Descriptors of chemical structures

A more comprehensive description of the Optiverse library design process,4,5 discussion of some of the descriptors of chemical structure and properties,8 and the validation methods used to ensure how well the descriptors represented molecular diversity9 appear elsewhere. However, for clarity, some of the relevant factors associated with these are discussed in this section. Note also that explicit discussions of Panlabs experimental chemistry are described elsewhere.10... 

Descriptors of chemical structure, the independent variables, or x variables can come from a variety of sources and may be measured, estimated, or calculated as discussed in Chapters 3 to 6. It is quite common these days to use many descriptors in a QSAR study, often many more variables than there are compounds (cases) in the set. This leads to the need for dimension reduction, variable elimination and variable selection. In the early days of QSAR modeling, the 1960s and 1970s, there was little need for any of these methods since the descriptors used were generally tabulated... 

For illustration, we shall consider here one of the nonlinear variable selection methods that adopts a k-Nearest Neighbor (kNN) principle to QSAR [kNN-QSAR (49)]. Formally, this method implements the active analog principle that lies in the foundation of the modern medicinal chemistry. The kNN-QSAR method employs multiple topological (2D) or topographical (3D) descriptors of chemical structures and predicts biological activity of any compound as the average activity of k most similar molecules. This method can be used to analyze the structure-activity relationships (SAR) of a large number of compounds where a nonlinear SAR may predominate. 

BCUT Descriptors. Descriptors of chemical structure that are derived from an eigen analysis of the connection table of the structure. The class of BCUT descriptor depends on the quantities that are stored in the table (simple connection information versus electronic or steric interaction values). BCUT descriptors have found value in molecular diversity and chemical library design. 

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). 

The principal relationships between descriptors of chemical structures in a large array of compounds can be demonstrated. The correlation matrix (Table 1.5) obtained for a variety of parameters for several hundred compounds in more than 20 chemical classes (Nendza and Russom, 1991) reveals that hardly any descriptor is truly independent and that several highly significant correlations result. 

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).

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