Selection cluster-based Compound

In dissimilarity-based compound selection the required subset of molecules is identified directly, using an appropriate measure of dissimilarity (often taken to be the complement of the similarity). This contrasts with the two-stage procedure in cluster analysis, where it is first necessary to group together the molecules and then decide which to select. Most methods for dissimilarity-based selection fall into one of two categories maximum dissimilarity algorithms and sphere exclusion algorithms [Snarey et al. 1997]. 

Many different methods have been developed for compound selection. They include clustering, dissimilarity-based compound selection, partitioning a collection of compounds into a low-dimensional space and the use of optimization methods such as simulated annealing and genetic algorithms. Filtering techniques are often employed prior to compound selection to remove undesirable compounds. 

Dissimilarity-based compound selection [26,27] is also based on calculating pairwise similarities however, in this case, compounds are selected directly, rather than via the two-stage process described for clustering. A compound is chosen to seed the subset then an iterative procedure is begun where, in each iteration, the next compound to be added to the subset is the one remaining in the dataset that is most dissimilar to those already included in the subset. 

Methods for selecting diverse subsets from a compound collection include (1) dissimilarity-based compound selection, (2) clustering, (3) partitioning, and... 

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. 

Since diversity is a collective property, its precise quantification requires a mathematical description of the distribution of the molecular collection in a chemical space. When a set of molecules are considered to be more diverse than another, the molecules in this set cover more chemical space and/or the molecules distribute more evenly in chemical space. Historically, diversity analysis is closely linked to compound selection and combinatorial library design. In reality, library design is also a selection process, selecting compounds from a virtual library before synthesis. There are three main categories of selection procedures for building a diverse set of compounds cluster-based selection, partition-based selection, and dissimilarity-based selection. 

The cluster-based selection procedure starts with classifying compounds into clusters of similar molecules with a clustering algorithm followed by selection of representative(s) from each cluster (24). On the other hand, the partition-based selection procedure partitions chemical space into cells by dividing values of each dimension into various intervals and selects representative... 

Given the variety of different descriptors and subset selection methods that are available, several studies have been carried out in an attempt to validate both the compound selection methods and the various descriptors. To some extent the choice of descriptors and subset selection methods are interlinked. For example, partitioning schemes are restricted to low-dimensional descriptors such as physicochemical descriptors, whereas clustering and dissimilarity-based methods can be used with high dimensional descriptors such as fingerprints. 

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