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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
  5. Altmetric Badge
    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
  11. Altmetric Badge
    Chapter 10 Building Molecular Interaction Networks from Microarray Data for Drug Target Screening
  12. Altmetric Badge
    Chapter 11 Absolute Alchemical Free Energy Calculations for Ligand Binding: A Beginner’s Guide
  13. Altmetric Badge
    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
  16. Altmetric Badge
    Chapter 15 Protein–Protein Docking in Drug Design and Discovery
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    Chapter 16 Automated Inference of Chemical Discriminants of Biological Activity
  18. Altmetric Badge
    Chapter 17 Computational Exploration of Conformational Transitions in Protein Drug Targets
  19. Altmetric Badge
    Chapter 18 Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design
  20. Altmetric Badge
    Chapter 19 Calculation of Thermodynamic Properties of Bound Water Molecules
  21. Altmetric Badge
    Chapter 20 Enhanced Molecular Dynamics Methods Applied to Drug Design Projects
  22. Altmetric Badge
    Chapter 21 AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures
  23. Altmetric Badge
    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 18: Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

wikipedia
1 Wikipedia page

Readers on

mendeley
30 Mendeley
Summary Wikipedia
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Chapter title
Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design
Chapter number 18
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_18
Pubmed ID
29594781
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Louis-Philippe Morency, Francis Gaudreault, Rafael Najmanovich, Morency, Louis-Philippe, Gaudreault, Francis, Najmanovich, Rafael

Abstract

Docking simulations help us understand molecular interactions. Here we present a hands-on tutorial to utilize FlexAID (Flexible Artificial Intelligence Docking), an open source molecular docking software between ligands such as small molecules or peptides and macromolecules such as proteins and nucleic acids. The tutorial uses the NRGsuite PyMOL plugin graphical user interface to set up and visualize docking simulations in real time as well as detect and refine target cavities. The ease of use of FlexAID and the NRGsuite combined with its superior performance relative to widely used docking software provides nonexperts with an important tool to understand molecular interactions with direct applications in structure-based drug design and virtual high-throughput screening.

View on publisher site
Mendeley readers

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 %
Student > Ph. D. Student 4 13%
Researcher 3 10%
Student > Doctoral Student 3 10%
Student > Bachelor 2 7%
Professor 2 7%
Other 6 20%
Unknown 10 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 17%
Pharmacology, Toxicology and Pharmaceutical Science 3 10%
Chemistry 3 10%
Agricultural and Biological Sciences 1 3%
Computer Science 1 3%
Other 4 13%
Unknown 13 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 April 2022.
All research outputs
#7,737,238
of 23,530,272 outputs
Outputs from Methods in molecular biology
#2,421
of 13,391 outputs
Outputs of similar age
#155,074
of 444,960 outputs
Outputs of similar age from Methods in molecular biology
#233
of 1,485 outputs
Altmetric has tracked 23,530,272 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,391 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% 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 444,960 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 54% of its contemporaries.
We're also able to compare this research output to 1,485 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.

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