LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Computational Phytochemistry explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance many of their operation… Read more
LIMITED OFFER
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Computational Phytochemistry explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance many of their operations, thus 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 extract 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 role 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 are 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.
The principal target audience is experienced and inexperienced researchers working in the area of phytochemistry. The secondary target includes academics who teach phytochemistry, pharmacognosy and related topics in various undergraduate and postgraduate programs, and the students in those programs of study.
SS
LN