1964 Nov;7:691-4
14262788
J Med Chem. 2001 Sep 27;44(20):3223-30
11563921
J Comb Chem. 2004 Nov-Dec;6(6):916-27
15530119
J Med Chem. 1990 Jan;33(1):136-42
2296013