title: Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.
abstract: Modeling non-linear descriptor-target activity/property relationships with many dependent descriptors has been a long-standing challenge in the design of biologically active molecules. In an effort to address this problem, we couple the supervised self-organizing map with the genetic algorithm. Although self-organizing maps are non-linear and topology-preserving techniques that hold great potential for modeling and decoding relationships, the large number of descriptors in typical quantitative structure-activity relationship or quantitative structure-property relationship analysis may lead to spurious correlation(s) and/or difficulty in the interpretation of resulting models. To reduce the number of descriptors to a manageable size, we chose the genetic algorithm for descriptor selection because of its flexibility and efficiency in solving complex problems. Feasibility studies were conducted using six different datasets, of moderate-to-large size and moderate-to-great diversity; each with a different biological endpoint. Since favorable training set statistics do not necessarily indicate a highly predictive model, the quality of all models was confirmed by withholding a portion of each dataset for external validation. We also address the variability introduced onto modeling through dataset partitioning and through the stochastic nature of the combined genetic algorithm supervised self-organizing map method using the z-score and other tests. Experiments show that the combined method provides comparable accuracy to the supervised self-organizing map alone, but using significantly fewer descriptors in the models generated. We observed consistently better results than partial least squares models. We conclude that the combination of genetic algorithms with the supervised self-organizing map shows great potential as a quantitative structure-activity/property relationship modeling tool.
authors.affiliation: Department of Biomedical Engineering, Wake Forest University, Medical Center Blvd., Winston-Salem, NC 27157-1022, USA.
authors.forename: Ersin
authors.initials: E
authors.lastname: Bayram
authors.forename: Peter
authors.initials: P
authors.lastname: Santago
authors.forename: Rebecca
authors.initials: R
authors.lastname: Harris
authors.forename: Yun-De
authors.initials: YD
authors.lastname: Xiao
authors.forename: Aaron J
authors.initials: AJ
authors.lastname: Clauset
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Schmitt
mesh_terms: D000465:Algorithms; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship
references.citation: J Comput Aided Mol Des. 2003 Feb-Apr;17(2-4):241-53
references.pmid: 13677490
references.citation: J Med Chem. 1991 Sep;34(9):2824-36
references.pmid: 1895302
references.citation: Eur J Med Chem. 2001 Apr;36(4):349-59
references.pmid: 11461760
references.citation: Acta Biochim Pol. 2000;47(1):37-45
references.pmid: 10961676
references.citation: J Chem Inf Comput Sci. 2001 Mar-Apr;41(2):402-7
references.pmid: 11277729
references.citation: J Pharm Sci. 1975 Jul;64(7):1186-91
references.pmid: 1151681
references.citation: J Med Chem. 1996 Mar 29;39(7):1521-30
references.pmid: 8691483
references.citation: J Chem Inf Comput Sci. 2000 Nov-Dec;40(6):1356-62
references.pmid: 11128094
references.citation: Curr Med Chem. 2000 Aug;7(8):749-800
references.pmid: 10828287