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Bioinformatics

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Cover of 'Bioinformatics'

Table of Contents

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    Book Overview
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    Chapter 1 3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data.
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    Chapter 2 Inferring Function from Homology.
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    Chapter 3 Inferring Functional Relationships from Conservation of Gene Order.
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    Chapter 4 Structural and Functional Annotation of Long Noncoding RNAs.
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    Chapter 5 Construction of Functional Gene Networks Using Phylogenetic Profiles.
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    Chapter 6 Inferring Genome-Wide Interaction Networks.
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    Chapter 7 Integrating Heterogeneous Datasets for Cancer Module Identification.
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    Chapter 8 Metabolic Pathway Mining.
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    Chapter 9 Analysis of Genome-Wide Association Data.
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    Chapter 10 Adjusting for Familial Relatedness in the Analysis of GWAS Data.
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    Chapter 11 Analysis of Quantitative Trait Loci.
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    Chapter 12 High-Dimensional Profiling for Computational Diagnosis.
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    Chapter 13 Molecular Similarity Concepts for Informatics Applications.
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    Chapter 14 Compound Data Mining for Drug Discovery.
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    Chapter 15 Studying Antibody Repertoires with Next-Generation Sequencing.
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    Chapter 16 Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.
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    Chapter 17 Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.
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    Chapter 18 Inference Method for Developing Mathematical Models of Cell Signaling Pathways Using Proteomic Datasets.
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    Chapter 19 Clustering.
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    Chapter 20 Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.
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    Chapter 21 Information Visualization for Biological Data.
Attention for Chapter 13: Molecular Similarity Concepts for Informatics Applications.
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Chapter title
Molecular Similarity Concepts for Informatics Applications.
Chapter number 13
Book title
Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6613-4_13
Pubmed ID
Book ISBNs
978-1-4939-6611-0, 978-1-4939-6613-4
Authors

Jürgen Bajorath

Editors

Jonathan M. Keith

Abstract

The assessment of small molecule similarity is a central task in chemoinformatics and medicinal chemistry. A variety of molecular representations and metrics are applied to computationally evaluate and quantify molecular similarity. A critically important aspect of molecular similarity analysis in chemoinformatics and pharmaceutical research is that one is typically not interested in quantifying the degree of structural or chemical similarity between compounds per se, but rather in extrapolating from molecular similarity to property similarity. In other words, one assumes that there is a correlation between calculated similarity and specific properties of small molecules including, first and foremost, biological activities. Although similarity is a priori a subjective concept, and difficult to quantify, it must computationally be assessed in a formally consistent manner. Otherwise, there is little utility of similarity calculations. Consistent treatment requires approximations to be made and the consideration of alternative computational similarity concepts, as discussed herein.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 12%
Other 4 9%
Student > Bachelor 4 9%
Researcher 4 9%
Student > Master 4 9%
Other 8 19%
Unknown 14 33%
Readers by discipline Count As %
Chemistry 11 26%
Computer Science 7 16%
Biochemistry, Genetics and Molecular Biology 5 12%
Pharmacology, Toxicology and Pharmaceutical Science 5 12%
Arts and Humanities 1 2%
Other 1 2%
Unknown 13 30%