Activity-based compound selection

Fig. 1. Median partitioning and compound selection. In this schematic illustration, a two-dimensional chemical space is shown as an example. The axes represent the medians of two uncorrelated (and, therefore, orthogonal) descriptors and dots represent database compounds. In A, a compound database is divided in into equal subpopulations in two steps and each resulting partition is characterized by a unique binary code (shared by molecules occupying this partition). In B, diversity-based compound selection is illustrated. From the center of each partition, a compound is selected to obtain a representative subset. By contrast, C illustrates activity-based compound selection. Here, a known active molecule (gray dot) is added to the source database prior to MP and compounds that ultimately occur in the same partition as this bait molecule are selected as candidates for testing. Finally, D illustrates the effects of descriptor correlation. In this case, the two applied descriptors are significantly correlated and the dashed line represents a diagonal of correlation that affects the compound distribution. As can be seen, descriptor correlation leads to over- and underpopulated partitions.

Figure 1.22. Diversity and activity-based compound selection as part of integrated screening schemes (adapted from Stahura and Bajorath 2004)...

In addition to physical properties, substructure-based filters can be applied to reduce further the number of molecules, for instance molecules with undesirable functionality for example, reactive or toxic groups can be removed and molecules with particular features (or atoms) can be actively selected. There may be particular functionality that it is desirable to avoid due to assay format, such as fluorophores in fluorescence-based approaches. Structural features for these inclusion and exclusion criteria can be readily formulated using SMILES-based procedures and this type of substructure-based compound selection technique can also be employed in the generation of focused sets of fragment molecules. 

Brown R D and Y C Martin 1996. Use of Structure-Activity Data to Compare Structure-Base Clustering Methods and Descriptors for Use in Compound Selection. Journal of Chemia Information and Computer Science 36 572-583. 

R.D. Brown and Y.C. Martin, Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection. J. Chem. Inf. Comput. Sci., 36 (1996) 572-584. 

The CBi functional activity of a number of the imidazole-based compounds reported exceeds that measured for rimonabant (382) (Table 6.43). While marked CB1/CB2 selectivity is witnessed throughout the series, the selectivity of direct (382) analogue (470) was found to be approximately 3-fold lower than for (382) itself (Table 6.44). 

One key NMR-based study has focused on the evaluation of the dynamic properties of heparin-like hexasaccharides.20 The analysis of Tj, T2 and NOE 13C-NMR data of biologically active synthetic compounds has shown that the sulphation pattern strongly influences the internal dynamics, and supports the importance of the GAGs flexibility on the selectivity of the interaction with fibroblast growth factors. 

While not convincing from a statishcal perspective, the results in this section are consistent with a trend high-activity molecules published in the past decade of medicinal chemistry literature are more likely to be found in the large, hydrophobic and poor solubility corner of chemical property space. These results are not consistent with, for example, cell-based [41] and median-based [42] partihoning of biologically active compounds however, such analyses were performed in the presence of inactive compounds selected from MDDR[41] or ACD [42], with quite probably unrelated chemotypes. ACD, the Available Chemicals Directory [43], and MDDR, the MDL Drug Data Report [43], are databases commonly used by the pharmaceuhcal industry. 

These descriptors were calculated for all compounds in the set of interest. Finally, cell-based subset selection was performed using uniform sampling of one compound per cell, with the choice of compound within the cell weighted by activity in the primary assay. The number of bins per axis was varied to achieve the closest possible match to the desired selection size. 

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