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Computational Drug Discovery and Design

Overview of attention for book
Cover of 'Computational Drug Discovery and Design'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Computer-Aided Drug Design: An Overview
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    Chapter 2 Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives
  4. Altmetric Badge
    Chapter 3 Practices in Molecular Docking and Structure-Based Virtual Screening
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    Chapter 4 Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites
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    Chapter 5 De Novo Design of Ligands Using Computational Methods
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    Chapter 6 Molecular Dynamics Simulation and Prediction of Druggable Binding Sites
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    Chapter 7 Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein–Ligand Docking Method
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    Chapter 8 Fragment-Based Ligand Designing
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    Chapter 9 Molecular Dynamics as a Tool for Virtual Ligand Screening
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    Chapter 10 Building Molecular Interaction Networks from Microarray Data for Drug Target Screening
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    Chapter 11 Absolute Alchemical Free Energy Calculations for Ligand Binding: A Beginner’s Guide
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    Chapter 12 Evaluation of Protein–Ligand Docking by Cyscore
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    Chapter 13 Molecular Dynamics Simulations of Protein–Drug Complexes: A Computational Protocol for Investigating the Interactions of Small-Molecule Therapeutics with Biological Targets and Biosensors
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    Chapter 14 Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand
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    Chapter 15 Protein–Protein Docking in Drug Design and Discovery
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    Chapter 16 Automated Inference of Chemical Discriminants of Biological Activity
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    Chapter 17 Computational Exploration of Conformational Transitions in Protein Drug Targets
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    Chapter 18 Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design
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    Chapter 19 Calculation of Thermodynamic Properties of Bound Water Molecules
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    Chapter 20 Enhanced Molecular Dynamics Methods Applied to Drug Design Projects
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    Chapter 21 AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures
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    Chapter 22 Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems
  24. Altmetric Badge
    Chapter 23 Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery
  25. Altmetric Badge
    Chapter 24 Identification of Potential MicroRNA Biomarkers by Meta-analysis
Attention for Chapter 9: Molecular Dynamics as a Tool for Virtual Ligand Screening
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Chapter title
Molecular Dynamics as a Tool for Virtual Ligand Screening
Chapter number 9
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_9
Pubmed ID
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Grégory Menchon, Laurent Maveyraud, Georges Czaplicki, Menchon, Grégory, Maveyraud, Laurent, Czaplicki, Georges

Abstract

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

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X Demographics

The data shown below were collected from the profiles of 2 X users 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 13%
Researcher 5 13%
Student > Bachelor 3 8%
Professor 2 5%
Student > Postgraduate 2 5%
Other 4 11%
Unknown 17 45%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 21%
Chemistry 5 13%
Agricultural and Biological Sciences 4 11%
Pharmacology, Toxicology and Pharmaceutical Science 3 8%
Computer Science 1 3%
Other 0 0%
Unknown 17 45%
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 March 2021.
All research outputs
#16,804,405
of 24,717,692 outputs
Outputs from Methods in molecular biology
#5,788
of 13,876 outputs
Outputs of similar age
#281,400
of 453,054 outputs
Outputs of similar age from Methods in molecular biology
#591
of 1,486 outputs
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So far Altmetric has tracked 13,876 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,486 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.