An integrated overview of modern approaches to lead discovery Lead generation is increasingly seen as a distinct and success-determining phase of the drug discovery process. Over recent years, there have been major advances in the understanding of what constitutes a good lead compound and how to improve the chances of finding such a compound. Written by leading scientists and established opinion leaders from industry and academia, this book provides an authoritative overview of the field, as well as the theory, practice, and scope, of the principal Lead Generation Approaches in Drug Discovery, including: The evolution of the lead discovery process, key concepts, current challenges, and future directions Strategies and technologies driving the high-throughput screening (HTS) approach to lead discovery, including the shifting paradigms in the design of compound collections and best practice in the hit confirmation process Knowledge-based in silico or "virtual" screening Theory and practice of the fragment-based approach to lead discovery The opportunities and challenges presented by multi-target drug discovery (MTDD) De novo design of lead compounds and new approaches to estimating the synthetic accessibility of de novo–designed molecules The impact of natural products on drug discovery, and potential of natural product–like compounds for exploring regions of biologically relevant chemical space Using early screening of hits and leads for metabolic, pharmacokinetic, and toxicological liabilities to reduce attrition during the later phases of drug discovery The utility of parallel synthesis and purification in lead discovery With each topic supported by numerous case studies, this is indispensable reading for researchers in industry and academia who wish to keep up to date with the latest strategies and approaches in drug discovery.

In this comprehensive two-volume resource on the topic senior lead generation medicinal chemists present a coherent view of the current methods and strategies in industrial and academic lead generation. This is the first book to combine both standard and innovative approaches in comparable breadth and depth, including several recent successful lead generation case studies published here for the first time. Beginning with a general discussion of the underlying principles and strategies, individual lead generation approaches are described in detail, highlighting their strengths and weaknesses, along with all relevant bordering disciplines like e.g. target identification and validation, predictive methods, molecular recognition or lead quality matrices. Novel lead generation approaches for challenging targets like DNA-encoded library screening or chemical biology approaches are treated here side by side with established methods as high throughput and affinity screening, knowledge- or fragment-based lead generation, and collaborative approaches. Within the entire book, a very strong focus is given to highlight the application of the presented methods, so that the reader will be able to learn from real life examples. The final part of the book presents several lead generation case studies taken from different therapeutic fields, including diabetes, cardiovascular and respiratory diseases, neuroscience, infection and tropical diseases. The result is a prime knowledge resource for medicinal chemists and for every scientist involved in lead generation.

Computational methods and understanding computational models are important in modern drug discovery. The book focuses on computational approaches that can improve the development of in silico methodologies. It includes lead hit methods, docking algorithms, computational chiral compounds, structure-based drug design, GROMACS and NAMD, structural genomics, toxicity prediction, enzyme inhibitors and peptidomimetic therapeutics

Molecular Docking for Computer-Aided Drug Design: Fundamentals, Techniques, Resources and Applications offers in-depth coverage on the use of molecular docking for drug design. The book is divided into three main sections that cover basic techniques, tools, web servers and applications. It is an essential reference for students and researchers involved in drug design and discovery. - Covers the latest information and state-of-the-art trends in structure-based drug design methodologies - Includes case studies that complement learning - Consolidates fundamental concepts and current practice of molecular docking into one convenient resource

Introduction to Fragment-Based Drug Discovery, by Daniel A. Erlanson Fragment Screening Using X-Ray Crystallography, by Thomas G. Davies and Ian J. Tickle Hsp90 Inhibitors and Drugs from Fragment and Virtual Screening, by Stephen Roughley, Lisa Wright, Paul Brough, Andrew Massey and Roderick E. Hubbard Combining NMR and X-ray Crystallography in Fragment-Based Drug Discovery: Discovery of Highly Potent and Selective BACE-1 Inhibitors, by Daniel F. Wyss, Yu-Sen Wang, Hugh L. Eaton, Corey Strickland, Johannes H. Voigt, Zhaoning Zhu and Andrew W. Stamford Combining Biophysical Screening and X-Ray Crystallography for Fragment-Based Drug Discovery, by Michael Hennig, Armin Ruf and Walter Huber Targeting Protein–Protein Interactions and Fragment-Based Drug Discovery, by Eugene Valkov, Tim Sharpe, May Marsh, Sandra Greive and Marko Hyvönen Fragment Screening and HIV Therapeutics, by Joseph D. Bauman, Disha Patel and Eddy Arnold Fragment-Based Approaches and Computer-Aided Drug Discovery, by Didier Rognan

