Pharmacophore validation

Optimize volume overlap for every combination of conformers. Analyze the models with the highest overlap volume 

Each of the automated techniques we have described has its own strengths and weaknesses. The 3D key approach provided within ChemDBS-SD o is attractive, because all stages of the process, from conformational analysis through model building, are handled automatically. The technique tends to produce many models, however. This is not necessarily a problem, but it does mean that more false positives will need to be weeded out during the validation 

Overall, it is clear that a good degree of variation exists in the techniques used to automatically determine potential pharmacophores. As a consequence, many of the techniques have the potential to extract models that could easily be missed using a different approach. The use of a variety of techniques is thus recommended if one is to maximize the chance of finding the best binding model. 

Whereas such qualitative approaches are valuable, it would be useful if they could be augmented with techniques that quantitatively link the binding models with ligand binding data. Recent progress in QSAR methodology has provided tools to allow us to do just that. With the advances in statistical techniques applied to QSAR problems in the late 1980s, the technique of Comparative Molecular Field Analysis (CoMFA) was developed.The 

A number of alternative 3D QSAR techniques have been advanced that also show potential in the area of pharmacophore development. Good et al. have developed a technique that utilizes molecular similarity matrices to derive 

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The PLS pseudo-coefficients profile of the third component of the PLS model, highlights the descriptors that have a greater importance in the chemometric model. The most important 3D-pharmacophoric descriptors in the PLS model suggest a common pharmacophore for all the substrates. The activity increases strongly in molecules ivith a high value of the descriptors 33-23, 11-33, 13-8, 14-41, 44-43. The descriptors are explained in detail in Table 9.1. The most important descriptors in the PLS model can be arranged to obtain an approximate pharmacophore valid for molecules actively transported by Pgp. The pharmacophore consists of two H-bond acceptor groups, two hydrophobic areas and the size of the molecule that plays a major role in the interaction (Fig. 9.3). 

The validity of pharmacophore models that overlay the classical can-nabinoids and AAIs has been brought into question by recent mutation work on the CBi receptor. It was found that mutation of Lys-192 in the third... 

Comparative Molecular Field Analysis (CoMFA), 6 16 16 755-757 pharmacophore generation and validation, 6 12 Comparative Molecular Shape Indexes Analysis (CoMSIA), J0 327t,... 

Computing properties, defined, 76 729-730 CoMSIA, pharmacophore generation and validation, 6 12 Concavalin A (Con A), 9 66-67 Concave receptor, 76 774 Concentrate, defined, 76 127 Concentration... 

Schuster, D., Laggner, C., Steindl, T.M. and Langer, T. (2006) Development and validation of an in silico P450 profiler based on pharmacophore models. Current Drug Discovery Technologies, 3, 1—48. 

As illustrated in the next section, the use of biological fingerprints, such as from a BioPrint profile, provides a way to characterize, differentiate and cluster compounds that is more relevant in terms ofthe biological activity of the compounds. The data also show that different in silico descriptors based on the chemical structure can produce quite different results. Thus, the selection of the in silico descriptor to be used, which can range from structural fragments (e.g. MACCS keys), through structural motifs (Daylight keys) to pharmacophore/shape keys (based on both the 2D structure via connectivity and from actual 3D conformations), is very important and some form of validation for the problem at hand should be performed. 

Validation of Antitarget Pharmacophore Models 6.3.3.1 Virtual Screening Hit Rates and Yields... 

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