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Computational Drug Discovery and Design

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

Table of Contents

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    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
  15. Altmetric Badge
    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
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    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
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    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 22: Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems
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Chapter title
Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems
Chapter number 22
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_22
Pubmed ID
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Sergey A. Samsonov

Abstract

Glycosaminoglycans (GAGs) are a class of anionic linear periodic polysaccharides, which play a key role in many cell signaling related processes via interactions with their protein targets. In silico analysis and, in particular, application of molecular docking approaches to these systems still experience many challenges including the need of proper treatment of solvent, which is crucial for protein-GAG interactions. Here, we describe two methods which we developed, to include solvent in the docking studies of protein-GAG systems: the first one allows to de novo predict favorable positions of water molecules as a part of a rigid receptor to be used for further molecular docking; the second one utilizes targeted molecular dynamics in explicit solvent for molecular docking.

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

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 67%
Researcher 1 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 67%
Agricultural and Biological Sciences 1 33%
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 30 March 2018.
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#20,472,403
of 23,031,582 outputs
Outputs from Methods in molecular biology
#9,955
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#378,224
of 442,391 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,499 outputs
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