GOLPE variable selection

Cruciani, G. and Watson, K.A. Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. 

Cmciani, G., Watson, K. Comparative Molecular Field Analysis Using GRID Force Field and GOLPE Variable Selection Methods in a Study of Inhibitors of... 

Generating Optimal Linear PLS Estimations = GOLPE variable selection > genetic algorithm - variable subset selection variable selection... 

Two variable selection methods described in the introduction to PLS, the r2-guided region selection method and GOLPE, have been applied to CoMFA, but only a few direct comparisons are available. GOLPE variable selection - has led to PLS models with higher cross-validated r values but no more accurate predictions. Comparison of models derived from traditional PLS with those using domain-selected or GOLPE-selected variables shows improve-... 

The variable selection methods have been also adopted for region selection in the area of 3D QSAR. For example, GOLPE [31] was developed with chemometric principles and q2-GRS [32] was developed based on independent CoMFA analyses of small areas of near-molecular space to address the issue of optimal region selection in CoMFA analysis. Both of these methods have been shown to improve the QSAR models compared to original CoMFA technique. 

Among these methods, Generating Optimal Linear PLS Estimations (GOLPE) is a variable selection method for selecting by -> experimental design a limited number of - interaction energy values, aimed at obtaining the best predictive PLS models. 

The TPW variable selection is a more rapid method compared to both GA and GOLPE. A TPW selected 228 variables using A= 19 PLS factors. The result from the analysis is shown in Fig. 15. The prediction error on the unseen validation set is 2.1%. 

Note PRESS and q may relate to any property that is being modelled and not just activity , will always be smaller than r. When q > 0.3, a model is considered significant. Although cross-validation may seem a robust validation technique, some difficulties should not be overlooked. Variables that do not contribute to prediction, i.e. cause noise in the model, may have detrimental effects on CV. This may particularly play a role when many variables have to be considered, such as in a 3D-QSAR CoMFA analysis (see Chapter 25). A procedure for variable selection in the case of many variables has been developed and is named GOLPE (generating optimal linear PLS estimations). ... 

GRID provides the user with a wide variety of atomic probe types (like CoMFA) and funaional group probe types (a feature not available in CoMFA). GOLPE, as originally presented, used a variable selection technique based on... 

CoMFA is a powerful 3D QSAR method which has already shown its practical value in many cases. The results obtained depend a lot on the care that is taken in the definition of the 3D pharmacophore and in the alignment of the molecules. Soft fields seem to be better than hard fields, especially for the interpretation of the contour maps. Variable selection seems unnecessary if such fields are used. On the other hand, the new GOLPE-guided regional selection looks more promising than other variable selection procedures. A general problem is the... 

---