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Computational Methods for GPCR Drug Discovery

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Cover of 'Computational Methods for GPCR Drug Discovery'

Table of Contents

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    Book Overview
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    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 13: Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs
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Chapter title
Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs
Chapter number 13
Book title
Computational Methods for GPCR Drug Discovery
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7465-8_13
Pubmed ID
Book ISBNs
978-1-4939-7464-1, 978-1-4939-7465-8
Authors

Damian Bartuzi, Agnieszka A. Kaczor, Dariusz Matosiuk

Abstract

From the pharmacological point of view, allosteric modulators may present numerous advantages over orthosteric ligands. Growing availability of novel tools and experimental data provides a tempting opportunity to apply computational methods to improve known modulators and design novel ones. However, recent progress in understanding of complexity of allostery increases awareness of problems involved in design of modulators with desired properties. Deeper insight into phenomena such as probe dependence, altering signaling bias with minor changes in ligand structure, as well as influence of subtle endogenous allosteric factors turns out to be fundamental. These effects make the design of a modulator with precise pharmacological outcome a very challenging task, and need to be taken into consideration throughout the design process. In this chapter, we focus on nuances of targeting GPCR allosteric sites in computational drug design efforts, in particular with application of docking, virtual screening, and molecular dynamics.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 24%
Student > Bachelor 5 12%
Researcher 4 10%
Student > Doctoral Student 3 7%
Other 3 7%
Other 7 17%
Unknown 9 22%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 8 20%
Agricultural and Biological Sciences 6 15%
Biochemistry, Genetics and Molecular Biology 5 12%
Chemistry 4 10%
Medicine and Dentistry 3 7%
Other 3 7%
Unknown 12 29%