BCUT method

An alternative to the use of principal components or factor analysis is the BCUT method of Pearlman [Pearlman and Smith 1998]. In this method, three square matrices are constructed for each molecule. Each matrix is of a size equal to the number of atoms in the molecule and has as its elements various atomic and interatomic parameters. One matrix is intended to represent atomic charge properties, another represents atomic polarisabilities and the third hydrogen-bonding capabilities. These quantities can be computed with semi-empirical... 

Dissimilarity and clustering methods only describe the compounds that are in the input set voids in diversity space are not obvious, and if compounds are added then the set must be re-analyzed. Cell-based partitioning methods address these problems by dividing descriptor space into cells, and then populating those cells with compounds [67, 68]. The library is chosen to contain representatives from each cell. The use of a partition-based method with BCUT descriptors [69] to design an NMR screening library has recently been described [70]. 

In our study we compare two diversity-driven design methods (uniform cell coverage and clustering), two analysis methods motivated by similarity (cell-based analysis and cluster-classification), and two descriptor sets (BCUT and constitutional). Thus, our study addresses some of the many questions arising in a sequential screen how to choose the initial screen, how to analyze the structure-activity data, and what molecular descriptor set to use. The study is limited to one assay and thus cannot be definitive, but it at least provides preliminary insights and reveals some trends. 

Design method Descriptor set used for design BCUT Constitutional - 6 PCs Constitutional - 20 PCs... 

The UCC and clustering methods require a descriptor set—BCUT or constitutional descriptors. As our implementation of UCC requires continuous descriptors, the 46 constitutional descriptors, which include discrete counts, were also reduced to either the first 6 or the first 20 principal components (PCs). Thus, the UCC algorithm was applied to the BCUT descriptors and either 6 or 20 PCs from the constitutional descriptors. In addition to these three sets, clustering was also applied to the 46 raw constitutional descriptors. The random design requires no descriptors. 

Thus, there are eight design-method/descriptor-set combinations to compare, as shown in the first two columns of Table 1. We use UCC to measure diversity, as it provides a comprehensive assessment of coverage in all low-dimensional subsets of variables. Recall that a small value of UCC is better. Furthermore, no matter how the design is generated, UCC can be measured according to the BCUT or constitutional descriptors (6 or 20 PCs). The results are very similar for the two replicates, hence only the first replicate is reported. 

The receptor relevance of BCUT descriptors has inspired several groups to apply them in conjunction with other methods. Beno and Mason reported the use of simulated annealing to optimize library design using BCUT chemistry space and four-point pharmacophores concurrently (33) and the use of chemistry spaces in conjunction with property profiles (52). The application of such composite methods to target class library design is readily apparent. Pirard and Pickett reported the application of the chemometric method, partial least squares discriminant analysis, with BCUT descriptors to successfully classify ATP-site-directed kinase inhibitors active against five different protein kinases... 

The use of receptor-relevant BCUT chemistry spaces from DiverseSolutions (DVS) [15-19] is discussed in section 3.1. This involves the use of a subset of descriptors (atomic/molecular properties) determined to be relevant to discriminate the diversity of a large set of molecules. This method, reported by Pearlman and Smith [19], can be considered as a type of relative similarity and diversity, where the subset of properties that are... 

A recent modification to the method has been reported [18] that involves the use of a subset of the dimensions for a set of structures with similar biological activity. This subset from an activity-seeded structure-based clustering has been called the receptor-relevant BCUT chemistry-space, and was used to perform a number of validation studies [18, 19]. The... 

In addition to these fairly straightforward methods, several newer, more complex, analytical approaches have been developed. Among these are the multidimensional BCUT parameters developed by Pearlman or adaptations of the genetic algorithms and neural networks originally used for assessment of combinatorial library diversity. 

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