Kleine Moleküle für Einsteiger: Dieser für Lehre und Selbststudium gleichermaßen geeignete Band behandelt den computergestützten Entwurf von Wirkstoffen, Enzyminhibitoren, Sonden und Markern für Biomoleküle und führt den Leser bis zum ersten eigenen De-Novo-Design eines funktionellen Moleküls. Gestützt auf lange Erfahrung im Molecular-Modeling-Umfeld erläutern die Autoren, welche Fragen mit den beschriebenen Methoden beantwortet werden können (und welche nicht).
CAMD or Computer Aided Molecular Design refers to the design of molecules with desirable properties. That is, through CAMD, one determines molecules that match a specified set of (target) properties. CAMD as a technique has a very large potential as in principle, all kinds of chemical, bio-chemical and material products can be designed through this technique.This book mainly deals with macroscopic properties and therefore does not cover molecular design of large, complex chemicals such as drugs. While books have been written on computer aided molecular design relating to drugs and large complex chemicals, a book on systematic formulation of CAMD problems and solutions, with emphasis on theory and practice, which helps one to learn, understand and apply the technique is currently unavailable.·This title brings together the theoretical aspects related to Computer Aided Molecular Design, the different techniques that have been developed and the different applications that have been reported. ·Contributing authors are among the leading researchers and users of CAMD·First book available giving a systematic formulation of CAMD problems and solutions
The computer-aided design of novel molecular systems has undoubtedly reached the stage of a mature discipline offering a broad range of tools available to virtually any chemist. However, there are few books coveringmost of these techniques in a single volume and using a language which may generally be understood by students or chemists with a limited knowledge of theoretical chemistry. The purpose of this book is precisely to review, in such a language, both methodological aspects and important applications of computer-aided molecular design (CAMD), with a special emphasis on drug design and protein modeling.Using numerous examples ranging from molecular models to shapes, surfaces, and volumes, Computer-Aided Molecular Design provides coverage of the role molecular graphics play in CAMD. The text also treats the very notion of the structure of molecular systems by presenting both the various experimental techniques giving access to it and the most common model builders based on force fields. Separate chapters are devoted to other important topics in CAMD, such as Monte Carlo and molecular dynamics simulations; most common quantum chemical methods; derivation and visualization of molecular properties; and molecular similarity. Finally, strategies used in protein modeling and drug design, such as receptor mapping and the pharmacophore approach, are presented and illustrated by several examples.The book is addressed to students and researchers who wish to enter this new exciting field of molecular sciences, but also practitioners in CAMD as a comprehensive source of refreshing information in their field.Key Features* Presents a comprehensive introduction to computer-aided molecular design* Describes applications of CAMD through the use of numerous examples* Emphasizes strategies used in protein modeling and drug design* Includes separate chapters devoted to other important topics in CAMD, such as:* Monte Carlo and molecular dynamics simulations* Common quantum chemical methods* Derivation and visualization of molecular properties* Molecular similarity
Drug design is a complex, challenging and innovative research area. Structure-based molecular design has transformed the drug discovery approach in modern medicine. Traditionally, focus has been placed on computational, structural or synthetic methods only in isolation. This one-of-akind guide integrates all three skill sets for a complete picture of contemporary structure-based design. This practical approach provides the tools to develop a high-affinity ligand with drug-like properties for a given drug target for which a high-resolution structure exists. The authors use numerous examples of recently developed drugs to present "best practice" methods in structurebased drug design with both newcomers and practicing researchers in mind. By way of a carefully balanced mix of theoretical background and case studies from medicinal chemistry applications, readers will quickly and efficiently master the basic skills of successful drug design. This book is aimed at new and active medicinal chemists, biochemists, pharmacologists, natural product chemists and those working in drug discovery in the pharmaceutical industry. It is highly recommended as a desk reference to guide students in medicinal and chemical sciences as well as to aid researchers engaged in drug design today.
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
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.
The molecular modeling perspective in drug design. (N. Calude Cohen). Molecular graphics and modeling: tools of the trade. (Roderick E. Hubbard). Molecular modeling of small molecules. (Tamara Gund). Computer assisted new lead design. (Akiko Itai, Miho Yamada Mizutani, Yoshihiko Nishibata, and Nubuo Tomioka). Experimental techniques and data banks. (John P. Priestle and C. Gregory Paris). Computer-assisted drug discovery. (Peter Gund, Gerald Maggiora, and James P. Snyder). Modeling drug-receptor interactions. (Konrad F. Koehler, Shashidhar N. Rao, and James P. Snyder). Glossary of terminology. (J. P. Tollenaere).
Molecular modeling (MM) tools offer significant benefits in the design of industrial chemical plants and material processing operations. While the role of MM in biological fields is well established, in most cases MM works as an accessory in novel products/materials development rather than a tool for direct innovation. As a result, MM engineers and practitioners are often seized with the question: "How do I leverage these tools to develop novel materials or chemicals in my industry?" Molecular Modeling for the Design of Novel Performance Chemicals and Materials answers this important question via a simple and practical approach to the MM paradigm. Using case studies, it highlights the importance and usability of MM tools and techniques in various industrial applications. The book presents detailed case studies demonstrating diverse applications such as mineral processing, pharmaceuticals, ceramics, energy storage, electronic materials, paints, coatings, agrochemicals, and personal care. The book is divided into themed chapters covering a diverse range of industrial case studies, from pharmaceuticals to cement. While not going too in-depth into fundamental aspects, the book covers almost all paradigms of MM, and references are provided for further learning. The text includes more than 100 color illustrations of molecular models.
