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Computational Methods for GPCR Drug Discovery

Overview of attention for book
Cover of 'Computational Methods for GPCR Drug Discovery'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Current and Future Challenges in GPCR Drug Discovery
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    Chapter 2 Characterization of Ligand Binding to GPCRs Through Computational Methods
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    Chapter 3 Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design
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    Chapter 4 A Structural Framework for GPCR Chemogenomics: What’s In a Residue Number?
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    Chapter 5 GPCR Homology Model Generation for Lead Optimization
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    Chapter 6 GPCRs: What Can We Learn from Molecular Dynamics Simulations?
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    Chapter 7 Methods of Exploring Protein–Ligand Interactions to Guide Medicinal Chemistry Efforts
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    Chapter 8 Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method
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    Chapter 9 Molecular Basis of Ligand Dissociation from G Protein-Coupled Receptors and Predicting Residence Time
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    Chapter 10 Methodologies for the Examination of Water in GPCRs
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    Chapter 11 Methods for Virtual Screening of GPCR Targets: Approaches and Challenges
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    Chapter 12 Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists
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    Chapter 13 Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs
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    Chapter 14 Challenges and Opportunities in Drug Discovery of Biased Ligands
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    Chapter 15 Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding
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    Chapter 16 Computational Support of Medicinal Chemistry in Industrial Settings
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    Chapter 17 Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations
  19. Altmetric Badge
    Chapter 18 Ligand-Based Methods in GPCR Computer-Aided Drug Design
  20. Altmetric Badge
    Chapter 19 Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery
  21. Altmetric Badge
    Chapter 20 Cheminformatics in the Service of GPCR Drug Discovery
  22. Altmetric Badge
    Chapter 21 Modeling and Deorphanization of Orphan GPCRs
Attention for Chapter 11: Methods for Virtual Screening of GPCR Targets: Approaches and Challenges
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Chapter title
Methods for Virtual Screening of GPCR Targets: Approaches and Challenges
Chapter number 11
Book title
Computational Methods for GPCR Drug Discovery
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7465-8_11
Pubmed ID
29188566
Book ISBNs
978-1-4939-7464-1, 978-1-4939-7465-8
Authors

Jason B. Cross

Abstract

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

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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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 30%
Student > Ph. D. Student 4 15%
Student > Bachelor 3 11%
Student > Postgraduate 2 7%
Student > Master 2 7%
Other 2 7%
Unknown 6 22%
Readers by discipline Count As %
Chemistry 6 22%
Pharmacology, Toxicology and Pharmaceutical Science 3 11%
Biochemistry, Genetics and Molecular Biology 3 11%
Agricultural and Biological Sciences 2 7%
Computer Science 1 4%
Other 2 7%
Unknown 10 37%
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 02 January 2018.
All research outputs
#20,742,744
of 23,344,526 outputs
Outputs from Methods in molecular biology
#10,114
of 13,338 outputs
Outputs of similar age
#380,340
of 444,166 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,502 outputs
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So far Altmetric has tracked 13,338 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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