Interaction Pharmacophore Elements

Fig. 6. The spheres corresponding to positive interaction pharmacophore element (IPE) represent regions that will be beneficial to the QSAR model. The sphere corresponding to negative IPEs indicate the region(s) where the particular IPE will degrade the QSAR model.

The results of the 4D-QSAR case study are interesting and provide large amounts of data about the system of interest, and, unlike static 3D-QSAR methods (CoMFA and SOMFA), 4D-QSAR is able to provide the exact locations of statistically important interaction pharmacophore elements. The ability of this method to overcome the question of What conformation to use and predict the bioactive conformation is impressive and a major reason to use the software. Yet it is the ability to construct manifold models and examine several models for the same alignment that is the true benefit of this method. Add to the list the ability to determine the best alignment scheme (based on statistical and experimental results) and this method will provide more information than one could imagine. This abundance of information is key when troubleshooting results that are not in agreement with current beliefs. 

Interaction Pharmacophore Elements 4D-Molecular Similarity Analysis... 

Then, the Main Distance-Dependent Matrix (MDDM), for each pair of Interaction Pharmacophore Elements (IPEs) is estimated. The IPEs are specific and independent groups representing molecule functionality (Table Numerical Entriesl). The seventh IPE type (HS) encodes information about the overall molecular shape since all the nonhydrogen atoms are considered. From the MDDM estimated for the HS-HS pair, a similarity measure with respect to the whole molecule is obtained. 

In many respects the fragment approach is ideally suited to projects which have X-ray crystal structures available. The fragments are small and relatively weak binders, but they often only possess one pharmacophoric element that binds to a specific feature on the target. If this interaction is identified by X-ray structure determination, then project teams can propose specific plans which maintain that critical interaction, and ideally optimize binding through other vectors in their fragments. 

Attempts to build predictive models based on common pharmacophoric elements have had some modest success. In the most general sense, a basic nitrogen atom which is substituted by aromatic or otherwise hydrophobic groups is a clearly problematic motif [73,74]. However, there are many compounds which interact with hERG which do not contain these features, and newer pharmacophore models have been proposed... 

In each structure, the assembled components satisfy the chemical descriptors specified while maintaining steric and electrostatic complementarity with the active site. The functional groups (in bold) interact with the receptor pharmacophoric elements as described in Figure 8.16. 

Fig. 5. Pharmacophoric element selection for ( —Fepibatidine (2). The nitrogen atoms (dark blue) are pharmacophoric elements (1) and (2), and elements (3) and (4) are points on the receptor (red) with which elements (1) and (2) interact.

A problem that often occurs in molecular modeling of bioactive compounds is to define the geometry adopted by a pharmacophore. A pharmacophore is a set of atoms or groups that are present in each bioactive molecule and are required for activity. Often it is assumed that molecules that have the same biological activity interact at the same receptor by presenting the pharmacophore elements to the receptor in the same way. The question, especially in the absence of a three-dimensional structure for the receptor, is whether a unique... 

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