GA strategy for variable selection in QSAR studies: Application of GA-based region selection to a 3D-QSAR study of acetylcholinesterase inhibitors

Comparative molecular field analysis (CoMFA) with partial least squares (PL S) is one of the most frequently used tools in three-dimensional quantitati ve structure-activity relationships (3D-QSAR) studies. Although many succes sful CoMFA applications have proved the value of this approach, there are s ome problems in its proper application. Especially, the inability of PLS to handle the low signal-to-noise ratio (sample-to-variable ratio) has attrac ted much attention from QSAR researchers as an exciting research target, an d several variable selection methods have been proposed. More recently, we have developed a novel variable selection method for CoMFA modeling (GARGS: genetic algorithm-based region selection), and its utility has been demons trated in the previous paper (Kimura, T., et al. J. Chem. Inf: Comput. Sci. 1998, 38, 276-282). The purpose of this study is to evaluate whether GARGS can pinpoint known molecular interactions in 3D space. We have used a publ ished set of acetylcholinesterase (AChE) inhibitors as a test example. By a pplying GARGS to a data set of AChE inhibitors, several improved models wit h high internal prediction and low number of field variables were obtained. External validation was performed to select a final model among them. The coefficient contour maps of the final GARGS model were compared with the pr operties of the active site in AChE and the consistency between them was ev aluated.