Intercorrelation structures

Buffered acetolysis of tosylate 420 gives diene 421 as the major product along with unrearranged acetate. In buffered formolysis, the cis formate 422 evolves as the principal component. The structural assignments were confirmed by the chemical intercorrelations shown... 

Fig. 2a-c. The strongest intercorrelations among investigated topological indices, a average value for correlation coefficients obtained in the three series of structures b correlation coefficients for the polyalkylcyclohexanes with 6-10 carbon atoms c correlation coefficients for the polyalkylcyclo- hexanes with 10 carbon atoms... 

In order to obtain reliable QSAR the design of the series must adhere to the following requirements. The variance of all the variables should be high in order to establish the sensitivity of the biological activity to the structural parameters. The variation of the parameters should be systematic in order to minimize the effort and can be guided by computations (9) or by operational schemes (225) The parameters should be characterized by the absence of intercorrelations. Intercorrelation between parameters may lead to non-unique solutions and to misinterpretation of the correlations which may result in incorrect predictions. The analysis of the physicochemical basis for the extra-thermodynamic parameters (section C) showed that in certain instances these correlations are unavoidable due to the inherent relationships between the physical properties they represent. 

Trinajstic, N., Nikolic, S., Basak, S.C. and Lukovits, I. (2001) Distance indices and their hypercounterparts intercorrelation and use in the structure-property modeling. SAR el QSAR Environ. Res., 12, 31-54. 

These three chi indexes are not highly intercorrelated. The x index encodes size and branching information. The index encodes even more specific information about skeletal branching, x increases with the increase in skeletal branching in the hexane series the x values increase in the order hexane < 3-methylpentane, < 2-methylpentane, < 2,3-dimethylbutane, < 2,2-dimethyl-butane. The Xpc index is sensitive to specific structural aspects, especially to gem and vicinal substitution patterns. The negative sign on Xpc reflects a... 

A rough summary of the structure-activity experiences with strobilurins is visualized in Fig. 13.2.8. It illustrates the complex intercorrelation network between... 

The structure descriptors are not intercorrelated to allow the recognition of the significant variables. If, for example, activity data on a set of chlorophenols are investigated with respect to their log and values, a statistically significant QSAR may be obtained with either one of the descriptors, but an assignment of the relevant descriptor is not feasible, because those are linearly intercorrelated. 

Table 1.5 Intercorrelations between descriptors of chemical structures for a set of diverse organic compounds, characterized by the correlation coefficient the second figure (in parentheses) gives the number of compounds available for each pair of parameters (modified from Nendza and Russom, 1991). 

Multiple intercorrelations between descriptors of chemical structures are illustrated best using multivariate statistics (section 3.2.2). A principal component analysis of the data set of 18 descriptors (Table 1.6, Figure 1.11) revealed that > 80% of the information content of these descriptors is expressed by four factors that explain 54.7%, 15.8%, 8.1% and 5.6% of the total variance, respectively. 

Table 1.6 Principal component (PC) analysis of descriptors of chemical structures for a set of diverse organic compounds (a) > 80% of the explained variance is expressed in the first four PCs (b) the loadings of the original descriptor variables in the VARIMAX rotated factor matrix reflect the grouping of the parameters (i.e. high loadings in the same PC indicate high intercorrelations between the descriptors).

The amount of information obtainable from any QSAR analysis, and hence ultimately its predictive power, largely depends on the initial design of the underlying experimental study. Only if the compounds included in the test data set are true representatives of the chemical class(es) concerned, can the maximum information be extracted from the data. The members of the training set, which are used to derive the model, should be as diverse as possible with respect to their structural features. In statistical terms this means that they should reveal minimum intercorrelation and maximum variance in the properties regarded relevant for the effects studied. 

The first method is to develop a cognitive or semantic map of the expert s knowledge using quantitative methods. This method requires the expert to complete word associations of all of die related concepts in the content domain. The intercorrelations are multi-dimensionally scaled to generate a structural map (see Jonassen, [7] for a description of this technique). This structural map would then be used as a graphical browser or concept map for accessing information in the hypertext. 

We will see that it is irrelevant whether two descriptors duplicate the same information, but what is important is whether the parts in which they differ make significant contributions to the regression. As we show, the parts in which two descriptors overlap can be eliminated thus, all that is important for collinear descriptors is to find if the residuals of their intercorrelation (parts in which they differ) are relevant for structure-property or structure-activity regression or not. To illustrate their point, Dearden et al. consider two simple regressions (that is, based on a single descriptor) from a paper by Randic and Basak on the toxicity of a series of alkyl ethers to mice [9] ... 

The structural parameters refer to the sum of parameters for R, R2 and R The partial correlation coefficients and. intercorrelation coefficients are listed in Table 23. Thus Schrader type compounds with cationic groups -S-CH CH SR or -S-CH2CH2NR2 probably bind in the same positions to acetylcholinesterase as acetylcholine. 

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