
A Practical Guide to Rational Drug Design
- 1st Edition - October 1, 2015
- Imprint: Woodhead Publishing
- Author: Sun Hongmao
- Language: English
- Hardback ISBN:9 7 8 - 0 - 0 8 - 1 0 0 0 9 8 - 4
- eBook ISBN:9 7 8 - 0 - 0 8 - 1 0 0 1 0 5 - 9
This book is not going to be an exhaustive survey covering all aspects of rational drug design. Instead, it is going to provide critical know-how through real-world examples. Re… Read more

Purchase options

Institutional subscription on ScienceDirect
Request a sales quoteThis book is not going to be an exhaustive survey covering all aspects of rational drug design. Instead, it is going to provide critical know-how through real-world examples. Relevant case studies will be presented and analyzed to illustrate the following: how to optimize a lead compound whether one has high or low levels of structural information; how to derive hits from competitors’ active compounds or from natural ligands of the targets; how to springboard from competitors’ SAR knowledge in lead optimization; how to design a ligand to interfere with protein-protein interactions by correctly examining the PPI interface; how to circumvent IP blockage using data mining; how to construct and fully utilize a knowledge-based molecular descriptor system; how to build a reliable QSAR model by focusing on data quality and proper selection of molecular descriptors and statistical approaches. A Practical Guide to Rational Drug Design focuses on computational drug design, with only basic coverage of biology and chemistry issues, such as assay design, target validation and synthetic routes.
- Discusses various tactics applicable to daily drug design
- Readers can download the materials used in the book, including structures, scripts, raw data, protocols, and codes, making this book suitable resource for short courses or workshops
- Offers a unique viewpoint on drug discovery research due to the author’s cross-discipline education background
- Explores the author’s rich experiences in both pharmaceutical and academic settings
Research scientists in big pharmaceutical and biotechnology companies, as well as professors and graduate students.
- Dedication
- Introduction to the Book
- Foreword
- Acknowledgements
- About the Author
- Part One: Structure-Based Ligand Design
- Chapter 1: Structures, Limitations, and Pitfalls
- Abstract
- 1.1 Introduction
- 1.2 The limitations of experimentally determined structures
- 1.3 The pitfalls of misusing structural information
- 1.4 Protein structural change upon activation
- Chapter 2: Structure-Based Ligand Design I: With Structures of Protein/Lead Compound Complex Available
- Abstract
- 2.1 Introduction
- 2.2 Case study 1: BACE1 – Fill the pocket by growing a molecule
- 2.3 Case study 2: heat shock protein 90 – Restore the electrostatic complementarity
- 2.4 Case study 3: estrogen receptor α agonists recognized by optimized pharmacophore models
- 2.5 Summary
- Chapter 3: Structure-Based Ligand Design II: With Structure of Protein/Lead Compound Complex Unavailable
- Abstract
- 3.1 Introduction
- 3.2 Case study 1: Plk1 kinase domain inhibitors as antitumor drugs
- 3.3 Case study 2: XIAP inhibitors to trigger apoptosis as an antitumor therapy
- 3.4 Case study 3: Bcl-xl inhibitors as anticancer drugs
- 3.5 Case study 4: design of kinase/bromodomain-containing 4 dual inhibitors
- Chapter 4: Homology Modeling and Ligand-Based Molecule Design
- Abstract
- 4.1 Introduction
- 4.2 Case study 1: prediction of human Yes1 kinase structure
- 4.3 Case study 2: homology modeling of human melanocortin-4 receptor, a G protein-coupled receptor target
- 4.4 Case study 3: ligand-based approaches to human MC4R
- 4.5 Summary
- 4.6 Summary of part I
- Chapter 1: Structures, Limitations, and Pitfalls
- Part Two: QSAR and ADMET Predictions
- Chapter 5: Quantitative Structure–Activity Relationships: Promise, Validations, and Pitfalls
- Abstract
- 5.1 Introduction
- 5.2 QSAR and its role in drug discovery
- 5.3 Preparation of datasets
- 5.4 Geometrical description of PLS and SVM
- 5.5 Support vector machine
- 5.6 Roles of molecular descriptors
- 5.7 Validation of QSAR models
- 5.8 Pitfalls in QSAR modeling
- 5.9 Summary
- Chapter 6: Quantitative Structure–Property Relationships Models for Lipophilicity and Aqueous Solubility
- Abstract
- 6.1 Introduction
- 6.2 Lipophilicity as estimated by log P
- 6.3 Atom type-based molecular descriptors and their optimization
- 6.4 Less complex PLS linear regression model and highly accurate SVR model of log P
- 6.5 Is log D more relevant for drug discovery?
- 6.6 Aqueous solubility is a key property of drug molecules
- 6.7 QSPR modeling of aqueous solubility
- 6.8 Summary
- Chapter 7: In Silico ADMET Profiling: Predictive Models for CYP450, P-gp, PAMPA, and hERG
- Abstract
- 7.1 Introduction
- 7.2 CYP450 for drug metabolism
- 7.3 Intestinal absorption and PAMPA models
- 7.4 Prediction of P-gp activities
- 7.5 Toxicity in drug discovery
- 7.6 Predictive models for hERG
- 7.7 Delivery of modeling results
- 7.8 Summary
- Chapter 5: Quantitative Structure–Activity Relationships: Promise, Validations, and Pitfalls
- Index
- Edition: 1
- Published: October 1, 2015
- No. of pages (Hardback): 292
- Imprint: Woodhead Publishing
- Language: English
- Hardback ISBN: 9780081000984
- eBook ISBN: 9780081001059
SH