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S2RSLDB: a comprehensive manually curated, internet-accessible database of the sigma-2 receptor selective ligands

Overview of attention for article published in Journal of Cheminformatics, January 2017
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Title
S2RSLDB: a comprehensive manually curated, internet-accessible database of the sigma-2 receptor selective ligands
Published in
Journal of Cheminformatics, January 2017
DOI 10.1186/s13321-017-0191-5
Pubmed ID
Authors

Giovanni Nastasi, Carla Miceli, Valeria Pittalà, Maria N. Modica, Orazio Prezzavento, Giuseppe Romeo, Antonio Rescifina, Agostino Marrazzo, Emanuele Amata

Abstract

Sigma (σ) receptors are accepted as a particular receptor class consisting of two subtypes: sigma-1 (σ1) and sigma-2 (σ2). The two receptor subtypes have specific drug actions, pharmacological profiles and molecular characteristics. The σ2 receptor is overexpressed in several tumor cell lines, and its ligands are currently under investigation for their role in tumor diagnosis and treatment. The σ2 receptor structure has not been disclosed, and researchers rely on σ2 receptor radioligand binding assay to understand the receptor's pharmacological behavior and design new lead compounds. Here we present the sigma-2 Receptor Selective Ligands Database (S2RSLDB) a manually curated database of the σ2 receptor selective ligands containing more than 650 compounds. The database is built with chemical structure information, radioligand binding affinity data, computed physicochemical properties, and experimental radioligand binding procedures. The S2RSLDB is freely available online without account login and having a powerful search engine the user may build complex queries, sort tabulated results, generate color coded 2D and 3D graphs and download the data for additional screening. The collection here reported is extremely useful for the development of new ligands endowed of σ2 receptor affinity, selectivity, and appropriate physicochemical properties. The database will be updated yearly and in the near future, an online submission form will be available to help with keeping the database widely spread in the research community and continually updated. The database is available at http://www.researchdsf.unict.it/S2RSLDB.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 24%
Student > Master 4 14%
Student > Ph. D. Student 3 10%
Lecturer > Senior Lecturer 2 7%
Professor > Associate Professor 2 7%
Other 3 10%
Unknown 8 28%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 6 21%
Chemistry 4 14%
Agricultural and Biological Sciences 3 10%
Biochemistry, Genetics and Molecular Biology 2 7%
Mathematics 2 7%
Other 3 10%
Unknown 9 31%
Attention Score in Context

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 21 January 2017.
All research outputs
#19,631,015
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#860
of 891 outputs
Outputs of similar age
#317,741
of 425,457 outputs
Outputs of similar age from Journal of Cheminformatics
#21
of 21 outputs
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