↓ Skip to main content

G Protein-Coupled Receptors - Modeling and Simulation

Overview of attention for book
Attention for Chapter 7: From Three-Dimensional GPCR Structure to Rational Ligand Discovery.
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
33 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
From Three-Dimensional GPCR Structure to Rational Ligand Discovery.
Chapter number 7
Book title
G Protein-Coupled Receptors - Modeling and Simulation
Published in
Advances in experimental medicine and biology, January 2014
DOI 10.1007/978-94-007-7423-0_7
Pubmed ID
Book ISBNs
978-9-40-077422-3, 978-9-40-077423-0
Authors

Albert J Kooistra, Rob Leurs, Iwan J P de Esch, Chris de Graaf, Kooistra AJ, Leurs R, de Esch IJ, de Graaf C, Albert J. Kooistra, Iwan J. P. de Esch

Abstract

This chapter will focus on G protein-coupled receptor structure-based virtual screening and ligand design. A generic virtual screening workflow and its individual elements will be introduced, covering amongst others the use of experimental data to steer the virtual screening process, ligand binding mode prediction, virtual screening for novel ligands, and rational structure-based virtual screening hit optimization. An overview of recent successful structure-based ligand discovery and design studies shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for GPCRs. Moreover, the recently solved GPCR crystal structures have further increased the opportunities in structure-based ligand discovery for this pharmaceutically important protein family. The current chapter will discuss several challenges in rational ligand discovery based on GPCR structures including: (i) structure-based identification of ligands with specific effects on GPCR mediated signaling pathways, and (ii) virtual screening and structure-based optimization of fragment-like molecules.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 15%
Student > Master 4 12%
Professor 3 9%
Lecturer 2 6%
Professor > Associate Professor 2 6%
Other 3 9%
Unknown 14 42%
Readers by discipline Count As %
Chemistry 7 21%
Agricultural and Biological Sciences 5 15%
Biochemistry, Genetics and Molecular Biology 3 9%
Pharmacology, Toxicology and Pharmaceutical Science 3 9%
Computer Science 1 3%
Other 0 0%
Unknown 14 42%
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 26 October 2013.
All research outputs
#18,351,676
of 22,727,570 outputs
Outputs from Advances in experimental medicine and biology
#3,296
of 4,925 outputs
Outputs of similar age
#229,288
of 305,158 outputs
Outputs of similar age from Advances in experimental medicine and biology
#88
of 138 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,925 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 305,158 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.