Local Structural Changes, Global Data Views: Graphical Substructure−Activity Relationship Trailing

The systematic extraction of structure−activity relationship (SAR) information from large and diverse compound data sets depends on the application of computational analysis methods. Irrespective of the methodological details, the ultimate goal of large-scale SAR analysis is to identify most informa...

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Bibliographic Details
Published inJournal of medicinal chemistry Vol. 54; no. 8; pp. 2944 - 2951
Main Authors Wawer, Mathias, Bajorath, Jürgen
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 28.04.2011
Subjects
Algorithms
Computer Graphics
Structure-Activity Relationship
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ISSN0022-2623
1520-4804
1520-4804
DOI10.1021/jm200026b

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Summary:The systematic extraction of structure−activity relationship (SAR) information from large and diverse compound data sets depends on the application of computational analysis methods. Irrespective of the methodological details, the ultimate goal of large-scale SAR analysis is to identify most informative compounds and rationalize structural changes that determine SAR behavior. Such insights provide a basis for further chemical exploration. Herein we introduce the first graphical SAR analysis method that globally organizes large compound data sets on the basis of local structural relationships, hence providing an immediate access to important structural modifications and SAR determinants.
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ISSN:0022-2623
1520-4804
1520-4804
DOI:10.1021/jm200026b