BCUT diversity

The 5 k actives with percent inhibition of 25 to 40% and that feU into clusters of less than five compounds were treated separately using BCUT diversity analysis, as described in Section 6.2.5. A cell-based selection biased by primary activity from six bins per each of six axes yielded 1258 compounds. The combined selection from filtering, clustering, and diversity totaled 6986 compounds representing 3337 ring scaffolds and was submitted for confirmation assays. Note that the full set of 16 k filtered actives contained 9254 ring hashcodes, so the selected set covers 36.1% of the represented scaffolds. Because of the presence of duplicate samples in the corporate screening collection, 7275 samples were pulled and assayed. 

Beno and Mason [19] describe a product-based method based on simulated annealing that simultaneously optimizes four-point pharmacophore coverage and BCUT diversity. The virtual library is preenumerated however, the four-point pharmacophores are calculated on-the-fly, that is, during the optimization itself. The approach was used to select 20 carboxylic acids and 20 amines from a virtual library of 86,140 amines (292 acids and 295 amines). The library was optimized on pharmacophore coverage simultaneously with diversity in BCUT space. They found a 20-23% increase in BCUT cell coverage and a 1.8- to 2.6-fold increase in the number of pharmacophores covered compared with randomly selected reagents. 

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]. 

Mason, J.S. and Beno, B.R. Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints simultaneous optimization and structure-based diversity. /. Mol. 

A significant percentage of any compound library will inevitably fall into small clusters unsuitable to rigorous statistical evaluation. These must be considered separately - in our case, using diversity analysis with BCUT descriptors [39] to supplement the list derived from clustering. Throughout this process, we use visualization to assess data quality, identify potential problems such as edge effects, and check trends and patterns. 

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. 

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. 

Burden, CAS, and University of Texas (BCUT) descriptors are well suited and widely used to describe diversity of a chemical population in a low dimensional Euclidian space and they allow for fast cell-based diversity selection algorithms (Pearlman and Smith, 1998). The DiverseSolutions... 

Pearlman [22] has developed novel molecular descriptors called BCUT values for use in diversity studies. They are designed to combine atomic properties with connectivity information in order to define a low-... 

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... 

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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