Library design cell based

Schnur, D.M. Design and diversity analysis of large combinatorial libraries using cell-based methods. /. Chem. Inf. Comput. Sci. 1999, 39, 36-45. 

Schnur, D. (1999). Design and Diversity Analysis of Large Combinatorial Libraries Using Cell-Based Methods. J.Chem.Inf.Comput.ScL, 39,36-45. 

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]. 

Schnur, D. and Venkatarangan, P. Applications of cell-based diversity methods to combinatorial library design. In Combinatorial Library Design and Evaluation, Ghose, A.K. and ViswADHAN, V.N. (Eds). Marcel Dekker, Inc, New York, 2001, 473-501. 

SELECT has been designed to allow optimization of a variety of different objectives. Diversity (and similarity) is optimized using functions either based on pairwise dissimilarities and fingerprints or using cell-based measures. The physicochemical properties of libraries are optimized by minimizing the dif-... 

Many library design methods require that the size (number of products) and configuration (numbers of reactants selected for each component) of the library are specified upfront. However, it is often difficult to determine optimum values a priori and usually there is a trade-off between these criteria and the other criteria to be optimized. Consider the design of a library where the aim is to maximize coverage of some cell-based chemistry space. It is clear that as more products are included in the library the chance of occupying more cells increases. Thus, an optimal library is likely to be one that represents a compromise in size and diversity. 

Compound selection is a core process of library design, and three main methods can be mentioned. Dissimilarity-based methods select compounds in terms of similar-ity/distance between individuals in chemical space. Clustering methods first group compounds into clusters based on similarity/distance and then choose representative compounds from different clusters. Partitioning methods first create a uniform cell space that subdivides the chemical space, then assign all virtual compounds to the relative cells according to their properties, and finally choose representative compounds from different cells. 

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