Computational Medicinal Chemistry For Drug Discovery

Observing computational chemistry's proven value to the introduction of new medicines, this reference offers the techniques most frequently utilized by industry and academia for ligand design. Featuring contributions from more than fifty pre-eminent scientists, Computational Medicinal Chemistry for Drug Discovery surveys molecular structure computation, intermolecular behavior, ligand-receptor interaction, and modeling responding to market demands in its selection and authoritative treatment of topics. The book examines molecular mechanics, semi-empirical methods, wave function-based quantum chemistry, density functional theory, 3-D structure generation, and hybrid methods.

Helps you choose the right computational tools and techniques to meet your drug design goals Computational Drug Design covers all of the major computational drug design techniques in use today, focusing on the process that pharmaceutical chemists employ to design a new drug molecule. The discussions of which computational tools to use and when and how to use them are all based on typical pharmaceutical industry drug design processes. Following an introduction, the book is divided into three parts: Part One, The Drug Design Process, sets forth a variety of design processes suitable for a number of different drug development scenarios and drug targets. The author demonstrates how computational techniques are typically used during the design process, helping readers choose the best computational tools to meet their goals. Part Two, Computational Tools and Techniques, offers a series of chapters, each one dedicated to a single computational technique. Readers discover the strengths and weaknesses of each technique. Moreover, the book tabulates comparative accuracy studies, giving readers an unbiased comparison of all the available techniques. Part Three, Related Topics, addresses new, emerging, and complementary technologies, including bioinformatics, simulations at the cellular and organ level, synthesis route prediction, proteomics, and prodrug approaches. The book's accompanying CD-ROM, a special feature, offers graphics of the molecular structures and dynamic reactions discussed in the book as well as demos from computational drug design software companies. Computational Drug Design is ideal for both students and professionals in drug design, helping them choose and take full advantage of the best computational tools available. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

This title covers a wide range of topics relevant to the development of drugs. It provides a comprehensive description of the major methodological strategies available for rational drug discovery.

Due to the rapid and steady growth of available low-cost computer power, the use of computers for discovering and designing new drugs is becoming a central topic in modern molecular biology and medicinal chemistry. In Computational Drug Discovery and Design: Methods and Protocols expert researchers in the field provide key techniques to investigate biomedical applications for drug developments based on computational chemistry. These include methods and techniques from binding sites prediction to the accurate inclusion of solvent and entropic effects, from high-throughput screening of large compound databases to the expanding area of protein-protein inhibition, toward quantitative free-energy approaches in ensemble-based drug design using distributed computing. Written in the highly successful Methods in Molecular BiologyTM series format, chapters include introductions to their respective topics, reference to software and open source analysis tools, step-by-step, readily reproducible computational protocols, and key tips on troubleshooting and avoiding known pitfalls. Thorough and intuitive, Computational Drug Discovery and Design: Methods and Protocols aids scientists in the continuing study of state-of-the-art concepts and computer-based methodologies.

This book reviews recent physicochemical and biophysical techniques applied in drug discovery research, and it outlines the latest advances in computational drug design. Divided into 10 chapters, the book discusses about the role of structural biology in drug discovery, and offers useful application cases of several biophysical and computational methods, including time-resolved fluorometry (TRF) with Förster resonance energy transfer (FRET), X-Ray crystallography, nuclear magnetic resonance spectroscopy, mass spectroscopy, generative machine learning for inverse molecular design, quantum mechanics/molecular mechanics (QM/MM,ONIOM) and quantum molecular dynamics (QMT) methods. Particular attention is given to computational search techniques applied to peptide vaccines using novel mathematical descriptors and structure and ligand-based virtual screening techniques in drug discovery research. Given its scope, the book is a valuable resource for students, researchers and professionals from pharmaceutical industry interested in drug design and discovery.

This insightful book represents the experience and understanding of the global experts in the field and spotlights both the structural and medicinal chemistry aspects of drug design. The need to 'encode' the physiological factors of pharmacology, a key area, is explored.

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs.

Covering computational tools in drug design using techniques from chemoinformatics, molecular modelling and computational chemistry, this book explores these methodologies and applications of in silico medicinal chemistry. The first part of the book covers molecular representation methods in computing in terms of chemical structure, together with guides on common structure file formats. The second part examines commonly used classes of molecular descriptors. The third part provides a guide to statistical learning methods using chemical structure data, covering topics such as similarity searching, clustering and diversity selection, virtual library design, ligand docking and de novo design. The final part of the book summarises the application of methods to the different stages of drug discovery, from target ID, through hit finding and hit-to-lead, to lead optimisation. This book is a practical introduction to the subject for researchers new to the fields of chemoinformatics, molecular modelling and computational chemistry.