A Practical Guide to Rational Drug Design
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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. 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.
Key Features
- 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
Readership
Research scientists in big pharmaceutical and biotechnology companies, as well as professors and graduate students.
Table of Contents
- 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
Product details
- No. of pages: 292
- Language: English
- Copyright: © Woodhead Publishing 2015
- Published: October 1, 2015
- Imprint: Woodhead Publishing
- eBook ISBN: 9780081001059
- Hardcover ISBN: 9780081000984
About the Author
Sun Hongmao
Dr. Sun received his BSc degree in chemistry and PhD degree in physics from University of Science and Technology of China (USTC). He was awarded his second PhD degree in medicinal and computational chemistry by Clark University and UMass Med School in 1997. Dr. Sun joined the faculty of Washington University Medical School at St. Louis in 1998. One and half years later, he became a computational scientist at Roche, where he spent over ten years to support dozens of rational drug design projects in such therapeutic areas as oncology, diabetes, obesity, virology, cardiovascular disease, etc. He is a well-recognized scholar in the field of drug discovery and ADMET predictions. His QSAR model ranked the first place in the Solubility Challenge. Dr. Sun and his colleagues also delivered the most accurate model in the GPCR homology modeling contest. Dr. Sun has published over 20 first and/or corresponding author peer-reviewed research papers, including 8 invited review articles covering different aspects of drug discovery. Dr. Sun was listed as Honorable Editor of three premium journals in the field of drug discovery and chemoinformatics.
Affiliations and Expertise
Researcher, National Institutes of Health, Maryland, USA.
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