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Rational Drug Design

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Cover of 'Rational Drug Design'

Table of Contents

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    Book Overview
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    Chapter 1 Molecular Dynamics Simulations on the Bioactive Molecule of hIAPP22–29 (NFGAILSS) and Rational Drug Design
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    Chapter 2 Development of Peptide-Based Inhibitors of Amylin Aggregation Employing Aromatic and Electrostatic Repulsion
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    Chapter 3 In Silico Drug Design: Non-peptide Mimetics for the Immunotherapy of Multiple Sclerosis
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    Chapter 4 Binding Moiety Mapping by Saturation Transfer Difference NMR
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    Chapter 5 Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection
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    Chapter 6 Rational Drug Design Using Integrative Structural Biology
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    Chapter 7 Enalos+ KNIME Nodes: New Cheminformatics Tools for Drug Discovery
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    Chapter 8 Bioguided Design of Trypanosomicidal Compounds: A Successful Strategy in Drug Discovery
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    Chapter 9 A Hybrid Virtual Screening Protocol Based on Binding Mode Similarity
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    Chapter 10 Single Step Determination of Unlabeled Compound Kinetics Using a Competition Association Binding Method Employing Time-Resolved FRET
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    Chapter 11 Dynamic Undocking: A Novel Method for Structure-Based Drug Discovery
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    Chapter 12 The Impact of Lipophilicity in Drug Discovery: Rapid Measurements by Means of Reversed-Phase HPLC
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    Chapter 13 Exploring Polypharmacology in Drug Design
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    Chapter 14 Development of Nuclear Receptor Modulators
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    Chapter 15 In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies
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    Chapter 16 Insights in Organometallic Synthesis of Various Adamantane Derivatives with Sigma Receptor-Binding Affinity and Antiproliferative/Anticancer Activity
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    Chapter 17 Supervised Molecular Dynamics (SuMD) Approaches in Drug Design
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    Chapter 18 Lead Identification Through the Synergistic Action of Biomolecular NMR and In Silico Methodologies
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    Chapter 19 The Use of Dynamic Pharmacophore in Computer-Aided Hit Discovery: A Case Study
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    Chapter 20 Rational Development of MAGL Inhibitors
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    Chapter 21 Application of Virtual Screening Approaches for the Identification of Small Molecule Inhibitors of the Methyllysine Reader Protein Spindlin1
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    Chapter 22 Designing Natural Product Hybrids Bearing Triple Antiplatelet Profile and Evaluating Their Human Plasma Stability
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    Chapter 23 Pharmacophore Generation and 3D-QSAR Model Development Using PHASE
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    Chapter 24 Design of Drugs by Filtering Through ADMET, Physicochemical and Ligand-Target Flexibility Properties
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    Chapter 25 Reactions in NMR Tubes as Key Weapon in Rational Drug Design
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    Chapter 26 Application of Multiscale Simulation Tools on GPCRs. An Example with Angiotensin II Type 1 Receptor
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    Chapter 27 Angiotensin II Type 1 Receptor Homology Models: A Comparison Between In Silico and the Crystal Structures
Attention for Chapter 26: Application of Multiscale Simulation Tools on GPCRs. An Example with Angiotensin II Type 1 Receptor
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Chapter title
Application of Multiscale Simulation Tools on GPCRs. An Example with Angiotensin II Type 1 Receptor
Chapter number 26
Book title
Rational Drug Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8630-9_26
Pubmed ID
Book ISBNs
978-1-4939-8629-3, 978-1-4939-8630-9
Authors

Ismail Erol, Busecan Aksoydan, Isik Kantarcioglu, Serdar Durdagi, Erol, Ismail, Aksoydan, Busecan, Kantarcioglu, Isik, Durdagi, Serdar

Abstract

G protein-coupled receptors (GPCRs) represent the biggest class of membrane proteins included in signal transduction cascade across the biological lipid bilayers. They are essential target structures for cell signaling and are of great commercial interest to the pharmaceutical industry (~50% of marketed drugs and ~25% of top-selling drugs targeting this receptor family). Recent advances made in molecular biology and computational chemistry open new avenues for the design of new therapeutic compounds. Molecular biology has recently provided the crystal structures of a few ligand-bound GPCRs in active and inactive states, which can be used as accurate templates in modeling studies. Computational chemistry offers a range of simulation, multiscale modeling with ligand- and structure-based approaches, and virtual screening tools for definition and analysis of protein-ligand, protein-protein, and protein-DNA interactions. Development of new approaches and algorithms on statistical methods and free energy simulations help to predict novel optimal compounds. Integrated approach to drug discovery that combines quantum mechanics calculations, molecular docking, molecular dynamics (MD) simulations, quantitative structure-activity relationships (QSAR), and de novo design studies under a single umbrella can be used for decreasing the risk of false-positive results. Each method has its own pros and cons and, when used alone, is not likely to yield very useful results. However, when these methods are combined with positive feedback loops, they may enhance each other and successful drug leads may be obtained. Moreover, investigating the activation mechanisms and atomistic determinants of ligand binding to GPCR targets would allow greater safety in the human life.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 15%
Professor > Associate Professor 2 15%
Researcher 2 15%
Student > Master 2 15%
Unknown 5 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 31%
Chemistry 2 15%
Psychology 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Social Sciences 1 8%
Other 1 8%
Unknown 3 23%
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 25 July 2018.
All research outputs
#20,527,576
of 23,096,849 outputs
Outputs from Methods in molecular biology
#9,977
of 13,208 outputs
Outputs of similar age
#378,510
of 442,670 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,499 outputs
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So far Altmetric has tracked 13,208 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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