Literature DB >> 23296990

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

Ye Hu1, Gerald M Maggiora, Jürgen Bajorath.   

Abstract

Activity cliffs are formed by pairs or groups of structurally similar compounds with significant differences in potency. They represent a prominent feature of activity landscapes of compound data sets and a primary source of structure-activity relationship (SAR) information. Thus far, activity cliffs have only been considered for active compounds, consistent with the principles of the activity landscape concept. However, from an SAR perspective, pairs formed by structurally similar active and inactive compounds should often also be informative. Therefore, we have extended the activity cliff concept to also take inactive compounds into consideration. As source of both confirmed active and inactive compounds, we have exclusively focused on PubChem confirmatory bioassays. Activity cliffs formed between pairs of active compounds (homogeneous pairs) and pairs of active and inactive compounds (heterogeneous pairs) were systematically analyzed on a per-assay basis, hence ensuring the currently highest possible degree of experimental consistency in activity measurement. Only very small numbers of large-magnitude activity cliffs formed between active compounds were detected in PubChem bioassays. However, when taking confirmed inactive compounds from confirmatory assays into account, the activity cliff frequency in assay data significantly increased, involving 11-15% of all qualifying pairs of similar compounds, depending on the molecular representations that were used. Hence, these non-conventional activity cliffs provide an additional source of SAR information.

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Year:  2013        PMID: 23296990     DOI: 10.1007/s10822-012-9632-4

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  22 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs.

Authors:  Xiaoying Hu; Ye Hu; Martin Vogt; Dagmar Stumpfe; Jürgen Bajorath
Journal:  J Chem Inf Model       Date:  2012-04-17       Impact factor: 4.956

3.  Activity landscape representations for structure-activity relationship analysis.

Authors:  Anne Mai Wassermann; Mathias Wawer; Jürgen Bajorath
Journal:  J Med Chem       Date:  2010-09-16       Impact factor: 7.446

4.  Similarity-potency trees: a method to search for SAR information in compound data sets and derive SAR rules.

Authors:  Mathias Wawer; Jürgen Bajorath
Journal:  J Chem Inf Model       Date:  2010-08-23       Impact factor: 4.956

5.  On outliers and activity cliffs--why QSAR often disappoints.

Authors:  Gerald M Maggiora
Journal:  J Chem Inf Model       Date:  2006 Jul-Aug       Impact factor: 4.956

6.  Structure--activity landscape index: identifying and quantifying activity cliffs.

Authors:  Rajarshi Guha; John H Van Drie
Journal:  J Chem Inf Model       Date:  2008-02-28       Impact factor: 4.956

7.  Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets.

Authors:  Jameed Hussain; Ceara Rea
Journal:  J Chem Inf Model       Date:  2010-03-22       Impact factor: 4.956

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

Authors:  Martin Vogt; Yun Huang; Jürgen Bajorath
Journal:  J Chem Inf Model       Date:  2011-08-04       Impact factor: 4.956

9.  Frequency of occurrence and potency range distribution of activity cliffs in bioactive compounds.

Authors:  Dagmar Stumpfe; Jürgen Bajorath
Journal:  J Chem Inf Model       Date:  2012-08-17       Impact factor: 4.956

10.  PubChem's BioAssay Database.

Authors:  Yanli Wang; Jewen Xiao; Tugba O Suzek; Jian Zhang; Jiyao Wang; Zhigang Zhou; Lianyi Han; Karen Karapetyan; Svetlana Dracheva; Benjamin A Shoemaker; Evan Bolton; Asta Gindulyte; Stephen H Bryant
Journal:  Nucleic Acids Res       Date:  2011-12-02       Impact factor: 16.971

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  5 in total

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Authors:  Hao Ming; Cheng Tiejun; Wang Yanli; Bryant H Stephen
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2.  Advancing the activity cliff concept.

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

3.  Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem.

Authors:  Ming Hao; Stephen H Bryant; Yanli Wang
Journal:  J Cheminform       Date:  2016-07-08       Impact factor: 5.514

4.  Systematic identification of target set-dependent activity cliffs.

Authors:  Huabin Hu; Dagmar Stumpfe; Jürgen Bajorath
Journal:  Future Sci OA       Date:  2019-01-18

5.  Target enhanced 2D similarity search by using explicit biological activity annotations and profiles.

Authors:  Xiang Yu; Lewis Y Geer; Lianyi Han; Stephen H Bryant
Journal:  J Cheminform       Date:  2015-11-17       Impact factor: 5.514

  5 in total

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