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In Silico Medicinal Chemistry: Computational Methods to Support Drug Design Hardcover - 2016
by Nathan Brown,
- New
- Hardcover
- first
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Details
- Title In Silico Medicinal Chemistry: Computational Methods to Support Drug Design
- Author Nathan Brown,
- Binding Hardcover
- Edition 1 ST
- Condition New
- Pages 232
- Volumes 1
- Language ENG
- Publisher Royal Society of Chemistry
- Date 2016
- Illustrated Yes
- Features Bibliography, Illustrated, Index
- Bookseller's Inventory # AME_9781782621638
- ISBN 9781782621638 / 1782621636
- Weight 1.1 lbs (0.50 kg)
- Dimensions 9.3 x 6.4 x 0.7 in (23.62 x 16.26 x 1.78 cm)
- Library of Congress subjects Drug Design, Computer Simulation
- Library of Congress Catalog Number 2015458785
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From the rear cover
Covering computational tools in drug design using techniques from chemoinformatics, molecular modelling and computational chemistry, this book explores these methodologies and applications of in silico medicinal chemistry.
The first part of the book covers molecular representation methods in computing in terms of chemical structure, together with guides on common structure file formats. The second part examines commonly used classes of molecular descriptors. The third part provides a guide to statistical learning methods using chemical structure data. The fourth part of the book covers topics such as similarity searching, clustering and diversity selection, virtual library design, ligand docking and de novo design in the drug discovery setting. The final part of the book summarises the application of methods to the different stages of drug discovery, from target ID, through hit finding and hit-to-lead, to lead optimisation.
This book is a practical introduction to the subject for researchers new to the fields of chemoinformatics, molecular modelling and computational chemistry.
The first part of the book covers molecular representation methods in computing in terms of chemical structure, together with guides on common structure file formats. The second part examines commonly used classes of molecular descriptors. The third part provides a guide to statistical learning methods using chemical structure data. The fourth part of the book covers topics such as similarity searching, clustering and diversity selection, virtual library design, ligand docking and de novo design in the drug discovery setting. The final part of the book summarises the application of methods to the different stages of drug discovery, from target ID, through hit finding and hit-to-lead, to lead optimisation.
This book is a practical introduction to the subject for researchers new to the fields of chemoinformatics, molecular modelling and computational chemistry.
