Similarity Intermolecular

Clustering methods are based solely on intermolecular similarities, and they hence provide a relative measure of the space covered by a data set rather... 

Vidal D, Thormann M, Pons M (2005) LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities. J. Chem. Inf. Model. 45 386-393. 

Fig. 3. Coverage of chemistry space by four overlapping sublibraries. (A) Different diversity libraries cover similar chemistry space but show little overlap. This shows three libraries chosen using different dissimilarity measures to act as different representations of the available chemistry space. The compounds from these libraries are presented in this representation by first calculating the intermolecular similarity of each of the compounds to all of the other compounds using fingerprint descriptors and the Tanimoto similarity index. Principal component analysis was then conducted on the similarity matrix to reduce it to a series of principal components that allow the chemistry space to be presented in three dimensions.

This chapter is concerned with some of the background theory for molecular diversity analysis and includes a discussion of diversity indices, intermolecular similarity and dissimilarity measures. The extent to which the different approaches to diversity analysis have been validated and compared is reviewed. Algorithms for the selection of diverse sets of compounds are covered in detail elsewhere in this book and are mentioned only briefly here. However, consideration is given to whether these algorithms should be applied in reactant or product space. 

The diversity of a library of compounds denotes the degree of heterogeneity, structural range or dissimilarity within the set of compounds. A number of different diversity metrics have been suggested and all are based, either directly or indirectly, on the concept of intermolecular similarity or distance. Determining the (dis)similarity between two molecules requires firstly that the molecules are represented by appropriate structural descriptors and secondly that a quantitative method of determining the degree of resemblance between the two sets of descriptors exists. 

Fig. 4. An illustration of three different distance measures used in the estimation of intermolecular similarity in a two-dimensional representation...

Mount et al. [32] describe a DBCS method that is based on a minimum spanning tree. A spanning tree is a set of edges that connect a set of objects. The objects in this method are the molecules in the subset, and each edge is labeled by the dissimilarity between the two molecules it connects. A minimum spanning tree is the spanning tree that connects all molecules in the subset with the minimum sum of pairwise dissimilarities thus, the diversity is the sum of just some of the intermolecular similarities rather than all of them as in MaxSum. A similar function has also been developed by Brown et al. [34],... 

Quantification of Intermolecular Similarity Using Substructural Parameters... 

This paper describes techniques, based on quantitative measures of intermolecular similarity, for browsing and clustering within computerised chemical information systems. In particular, their implementation within the SOCRATES chemical databank system at Pfizer Central Research, Sandwich, is described much of this work was carried out by Vivienne Winterman, a CASE student in the Department of Information Studies, Sheffield University. Although these methods have only recently been introduced into operational information systems, the basic ideas are those pioneered by Greorge Adamson s group at Sheffield. ... 

LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities. Journal of... 

The atom-mapping method provides a quantitative measure of the similarity between a pair of 3D molecules, A and B, that are represented by their interatomic distance matrices. The measure is calculated in two stages. First, the geometric environment of each atom in A is compared with the corresponding environment of each atom in B to determine the similarity between each possible pair of atoms. The resulting interatomic similarities are then used to identify pairs of geometrically related atoms, and these equivalences allow the calculation of the overall intermolecular similarity. 

In this paper, we have described two quantitative studies of the use of intermolecular similarity for the prediction of activity in large data-sets of chemical structures characterised by substructure searching screens. Our results may be summarised as follows ... 

Willett et al. [59] have noted that if the diversity of a set of compounds, Z>(A), is defined as the complement of the mean pairwise intermolecular similarity ... 

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