Qualitative pharmacophore models

Often, all alignment-based methods and molecular field and potential calculations are classified as pharmacophore perception techniques. We will include most of these methods in this review however, when using the term pharmacophore model, we will be referring mainly to one specific type of perception, namely three-dimensional feature-based pharmacophore models represented by geometry or location constraints, qualitative or quantitative. An extrapolation of the pharmacophore approach to a set of multi-dimensional descriptors (pharmacophore fingerprints) has been developed mostly for library design and focusing purposes [3-8]. 

The pharmacophore models built in MOE are qualitative. There is no possibility of using the SAR of a set of molecules in the building of the models. 

In Table 1, information about the origin and the characteristics of the different types of pharmacophores that can be generated is summarized. In their great majority, pharmacophore models are qualitative tools, but some methods can associate experimental activity values of the molecules in the building process to derive quantitative pharmacophore models. Examples of these are HASL, APEX, and HypoGen. Conformer generation is often a prerequisite, especially when working with flexible molecules. 

P., Roussis, V., and Tafi, A. (2009) Pharmacophore modeling for qualitative prediction of antiestrogenic activity. Journal of Chemical Information and Modeling, 49, 2489-2497. 

Another way to construct pharmacophore models when the structure of a receptor is not known is to use a ligand-based approach. Here, one does not try to predict the structure of the receptor but focuses on analyzing similar and dissimilar features among a number of ligands that are known to act on a receptor. A long-used qualitative method is to simply draw the structure of a number of inhibitors along with their measured activity levels. 

Catalyzed N-Dealkylation Reactions and Qualitative Metabolite Predictions Using a Combined Protein and Pharmacophore Model for CYP2D6. 

Figure 13.11 Overview diagram of the NCTR Four-Phase approach for priority setting. In Phase I, chemicals with molecular weight < 94 or > 1000 or containing no ring structure will be rejected. In Phase II, three approaches (structural alerts, pharmacophores, and classification methods) that include a total of 11 models are used to make a qualitative activity prediction. In Phase III, a 3D QSAR/CoMFA model is used to make a more accurate quantitative activity prediction. In Phase IV, an expert system is expected to make a decision on priority setting based on a set of rules. Different phases are hierarchical different methods within each phase are complementary.

Some hits also revealed sufficient selectivity of type 1 inhibition versus the type 2 isoform, which is advantageous for the side-effect profile of these compounds. Comparison of the model for llp-HSDl inhibitors with the X-ray crystal structure (which was published shortly after model generation and VS) showed good correlation of the chemical features responsible for ligand binding. In another study, a combination of common feature-based qualitative and quantitative models was used as 3D pharmacophore search query to successfully detect novel endothelin-A antagonistic lead structures. 

---