Literature DB >> 21761918

From activity cliffs to activity ridges: informative data structures for SAR analysis.

Martin Vogt1, Yun Huang, Jürgen Bajorath.   

Abstract

The extraction of SAR information from structurally diverse compound data sets is a challenging task. One of the focal points of systematic SAR analysis is the search for activity cliffs, that is, structurally similar compounds having large potency differences, from which SAR determinants can be deduced. The assessment of SAR information is usually based on pairwise similarity and potency comparisons of data set compounds. As a consequence, activity cliffs are mostly evaluated at a compound pair level. Here, we present an extension of the activity cliff concept by introducing "activity ridges" that are formed by overlapping "combinatorial" activity cliffs between participating compounds, giving rise to ridge-like structures in activity landscapes. Activity ridges are rich in SAR information. In a systematic analysis of 242 compound data sets, we have identified well-defined activity ridges in 71 different sets. In addition, an information-theoretic approach has been devised to characterize the structural composition of activity ridges. Taken together, our results show that activity ridges frequently occur in sets of active compounds and that different categories of ridges can be distinguished on the basis of their structural content. The computational identification of activity ridges provides access to compound subsets having high priority for SAR analysis.

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Year:  2011        PMID: 21761918     DOI: 10.1021/ci2002473

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  8 in total

1.  Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account.

Authors:  Ye Hu; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2013-01-08       Impact factor: 3.686

2.  Exploring uncharted territories: predicting activity cliffs in structure-activity landscapes.

Authors:  Rajarshi Guha
Journal:  J Chem Inf Model       Date:  2012-08-16       Impact factor: 4.956

3.  Structure-based predictions of activity cliffs.

Authors:  Jarmila Husby; Giovanni Bottegoni; Irina Kufareva; Ruben Abagyan; Andrea Cavalli
Journal:  J Chem Inf Model       Date:  2015-05-11       Impact factor: 4.956

4.  Exploring Structure-Activity Data Using the Landscape Paradigm.

Authors:  Rajarshi Guha
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2012-11

5.  Advancing the activity cliff concept.

Authors:  Ye Hu; Dagmar Stumpfe; Jürgen Bajorath
Journal:  F1000Res       Date:  2013-09-30

6.  "Molecular Anatomy": a new multi-dimensional hierarchical scaffold analysis tool.

Authors:  Candida Manelfi; Marica Gemei; Carmine Talarico; Carmen Cerchia; Anna Fava; Filippo Lunghini; Andrea Rosario Beccari
Journal:  J Cheminform       Date:  2021-07-23       Impact factor: 5.514

7.  Advancing the activity cliff concept, part II.

Authors:  Dagmar Stumpfe; Antonio de la Vega de León; Dilyana Dimova; Jürgen Bajorath
Journal:  F1000Res       Date:  2014-03-18

8.  Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs.

Authors:  Samuel Ayodele Egieyeh; James Syce; Sarel F Malan; Alan Christoffels
Journal:  Malar J       Date:  2016-01-29       Impact factor: 2.979

  8 in total

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