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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.
- 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
- Sales Rank: #2068367 in Books
- Published on: 2015-10-26
- Original language: English
- Number of items: 1
- Dimensions: 8.90" h x .70" w x 6.10" l, 1.45 pounds
- Binding: Hardcover
- 292 pages
About the Author
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.
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