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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
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    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
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    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
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    Chapter 23 Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery
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    Chapter 24 Identification of Potential MicroRNA Biomarkers by Meta-analysis
Attention for Chapter 12: Evaluation of Protein–Ligand Docking by Cyscore
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Chapter title
Evaluation of Protein–Ligand Docking by Cyscore
Chapter number 12
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_12
Pubmed ID
29594775
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Yang Cao, Wentao Dai, Zhichao Miao

Abstract

Protein-ligand docking is a powerful method in drug discovery. The reliability of docking can be quantified by RMSD between a docking structure and an experimentally determined one. However, most experimentally determined structures are not available in practice. Evaluation by scoring functions is an alternative for assessing protein-ligand docking results. This chapter first provides a brief introduction to scoring methods used in docking. Then details are provided on how to use Cyscore programs. Finally it describes a case study for evaluation of protein-ligand docking.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 36%
Student > Bachelor 3 14%
Student > Master 3 14%
Researcher 1 5%
Student > Postgraduate 1 5%
Other 0 0%
Unknown 6 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 27%
Pharmacology, Toxicology and Pharmaceutical Science 3 14%
Agricultural and Biological Sciences 3 14%
Chemistry 2 9%
Computer Science 1 5%
Other 1 5%
Unknown 6 27%

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