TOPP Triplets of Pharmacophoric Points

The base theory underpinning TOPP is similar to the FLAP approach, previously described in this chapter. First, atoms in the molecules are classified by the GRID force field parametrization. In this way, atoms are described according 

The kinds of calculations described above are done for all the molecules under investigation and then all the data (combinations of 3-point pharmacophores) are stored in an X-matrix of descriptors suitable to be submitted for statistical analysis. In theory, every kind of statistical analysis and regression tool could be applied, however in this study we decided to focus on the linear regression model using principal component analysis (PCA) and partial least squares (PLS) (Fig. 4.9). PCA and PLS actually work very well in all those cases in which there are data with strongly collinear, noisy and numerous X-variables (Fig. 4.9). 

Applying this procedure to investigation of the metabolic stability of CYP2D6, we were able to find a model to correctly classify metabolically stable and unstable compounds. This model was trained using a set of 129 compounds from the Bio-Print [31] database. Drug-likeness and solubility properties were used as primary filter in order to eliminate unattractive compounds and all those compounds classified as not soluble, which are always classified as metabolically stable. The data- 

Using this model as imtial filter in the very early stages of dmg discovery, for example in library design and virtual screening, could be very useful in order to decrease the large amount of compounds to be tested in vitro or in vivo. 

FLAP 4-Point Pharmacophore Fingerprints from GRID 

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