CPCA

AAHa6aTH[Pg.126]

Rocha J.R. Freitas R.F. Montanari C.A. The GRID/CPCA approach in drug discovery. Expert Opinion on Drug Discovery, 2010, 5 (4), 333-346. 

FIGURE 6.7 Score plot and 97.5% tolerance ellipse of the Hawkins-Bradu-Kass data obtained with (a) CPCA and (b) MCD [44],... 

In the last few years, a number of publications have demonstrated that the GRID/PCA or GRID/CPCA methods can be successfully applied to characterize the structural differences between protein binding sites, and to identify differences in the protein-ligand interactions as well as the regions on the target enzymes which mediate highly selective interactions [4—17]. 

In this chapter, we will first review the basic principles of GRID/PCA and GRID/CPCA, highlighting some important technical aspects necessary for their successful application. Some space will be given to the discussion of the differences between PGA and the (hierarchical) GPGA methods, before their application to various selectivity problems will be summarized. 

Table 3.1 Overview of PCA/CPCA models described in the literature and discussed in this review. 

Figure 3.4. Data collection for GRID/CPCA Starting from the GRID calculations for one probe, a one-dimensional vector containing all interaction energies at the k grid points is constructed. Then the vectors for the m probes are compiled into one long vector which contains (kx m) data points. The final X matrix is built by stacking these vectors for every target protein.

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