Spress

The maximum number of latent variables is the smaller of the number of x values or the number of molecules. However, there is an optimum number of latent variables in the model beyond which the predictive ability of the model does not increase. A number of methods have been proposed to decide how many latent variables to use. One approach is to use a cross-validation method, which involves adding successive latent variables. Both leave-one-out and the group-based methods can be applied. As the number of latent variables increases, the cross-validated will first increase and then either reach a plateau or even decrease. Another parameter that can be used to choose the appropriate number of latent variables is the standard deviation of the error of the predictions, SpREss ... 

Figure 4-12 [ lie Potential Energy Form for -Butane. Hie energy is e.spressed relative to... 

Spress Standard deviation sum of the squared errors of prediction... 

The numbers of partial least squares (PLS) components were higher in CoMSIA than in CoMFA. This difference probably resulted from the significantly higher number of lattice points showing steadily varying field values (e.g., inside the molecules). The optimal numbers of components were selected on the basis of lowest Spress. 

Compressihle starch Instastarch- Lycatab C Lycatab PGS Merigel National 78-1551 Pharma-Gel Prejel Sepistab ST 200 Spress B820 Starch 1500 G Tablitz Unipure LD Unipure WG220. 

Tel +44 (0)20 7735 2425 Fax +44 (0)20 7735 4408 E-mail paroxite clara.co.uk Trade names Albagel EmCon CO Fancol Hygum TP-1 Phenoxen Pure-Dent Pure-Dent B851 Spress B820 Waglinol 6014. 

A real advance resulted in 1987 [35] the method was still named DYLOMMS, but now it used grids including several thousands of points, PLS analysis and, most important, a cross-validation procedure (see chapter 5.3) to check the predictive ability of different models. An excellent result was obtained for the binding of steroids to the corticosteroid-binding globulin (5 components n = 20 rFiT = 0.992 SpiT = 0.045 rpREss = 0.860 Spress = 0.434) and other binding, uptake, and enzyme inhibition data. 

CoMFA and related 3D QSAR approaches have been applied to correlate various physicochemical properties. Equilibrium constants of the hydration of carbonyl groups could be explained by a combination of C=0 bond order, steric, and electrostatic fields [1005]. 3D QSAR studies that correlate a, inductive, and resonance parameters of benzoic acids [1015, 1016] as well as pKg values ofclonidine analogs [1017] show that a H " field precisely describes such electronic parameters, e.g. (Jm.p of benzoic acids (n = 49 rpir = 0.976 snr = 0.082 Spress = 0.093). Steric parameters of benzoic acids, like surface area and van der Waals volume can be described by a steric field alone, while values of acetic acid methyl esters need a combination of both steric and electrostatic fields (n = 21 rpix = 0.984 Sfit = 0.133 SpREss = 0.209) [1016]. 

The problems of this data set are easily understood if a Free-Wilson analysis is applied. " The training set compounds ( 1-21) can be described by a simple one-parameter regression equation (equation 1 the term 4,5-C=C- indicates the presence or absence of a cycloaliphatic 4,5-double bond in ring A of the steroids). The internal predictivity of this model (Q = 0.726 spress = 0.630) and the test set predictivity (n = 10 = 0.477 spress = 0.733) are even slightly... 

Despite the worse fit and internal predictivity, as compared with equation (1), the validity of this model is proven by its excellent test set (compounds 13-22) predictivity pnsd = 0.909 spRESs = 0.406). The differences between both models, especially in their test set predictivity, provide striking evidence for the influence of the training and test set selections on the obtained results. Thus, a careful selection of the training set molecules is of utmost importance. A broad variety of structural features should be included in these molecules, in order to allow reliable predictions for the test set compounds. 

SDEP (the standard deviation of the error of predictions) corresponds to jpress but the number of degrees of freedom is not considered in the calculation of the SDEP value. The smallest -spress or SDEP value should be taken as the criterion for the optimum number of components. Alternatively, an increase of the value by a certain percentage, e.g., 5%, may be defined as the criterion to accept a further PLS component. As long as only significant components are extracted in the PLS analysis, PRESS, SDEP and Jpress will decrease if too many components are derived, overprediction results and PRESS, SDEP and spress increase. 

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