Selection dissimilarity-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]. 

Dissimilarity-based compound selection (DECS) methods involve selecting a subset of compounds directly based on pairwise dissimilarities [37]. The first compound is selected, either at random or as the one that is most dissimilar to all others in the database, and is placed in the subset. The subset is then built up stepwise by selecting one compound at a time until it is of the required size. In each iteration, the next compound to be selected is the one that is most dissimilar to those already in the subset, with the dissimilarity normally being computed by the MaxMin approach [38]. Here, each database compound is compared with each compound in the subset and its nearest neighbor is identified the database compound that is selected is the one that has the maximum dissimilarity to its nearest neighbor in the subset. 

Snarey M, Terrett NK, Willett P, Wilton DJ. Comparison of algorithms for dissimilarity-based compound selection. J Mol Graph Model 1997 15 372-85. 

Lajiness, M.S. Dissimilarity-based compound selection techniques. Perspect. Drug Discov. Des., 1997, 7/8, 65-84. 

Gillet, V. J. and Willett, P. (2001) Dissimilarity-based compound selection for library design. In Combinatorial library design and evaluation. Principles, software tools and applications in drug discovery, Ghose, A. K. and Viswanadhan, A. N. (eds.), Marcel Dekker, New York, pp. 379-398. 

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 (DBCS) involves identifying directly the subset comprising the n most dissimilar compounds in a database containing N compounds, where typically n< N. Identification of the most dissimilar subset is not computationally feasible since it requires consideration... 

Holliday JD, Willet P, Definitions of dissimilarity for dissimilarity-based compound selection, J. Biomolec. Screening, 1 145-151, 1996. 

Pickett et al. [68] describe a program, DIVSEL, for selecting reactants while taking account of the pharmacophoric diversity that exists in the final products. They describe a 2-component library where the reactants in one pool are fixed and a subset of reactants is to be selected from the second pool. The virtual library is enumerated and a pharmacophore key is generated for each of the product molecules. Reactants are selected from the second pool using a dissimilarity-based compound selection process that represents a candidate reactant by a pharmacophore key that covers an ensemble of products. 

Holliday, J.D. and Willett, P. Definitions of Dissimilarity for Dissimilarity-Based Compound Selection.J.BiomolecularScreening, 1996,1, 145-151. 

Snarey, M., Terrett, N.K., Willett, P. and Wilton D.J. Comparison of Algorithms for Dissimilarity-Based Compound Selection. J. Mol. Graph. Modelling, 1997,15, 372-385. 

This approach is particularly efficient when combined with the Cosine coefficient (69) and was used by Pickett et al. in combination with pharmacophore descriptors (70). In lower dimensional spaces the maxsum measure tends to force selection from the comers of diversity space (6b, 71) and hence maxmin is the preferred function in these cases. A similar conclusion was drawn from a comparison of algorithms for dissimilarity-based compound selection (72). 

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. 

Gillet VJ, Willett P. Dissimilarity-based compound selection for library design. In Ghose AK, Viswanadhan VN, eds. Principles, Software Tools and Applications in Drug Discovery. New York Marcel Dekker, 2001 379-398. 

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