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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
  3. Altmetric Badge
    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
  21. Altmetric Badge
    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 17: Computational Exploration of Conformational Transitions in Protein Drug Targets
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Chapter title
Computational Exploration of Conformational Transitions in Protein Drug Targets
Chapter number 17
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_17
Pubmed ID
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Benjamin P. Cossins, Alastair D. G. Lawson, Jiye Shi

Abstract

Protein drug targets vary from highly structured to completely disordered; either way dynamics governs function. Hence, understanding the dynamical aspects of how protein targets function can enable improved interventions with drug molecules. Computational approaches offer highly detailed structural models of protein dynamics which are becoming more predictive as model quality and sampling power improve. However, the most advanced and popular models still have errors owing to imperfect parameter sets and often cannot access longer timescales of many crucial biological processes. Experimental approaches offer more certainty but can struggle to detect and measure lightly populated conformations of target proteins and subtle allostery. An emerging solution is to integrate available experimental data into advanced molecular simulations. In the future, molecular simulation in combination with experimental data may be able to offer detailed models of important drug targets such that improved functional mechanisms or selectivity can be accessed.

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

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 27%
Researcher 3 20%
Other 2 13%
Student > Bachelor 2 13%
Librarian 1 7%
Other 4 27%
Readers by discipline Count As %
Chemistry 4 27%
Biochemistry, Genetics and Molecular Biology 3 20%
Agricultural and Biological Sciences 2 13%
Nursing and Health Professions 1 7%
Physics and Astronomy 1 7%
Other 3 20%
Unknown 1 7%
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 03 April 2018.
All research outputs
#18,594,219
of 23,031,582 outputs
Outputs from Methods in molecular biology
#7,974
of 13,177 outputs
Outputs of similar age
#330,599
of 442,391 outputs
Outputs of similar age from Methods in molecular biology
#950
of 1,499 outputs
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