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Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem

Overview of attention for article published in Journal of Cheminformatics, July 2016
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Title
Cheminformatics analysis of the AR agonist and antagonist datasets in PubChem
Published in
Journal of Cheminformatics, July 2016
DOI 10.1186/s13321-016-0150-6
Pubmed ID
Authors

Ming Hao, Stephen H. Bryant, Yanli Wang

Abstract

As one of the largest publicly accessible databases for hosting chemical structures and biological activities, PubChem has been processing bioassay submissions from the community since 2004. With the increase in volume for the deposited data in PubChem, the diversity and wealth of information content also grows. Recently, the Tox21 program, has deposited a series of pairwise data in PubChem regarding to different mechanism of actions (MOA), such as androgen receptor (AR) agonist and antagonist datasets, to study cell toxicity. To the best of our knowledge, little work has been reported from cheminformatics study for these especially pairwise datasets, which may provide insight into the mechanism of actions of the compounds and relationship between chemical structures and functions, as well as guidance for lead compound selection and optimization. Thus, to fill the gap, we performed a comprehensive cheminformatics analysis, including scaffold analysis, matched molecular pair (MMP) analysis as well as activity cliff analysis to investigate the structural characteristics and discontinued structure-activity relationship of the individual dataset (i.e., AR agonist dataset or AR antagonist dataset) and the combined dataset (i.e., the common compounds between the AR agonist and antagonist datasets). Scaffolds associated only with potential agonists or antagonists were identified. MMP-based activity cliffs, as well as a small group of compounds with dual MOA reported were recognized and analyzed. Moreover, MOA-cliff, a novel concept, was proposed to indicate one pair of structurally similar molecules which exhibit opposite MOA. Cheminformatics methods were successfully applied to the pairwise AR datasets and the identified molecular scaffold characteristics, MMPs as well as activity cliffs might provide useful information when designing new lead compounds for the androgen receptor.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 6%
Unknown 17 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 28%
Student > Master 3 17%
Student > Doctoral Student 2 11%
Student > Postgraduate 2 11%
Student > Ph. D. Student 1 6%
Other 4 22%
Unknown 1 6%
Readers by discipline Count As %
Chemistry 4 22%
Chemical Engineering 2 11%
Agricultural and Biological Sciences 2 11%
Computer Science 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 4 22%
Unknown 3 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 July 2016.
All research outputs
#10,650,930
of 12,010,397 outputs
Outputs from Journal of Cheminformatics
#461
of 467 outputs
Outputs of similar age
#222,512
of 267,648 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 10 outputs
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