Distance geometry pharmacophore modeling

The ensemble distance geometry pharmacophore modeling approach of Sheridan et al. (145) provides the most direct and efficient method for... 

Figure 2. Nicotinic 3-D pharmacophore query based on the ensemble distance geometry derived model of Dixon et alP...

Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching. J Chem Inf Model 52 577-588... 

Another useful distance geometry model-building application is the elegant ensemble approach of Sheridan et al. (145), where multiple molecules are entered into a single distance bounds matrix. Intramolecular distance constraints are set as described in Section VII.A and mtermolecular distance constraints are entered to force specific intermolecular interactions to occur, for example, to superimpose a set of molecules using common functional groups. This approach is described in more detail in Section X.B on pharmacophore modeling. 

Developing a pharmacophore model using methods such as systematic conformational search, QSAR, distance geometry, and probe interaction calculations. 

Usually the 3-D structure of the enzyme/receptor is not known. In this case, receptor mapping techniques such as CoMFA and conformational analysis techniques such as systematic search and distance geometry are applied to a series of active and inactive structures to obtain a pharmacophore model for use in 3-D database searching. 

In addition to the VolSurf treatment of the GRID fields, the information from the MIF can also be transformed to obtain a pharmacophoric type of representation, which is useful in the modeling of metabolic stability, cytochrome inhibition or even the direct study of the ADME related proteins (Fig. 10.3). The Almond software [17] transforms the MIF into a distance-based representation of the molecule interaction. These parameters describe the geometry of the interaction and QSAR models can be derived where the interaction with a protein is essential. Detailed information on these descriptors is presented elsewhere in this book. 

Figure 3 Structural alignments with discrete properties. Methods are based on discrete properties using the DG algorithm (1) or clique-detection (11) as implemented in distance comparisons (DISCO), and Apex-3D. The structure representation, based on discrete properties, resorts to one atomic descriptor (I), usually the atom type, or multiple atomic or site descriptors (II). In the first method (I), the conformational analysis is restricted to the generation of molecular geometries which allow a common arrangement of selected phaimacophoric moieties present in a rigid compound used as template. In the second method (II), the conformational analysis procedure may involve a systematic enumeration of all the possible conformadons for each ligand. The search similarity is directed towards the confirmation of a predefined pharmacophore postulated by the modeler or from some classical SAR in the case of the active analog approach (1), or the automated identification of pharmacophores and bioacdve conformations (II)...

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