Variable GOLPE

If Ej < >. it cannot be decided whether the variable is significant or not. These are the uncertain variables. GOLPE will start a new iteration to determine the status of these variables. 

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. 

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... 

Figure 10.8. G Rl D-GOLPE model for the PepTl transport system (2 latent variables, = 0.88, = 0.66). (a) Prediction vs. experimental, (b) Activity...

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. 

In order to optimise the in vitro profile, we focused our attention on the nature of the substituent at N-1 and a quantitative structure-activity study was performed on a series of N-1 alkyl derivatives. After selection of variables, the affinity for the CCK-B receptor was related to the calculated values of both lipophilicity [26] and molar refractivity [27] of the substituent and the following equation was derived using PLS analysis implemented in program GOLPE [28] (all parameters are referred to the substituents at N-1) ... 

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

Therefore, the GOLPE process is iterative where uncertain and "fixed variables are kept in the next step. The variables in step 3 above are removed. 

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). ... 

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