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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

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    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
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    Chapter 18 Ligand-Based Methods in GPCR Computer-Aided Drug Design
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    Chapter 19 Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery
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    Chapter 20 Cheminformatics in the Service of GPCR Drug Discovery
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    Chapter 21 Modeling and Deorphanization of Orphan GPCRs
Attention for Chapter 21: Modeling and Deorphanization of Orphan GPCRs
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Chapter title
Modeling and Deorphanization of Orphan GPCRs
Chapter number 21
Book title
Computational Methods for GPCR Drug Discovery
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7465-8_21
Pubmed ID
29188576
Book ISBNs
978-1-4939-7464-1, 978-1-4939-7465-8
Authors

Constantino Diaz, Patricia Angelloz-Nicoud, Emilie Pihan

Abstract

Despite tremendous efforts, approximately 120 GPCRs remain orphan. Their physiological functions and their potential roles in diseases are poorly understood. Orphan GPCRs are extremely important because they may provide novel therapeutic targets for unmet medical needs. As a complement to experimental approaches, molecular modeling and virtual screening are efficient techniques to discover synthetic surrogate ligands which can help to elucidate the role of oGPCRs. Constitutively activated mutants and recently published active structures of GPCRs provide stimulating opportunities for building active molecular models for oGPCRs and identifying activators using virtual screening of compound libraries. We describe the molecular modeling and virtual screening process we have applied in the discovery of surrogate ligands, and provide examples for CCKA, a simulated oGPCR, and for two oGPCRs, GPR52 and GPR34.

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Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 30%
Student > Ph. D. Student 5 17%
Student > Postgraduate 2 7%
Professor 1 3%
Student > Bachelor 1 3%
Other 1 3%
Unknown 11 37%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 20%
Agricultural and Biological Sciences 3 10%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Computer Science 1 3%
Medicine and Dentistry 1 3%
Other 2 7%
Unknown 15 50%

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