Computational Phytochemistry

2nd Edition - March 6, 2024
Editors: Satyajit Dey Sarker, Lutfun Nahar
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 6 1 0 2 - 5
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 6 1 0 3 - 2

Computational Phytochemistry, Second Edition, explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance ma… Read more

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Computational Phytochemistry, Second Edition, explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance many of their operations, refocusing and expanding the possibilities of phytochemical studies. By applying computational aids and mathematical models to extraction, isolation, structure determination, and bioactivity testing, researchers can obtain highly detailed information about phytochemicals and optimize working approaches.
This book aims to support and encourage researchers currently working with or looking to incorporate computational methods into their phytochemical work. Topics in this book include computational methods for predicting medicinal properties, optimizing extraction, isolating plant secondary metabolites, and building dereplicated phytochemical libraries. The roles of high-throughput screening, spectral data for structural prediction, plant metabolomics, and biosynthesis are all reviewed before the application of computational aids for assessing bioactivities and virtual screening is discussed. Illustrated with detailed figures and supported by practical examples, this book is an indispensable guide for all those involved with the identification, extraction, and application of active agents from natural products.
This new edition captures remarkable advancements in mathematical modeling and computational methods that have been incorporated in phytochemical research, addressing, e.g., extraction, isolation, structure determination, and bioactivity testing of phytochemicals.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface to the first edition
Preface to the second edition
Chapter 1 Computational phytochemistry: An overview
Abstract
Acknowledgement
1.1 Introduction
1.2 Computational phytochemistry
1.3 Techniques, theories and applications of computational phytochemistry
1.4 Conclusions
References
Chapter 2 Response surface methodology (RSM) in phytochemical research
Abstract
Acknowledgement
2.1 Introduction
2.2 Generic steps in response surface methodology
2.3 Application of response surface methodology (RSM) in phytochemical research
2.4 Conclusion
References
Chapter 3 Prediction of medicinal properties using mathematical models and computation, and selection of plant materials
Abstract
Acknowledgement
3.1 Introduction
3.2 Mathematical models
3.3 Computational models in drug discovery
3.4 Selection of medicinal plants
3.5 Role of medicinal plant databases
3.6 Tools and techniques
3.7 Role of data mining in medicinal plant selection
3.8 Safety considerations
3.9 Conclusion
References
Chapter 4 Optimization of extraction using mathematical models and computation
Abstract
Acknowledgement
4.1 Introduction
4.2 Designs of experiment (DOE)
4.3 Optimization phase
4.4 Specific examples
4.5 Conclusion
References
Chapter 5 Application of computational methods for the isolation of plant secondary metabolites
Abstract
5.1 Introduction
5.2 Computational methods in natural products isolations
5.3 Conclusion
References
Chapter 6 Application of computation in creating dereplicated phytochemical libraries
Abstract
Acknowledgement
6.1 Introduction
6.2 Compound library
6.3 Dereplication
6.4 Application of computation in constructing dereplicated phytochemical libraries
6.5 Conclusions
References
Chapter 7 High throughput screening of phytochemicals: Application of computational methods
Abstract
Acknowledgement
7.1 Introduction
7.2 The pre-HTS era
7.3 High-throughput screening (HTS)
7.4 HTS platforms for natural products/phytochemicals
7.5 High content screening
7.6 HTS screening against SARS-CoV-2
7.7 Conclusions
References
Chapter 8 Prediction of structure based on spectral data using computational techniques
Abstract
Acknowledgement
8.1 Introduction
8.2 Structure elucidation strategies
8.3 What is density functional theory (DFT)?
8.4 Era of assignment vs prediction
8.5 Can Raman be used for automated assays and HTS?
8.6 X-ray sponge technique
8.7 Data curation
8.8 Conclusions
References
Chapter 9 Mathematical models and computation in plant metabolomics: An update
Abstract
Acknowledgement
9.1 Introduction
9.2 Metabolomics approaches
9.3 Mathematical models in metabolomics
9.4 Plant metabolomics
9.5 Limitations in plant metabolomics and future prospects
9.6 Conclusion
References
Chapter 10 Application of computation in the study of biosynthesis of phytochemicals
Abstract
10.1 Introduction
10.2 Genome-mining tools, resources, and computational software for identification and analysis of BGCs
10.3 Computational tools for metabolomics study
10.4 Databases of secondary metabolites and chemical compounds
10.5 Tools for prediction of biochemical pathways
10.6 Overview and conclusions
References
Chapter 11 Computational aids for assessing bioactivities in phytochemical and natural products research
Abstract
11.1 Introduction: Computational aids and artificial intelligence in science and their role in bioactivity studies of natural products
11.2 Strategies for separation and identification of bioactive natural compounds for drug discovery
11.3 Bioactivity assessment in phytochemistry
11.4 Computational tools for data analysis from metabolomics and bioactivity assessment data in natural product research and drug discovery
11.5 Data and text mining strategies
11.6 Virtual or in silico screening of natural products
11.7 Application of in silico assessment of bioactivities
11.8 Overview of software and web-tools for bioactive phytochemicals and natural product research
11.9 Conclusions
References
Chapter 12 Computational approaches to phytochemical drug discovery
Abstract
12.1 Introduction
12.2 Molecular modelling and virtual screening
12.3 Conclusions
References
Chapter 13 Unveiling the power of phytochemicals: Virtual screening of phytochemicals
Abstract
Acknowledgement
13.1 Introduction
13.2 Overview of phytochemistry
13.3 Computational phytochemical pipeline
13.4 Virtual screening
13.5 Prediction of biological activity
13.6 Post-screening analysis and validation
13.7 Strategies for ensuring compound library diversity
13.8 Integrating multi-omics data and computational biology models
13.9 Challenges and future directions
13.10 Conclusions
References
Chapter 14 Predictive toxicology of phytochemicals
Abstract
Acknowledgement
14.1 Introduction to predictive toxicology
14.2 Methods in predictive toxicology
14.3 Predictive toxicological methods in phytochemical analysis: Specific examples
14.4 Conclusion
References
Chapter 15 Network pharmacology in phytochemical research
Abstract
Acknowledgement
15.1 Introduction to network pharmacology
15.2 Methods in network pharmacology
15.3 Network pharmacology in phytochemical research: Specific examples
15.4 Conclusion
References
Index

Life sciences

Physical sciences & Engineering

Social sciences & Humanities

Health

Computational Phytochemistry

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Satyajit Dey Sarker

Lutfun Nahar