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Computational Chemogenomics

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Cover of 'Computational Chemogenomics'

Table of Contents

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    Book Overview
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    Chapter 1 A Survey of Web-Based Chemogenomic Data Resources
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    Chapter 2 Finding Potential Multitarget Ligands Using PubChem
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    Chapter 3 Fundamental Bioinformatic and Chemoinformatic Data Processing
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    Chapter 4 Parsing Compound–Protein Bioactivity Tables
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    Chapter 5 Impact of Molecular Descriptors on Computational Models
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    Chapter 6 Physicochemical Property Labels as Molecular Descriptors for Improved Analysis of Compound–Protein and Compound–Compound Networks
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    Chapter 7 Core Statistical Methods for Chemogenomic Data
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    Chapter 8 Structure-Based Detection of Orthosteric and Allosteric Pockets at Protein–Protein Interfaces
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    Chapter 9 Single Binding Pockets Versus Allosteric Binding
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    Chapter 10 Mapping Biological Activities to Different Types of Molecular Scaffolds: Exemplary Application to Protein Kinase Inhibitors
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    Chapter 11 SAR Matrix Method for Large-Scale Analysis of Compound Structure–Activity Relationships and Exploration of Multitarget Activity Spaces
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    Chapter 12 Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework
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    Chapter 13 Selection of Informative Examples in Chemogenomic Datasets
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    Chapter 14 A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection
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    Chapter 15 The Future of Computational Chemogenomics
Attention for Chapter 11: SAR Matrix Method for Large-Scale Analysis of Compound Structure–Activity Relationships and Exploration of Multitarget Activity Spaces
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Chapter title
SAR Matrix Method for Large-Scale Analysis of Compound Structure–Activity Relationships and Exploration of Multitarget Activity Spaces
Chapter number 11
Book title
Computational Chemogenomics
Published by
Humana Press, New York, NY, October 2018
DOI 10.1007/978-1-4939-8639-2_11
Pubmed ID
Book ISBNs
978-1-4939-8638-5, 978-1-4939-8639-2
Authors

Ye Hu, Jürgen Bajorath

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 50%
Computer Science 1 50%