Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.

When trying to find new methods and problem-solving strategies for their research, scientists often turn to nature for inspiration. An excellent example of this is the application of Darwin's Theory of Evolution, particularly the notion of the 'survival of the fittest', in computer programs designed to search for optimal solutions to many kinds of problems. These 'evolutionary algorithms' start from a population of possible solutions to a given problem and, by applying evolutionary principles, evolve successive generations with improved characteristics until an optimal, or near-optimal, solution is obtained. This book highlights the versatility of evolutionary algorithms in areas of relevance to molecular design with a particular focus on drug design. The authors, all of whom are experts in their field, discuss the application of these computational methods to a wide range of research problems including conformational analysis, chemometrics and quantitative structure-activity relationships, de novo molecular design, chemical structure handling, combinatorial library design, and the study of protein folding. In addition, the use of evolutionary algorithms in the determination of structures by X-ray crystallography and NMR spectroscopy is also covered. These state-of-the-art reviews, together with a discussion of new techniques and future developments in the field, make this book a truly valuable and highly up-to-date resource for anyone engaged in the application or development of computer-assisted methods in scientific research.

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.

Computer-Aided Drug Design (CADD) is a comprehensive guide designed for both beginners and experienced users in CADD. This book covers the fundamental principles and gradually delves into more advanced concepts and techniques, making it an invaluable resource to anyone interested in CADD. It begins by establishing a solid foundation, explaining the core concepts of CADD, the user interface and essential tools. It covers QSAR, molecular docking, homology modeling, virtual screening, pharmacophore modeling, ensuring that the reader can quickly become proficient in CADD. The book provides in-depth insights into 3D modeling, rendering, and parametric design. The style of the book is simple, every topic begins from the very basics and explores advanced levels with clarity. Practical examples, step-by-step tutorials and hands-on exercises, are included for better understanding.

A brief history of drug design presented to make clear that there are fashions in this important field and that they change rather rapidly. This is due in part to the fact that the way that a new paradigm is accepted in a drug company often does not depend on its scientific merit alone.

A guide to techniques for the discovery and evaluation of pharamcologically active compounds for therapeutic development, this book covers rational drug design, high-throughput screening, and genetic approaches to drug discovery. The authors focus on advances in the use of combinatorial chemistry and natural products, both of which support the chemical diversity for many drug screening programmes. They examine typical screening studies and their link to robotics and informatics in detail and present an overview of current progress within anitsense therapeutics. The book explores the rapid changes in drug discovery resulting from developments in molecular biology, robotics, and informatics.

Standard medicinal chemistry courses and texts are organized by classes of drugs with an emphasis on descriptions of their biological and pharmacological effects. This book represents a new approach based on physical organic chemical principles and reaction mechanisms that allow the reader to extrapolate to many related classes of drug molecules. The Second Edition reflects the significant changes in the drug industry over the past decade, and includes chapter problems and other elements that make the book more useful for course instruction. - New edition includes new chapter problems and exercises to help students learn, plus extensive references and illustrations - Clearly presents an organic chemist's perspective of how drugs are designed and function, incorporating the extensive changes in the drug industry over the past ten years - Well-respected author has published over 200 articles, earned 21 patents, and invented a drug that is under consideration for commercialization

Volume 11 Reviews in Computational Chemistry Kenny B. Lipkowitz and Donald B. Boyd The Theme of this Eleventh Volume is Computer-Aided Ligand Design and Modeling of Biomolecules. A Stellar Group of Scientists from Around the World Join in this Volume to Provide Tutorials for Beginners and Experts. Chapters 1 and 2 Take A Detailed Look at De Novo Design Methodologies for Discovering New Ligands which May Become Pharmaceuticals. Chapters 3 and 4 Cover the Methods and Applications of Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) Currently Used in Drug Discovery. Ways to Compute the Correct Lipophilic/Hydrophilic Behavior of Molecules are Taught in Chapter 5. Chapter 6 is an Exposition of Realistically Simulating DNA in the Complex Milieu of Ions that Surround it. An Appendix to this Volume Gives A Compendium of Software and Internet Tools for Computational Chemistry. -From Reviews of the Series . This Well-Respected Series Continues the Fine Selection of Topics and Presentation Qualities Set Forth by the Previous Members. For Example, Each Chapter Contains Thorough Treatment of the Theory Behind the Topic Being Covered. Moreover, the Background Material is Followed by Ample Timely Examples Culled From Recent Literature. Journal of Medicinal Chemistry

Chemical Drug Design provides a compact overview on recent advances in this rapidly developing field. With contributions on in silico drug design, natural product based compounds, as well as on ligand- and structure-based approaches, the authors present innovative methods and techniques for identifying and synthetically designing novel drugs.

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications. Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare. This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.