From its origins as a niche technique more than 15 years ago, fragment-based approaches have become a major tool for drug and ligand discovery, often yielding results where other methods have failed. Written by the pioneers in the field, this book provides a comprehensive overview of current methods and applications of fragment-based discovery, as well as an outlook on where the field is headed. The first part discusses basic considerations of when to use fragment-based methods, how to select targets, and how to build libraries in the chemical fragment space. The second part describes established, novel and emerging methods for fragment screening, including empirical as well as computational approaches. Special cases of fragment-based screening, e. g. for complex target systems and for covalent inhibitors are also discussed. The third part presents several case studies from recent and on-going drug discovery projects for a variety of target classes, from kinases and phosphatases to targeting protein-protein interaction and epigenetic targets.

In this comprehensive two-volume resource on the topic senior lead generation medicinal chemists present a coherent view of the current methods and strategies in industrial and academic lead generation. This is the first book to combine both standard and innovative approaches in comparable breadth and depth, including several recent successful lead generation case studies published here for the first time. Beginning with a general discussion of the underlying principles and strategies, individual lead generation approaches are described in detail, highlighting their strengths and weaknesses, along with all relevant bordering disciplines like e.g. target identification and validation, predictive methods, molecular recognition or lead quality matrices. Novel lead generation approaches for challenging targets like DNA-encoded library screening or chemical biology approaches are treated here side by side with established methods as high throughput and affinity screening, knowledge- or fragment-based lead generation, and collaborative approaches. Within the entire book, a very strong focus is given to highlight the application of the presented methods, so that the reader will be able to learn from real life examples. The final part of the book presents several lead generation case studies taken from different therapeutic fields, including diabetes, cardiovascular and respiratory diseases, neuroscience, infection and tropical diseases. The result is a prime knowledge resource for medicinal chemists and for every scientist involved in lead generation.

The multidisciplinary applications of natural science in drug discovery are essential for identifying and developing new drugs and represent a promising area of research. Drug discovery is a complex and challenging process that involves multiple disciplines, including biology, chemistry, pharmacology, and medicine. One of the most interesting areas of drug discovery is using natural science, which involves the study of natural products and their potential therapeutic benefits, in which the research literature has previously been lacking appropriate attention. Multidisciplinary Applications of Natural Science for Drug Discovery and Integrative Medicine is a research book that bridges the gap between traditional and natural science approaches to identify new drug molecules in treating various diseases. The book focuses on allopathic and complementary medicine. It takes a cross-disciplinary examination of biology, chemistry, pharmacology, mathematics, physics, and medicine. This book is suitable for researchers, post-doctoral fellows, graduate students, and post-graduate students interested in exploring natural science's multidisciplinary applications in drug discovery and integrative medicine.

Computational Approaches in Drug Discovery, Development and Systems Pharmacology provides detailed information on the use of computers in advancing pharmacology. Drug discovery and development is an expensive and time-consuming practice, and computer-assisted drug design (CADD) approaches are increasing in popularity in the pharmaceutical industry to accelerate the process. With the help of CADD, scientists can focus on the most capable compounds so that they can minimize the synthetic and biological testing pains. This book examines success stories of CADD in drug discovery, drug development and role of CADD in system pharmacology, additionally including a focus on the role of artificial intelligence (AI) and deep machine learning in pharmacology. Computational Approaches in Drug Discovery, Development and Systems Pharmacology will be useful to researchers and academics working in the area of CADD, pharmacology and Bioinformatics. - Explains computer use in pharmacology using real-life case studies - Provides information about biological activities using computer technology, thus allowing for the possible reduction of the number of animals used for research - Describes the role of AI in pharmacology and applications of CADD in various diseases