Handbook of Chemoinformatics Algorithms

Handbook of Chemoinformatics Algorithms

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Features

  • Explains how algorithms and graph theory describe chemical structures and compute chemical descriptors applied to chemical problems, such as structure–activity/property predictions
  • Explores the development and validation of QSAR models
  • Describes virtual screening techniques, docking methods, inverse-QSAR methods, and de novo design algorithms
  • Covers applications in combinatorial library design and synthesis design
  • Reviews open source software and databases
  • Illustrates applications of bioinformatics techniques to chemical problems, including tree alignment algorithms, classical machine learning algorithms, biological network inference, and systems biology

Summary

Unlike in the related area of bioinformatics, few books currently exist that document the techniques, tools, and algorithms of chemoinformatics. Bringing together worldwide experts in the field, the Handbook of Chemoinformatics Algorithms provides an overview of the most common chemoinformatics algorithms in a single source.

After a historical perspective of the applications of algorithms and graph theory to chemical problems, the book presents algorithms for two-dimensional chemical structures and three-dimensional representations of molecules. It then focuses on molecular descriptors, virtual screening methods, and quantitative structure–activity relationship (QSAR) models, before introducing algorithms to enumerate and sample chemical structures. The book also covers computer-aided molecular design, reaction network generation, and open source software and database technologies. The remaining chapters describe techniques developed in the context of bioinformatics and computational biology and their potential applications to chemical problems.

This handbook presents a selection of algorithms relevant in practice, making the book useful to those working in the field. It offers an up-to-date account of many algorithmic aspects of chemoinformatics.

Table of Contents

Representing 2D Chemical Structures with Molecular Graphs, Ovidiu Ivanciuc

Algorithms to Store and Retrieve 2D Chemical Structures, Milind Misra and Jean-Loup Faulon

3D Molecular Representations, Egon L. Willighagen

Molecular Descriptors, Nikolas Fechner, Georg Hinselmann, and Jörg Kurt Wegner

Ligand- and Structure-Based Virtual Screening, Robert D. Clark and Diana C. Roe

Predictive Quantitative Structure–Activity Relationships Modeling: Data Preparation and the General Modeling Workflow, Alexander Tropsha and Alexander Golbraikh

Predictive Quantitative Structure–Activity Relationships Modeling: Development and Validation of QSAR Models, Alexander Tropsha and Alexander Golbraikh

Structure Enumeration and Sampling, Markus Meringer

Computer-Aided Molecular Design: Inverse Design, Donald P. Visco, Jr.

Computer-Aided Molecular Design: De Novo Design, Diana C. Roe

Reaction Network Generation, Jean-Loup Faulon and Pablo Carbonell

Open Source Chemoinformatics Software and Database Technologies, Rajarshi Guha

Sequence Alignment Algorithms: Applications to Glycans, Trees, and Tree-Like Structures, Tatsuya Akutsu

Machine Learning-Based Bioinformatics Algorithms: Application to Chemicals, Shawn Martin

Using Systems Biology Techniques to Determine Metabolic Fluxes and Metabolite Pool Sizes, Fangping Mu, Amy L. Bauer, James R. Faeder, and William S. Hlavacek

Index

Author Bio(s)

Jean-Loup Faulon is a professor in the Department of Biology at the University of Evry in France.

Andreas Bender is an assistant professor in the Leiden/Amsterdam Center for Drug Research (LACDR) at Leiden University in the Netherlands.

 
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