Generating optimal linear PLS estimation (GOLPE). An advanced chemometric tool for handling 3D-QSAR problems

n advanced variable selection procedure, called GOLPE, aimed at obtaining PLS regression models with the highest prediction ability is presented and illustrated with an application in 3D-QSAR. Key steps in the procedure are a preliminary variable selection by means of a D-optimal design in the loading space, and an iterative evaluation of the effects of individual variables on the model predictivity based on the validation of a number of reduced models, on variables combinations selected according to a FFD strategy. The procedure is successfully applied to a real 3D-QSAR case study: the results obtained by GOLPE are compared with those obtained by CoMFA and found to be in good agreement in terms of variable importance, but with a much higher prediction ability. Accordingly, the results encourage to think that it might be used within the CoMFA framework in the place of the present PLS version there, or in CoMFA-like studies on the structures generated by GRID probes