Cell-based partitioning

Therefore, partition/cell-based methods are preferred for such library comparison tasks. They provide a common frame of reference in which it is possible to identify voids within the chemistry space of a population. It must be emphasized that the chemistry space... 

Partitioning or cell-based methods provide an absolute measure of the chemical space covered by a collection of compounds. They are based on the definition of a low-dimensional chemistry space, for example, one based on a small number of physicochemical properties such as molecular weight, calculated logP, and number of hydrogen bond donors [45]. Each property defines an axis of the chemistry-space. The range of values for each property is divided into a set of bins, and the combinatorial product of all bins then defines the set of cells or partitions that make up the space. 

Xue L, Stahura FL, Bajorath J. Cell-based partitioning. Methods Mol Biol 2004 275 279-90. 

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]. 

DVS offers a cell-based method for assessing the diversity of compound collections. The orthogonal chemistry space is partitioned into cells, and the occupancy of each cell is easily determined. Diversity voids , or unoccupied regions of chemistry space, are identified with unoccupied cells or cells of low occupancy, which are also easily recognized. Enhancing diversity of a compound collection thus consists of filling empty and low-occupancy cells (see Figure 13.5). 

The currently perhaps most popular approach to cell-based partitioning in low-dimensional chemical spaces focuses on the so-called the BCUT metric... 

In chemoinformatics research, partitioning algorithms are applied in diversity analysis of large compound libraries, subset selection, or the search for molecules with specific activity (1-4). Widely used partitioning methods include cell-based partitioning in low-dimensional chemical spaces (1,3) and decision tree methods, in particular, recursive partitioning (RP) (5-7). Partitioning in low-dimensional chemical spaces is based on various dimension reduction methods (4,8) and often permits simplified three-dimensional representation of... 

Classification of Active Compounds by Median and Cell-Based Partitioning ... 

We have also compared MP to cell-based partitioning in a compound classification study focusing on a number of different activity classes. Both partitioning techniques were coupled to a GA and scoring function S2 (see above) was optimized. Results are summarized in Table 2. 

A brief overview of different types of molecular descriptors is given in Chapter 9 about cell-based partitioning by Xue et al. this chapter also includes a description of genetic algorithm calculations. 

Key Words Biological activity cell-based partitioning chemical descriptors classification clustering distance-based design diversity selection high-throughput screening quantitative structure-activity relationship. 

Cell-based partitioning can not only be used for compound selection but also to aid in combinatorial diversity design. In this case, a chemical descriptor space is defined and empty partitions are generated by binning. Test compounds are then enumerated on the computer based on reaction schemes and selected to evenly populate these partitions. 

In addition to cell-based partitioning, statistical partitioning methods are widely used for compound classification. One of the most popular approaches is recursive partitioning (Rusinko et al. 1999), a decision tree method, as illustrated in Figure 1.8. Recursive partitioning divides data sets along decision trees formed by sequences of molecular descriptors. At each node of the tree, a descriptor-based decision is made and the molecular data set is subdivided. For example, a chosen descriptor could simply detect the presence or absence of a structural fragment in a molecule. Alternatively, the... 

Figure 1.21. Application of a statistical partitioning algorithm (median partitioning, MP) and cell-based partitioning (based on PCA) to virtually screen compound databases. Known active compounds (or baits ) are shown in back and candidate molecules in gray (adapted from Stahura and Bajorath 2004)...

The PDQ method [21] is a partitioning scheme that is based on 3-point pharmacophores. In this scheme, each potential 3-point pharmacophore in a pharmacophore key is considered as a single cell. A molecule is mapped onto the key by identifying the 3-point pharmacophores that it contains. Thus, a molecule will typically occupy more than one cell this is in contrast to partitioning schemes based on physicochemical properties in which each molecule occupies a single cell. The pharmacophore keys for a set of molecules can be combined into an ensemble pharmacophore which is the union of the individual keys. The resulting ensemble key can be used to measure total pharmacophore coverage, to identify pharmacophores that are not represented in the set of molecules and to compare different sets of molecules. 

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