Clustering pharmacophores

Adenosine Modified with Boron Cluster Pharmacophores as a New Human... 

FIGURE 1.6 Selected examples of two types of specific nuclear receptor ligands (retinoic acid receptors RARs and retinoid X receptors RXRs), unmodified and modified with boron cluster pharmacophore. (Adapted from Y. Endo et al., Bioorg. Med. Chem. Lett.. 1999, 9, 3313-3318 Y. Endo et al., Bioorg. Med. Chem. Lett., 1999, 9, 3387-3392 K. Ohta et al., Bioorg. Med. Chem. Lett., 2004,14,5913-5918.)... 

Hence there are multiple solutions for the final set of 10000 compounds. The final selection can be diversity driven using for example cluster analysis based on multiple fingerprints [63], hole filling strategies by using scaffold/ring analysis (LeadScope [66], SARVision [66]) or pharmacophore analysis [67, 68]. For a review of computational approaches to diversity and similarity-based selections, see the paper of Mason and Hermsmeier [69] and the references therein. 

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. 

The final library was designed with the purpose of adding incremental diversity to the first three fragment libraries. The main filtering criterion was novel pharmacophoric triangles not found in the first three libraries. After clustering and visual inspection from a panel of medicinal chemists, only 65 compounds were purchased and 61 compounds passed QC. 

Fig. 3.10 SQUID fuzzy pharmacophore model for COX-2 inhibitors. Using a larger cluster radius results in more general models. From left to right 1 A, 1.5 A, 2.5 A, 3.5 A.

Fig. 8.3 Concept of PhDOCK. Conformers of similar molecules are aligned on the largest common pharmacophore. The pharmacophore of each cluster is consequently matched on spheres in the binding pocket labeled with pharmacophoric properties.

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