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Rational Drug Design

Overview of attention for book
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 5: Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection
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  • High Attention Score compared to outputs of the same age and source (87th percentile)

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Chapter title
Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection
Chapter number 5
Book title
Rational Drug Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8630-9_5
Pubmed ID
Book ISBNs
978-1-4939-8629-3, 978-1-4939-8630-9
Authors

Flavio Ballante, Ballante, Flavio

Abstract

Main goal in drug discovery is the identification of drug-like compounds capable to modulate specific biological targets. Thus, the prediction of reliable binding poses of candidate ligands, through molecular docking simulations, represents a key step to be pursued in structure-based drug design (SBDD). Since the increasing number of resolved three-dimensional ligand-protein structures, together with the expansion of computational power and software development, the comprehensive and systematic use of experimental data can be proficiently employed to validate the docking performance. This allows to select and refine the protocol to adopt when predicting the binding pose of trial compounds in a target. Given the availability of multiple docking software, a comparative docking assessment in an early research stage represents a must-use step to minimize fails in molecular modeling. This chapter describes how to perform a docking assessment, using freely available tools, in a semiautomated fashion.

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

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 19%
Student > Master 6 14%
Student > Bachelor 4 10%
Student > Doctoral Student 3 7%
Student > Ph. D. Student 2 5%
Other 2 5%
Unknown 17 40%
Readers by discipline Count As %
Chemistry 8 19%
Biochemistry, Genetics and Molecular Biology 5 12%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Economics, Econometrics and Finance 1 2%
Immunology and Microbiology 1 2%
Other 2 5%
Unknown 23 55%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 23 July 2020.
All research outputs
#6,516,749
of 23,096,849 outputs
Outputs from Methods in molecular biology
#1,975
of 13,208 outputs
Outputs of similar age
#132,316
of 442,670 outputs
Outputs of similar age from Methods in molecular biology
#179
of 1,499 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 13,208 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 84% of its peers.
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 442,670 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.