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).
Written by experienced experts in molecular modeling, this books describes the basics to the extent that is necessary if one wants to be able to reliably judge the results from molecular modeling calculations. Its main objective is the description of the various pitfalls to be avoided. Without unnecessary overhead it leads the reader from simple calculations on small molecules to the modeling of proteins and other relevant biomolecules. A textbook for beginners as well as an invaluable reference for all those dealing with molecular modeling in their daily work!
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.
The gap between introductory level textbooks and highly specialized monographs is filled by this modern textbook. It provides in one comprehensive volume the in-depth theoretical background for molecular modeling and detailed descriptions of the applications in chemistry and related fields like drug design, molecular sciences, biomedical, polymer and materials engineering. Special chapters on basic mathematics and the use of respective software tools are included. Numerous numerical examples, exercises and explanatory illustrations as well as a web site with application tools (http://www.amrita.edu/cen/ccmm) support the students and lecturers.
Computer-based design and modeling, computational approaches, and instrumental methods for elucidating molecular mechanisms of protein folding and ligand-acceptor interactions are included in Volumes 202 and 203, as are genetic and chemical methods for the production of functional molecules including antibodies and antigens, enzymes, receptors, nucleic acids and polysaccharides, and drugs.
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).
Very broad overview of the field intended for an interdisciplinary audience; Lively discussion of current challenges written in a colloquial style; Author is a rising star in this discipline; Suitably accessible for beginners and suitably rigorous for experts; Features extensive four-color illustrations; Appendices featuring homework assignments and reading lists complement the material in the main text
Molecular modelling is the scientific art of simulating chemicalor biological systems, so that computational methods can beapplied to understand the process concerned. Models usingcomputers are generated using mathematical equations and areevolved based on experimental information that is taken intoconsideration during model building. This book is anintroduction to the field of molecular modelling and drug designin which biological molecules effective in treating diseases arediscovered using in silico methods.
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.
Molecular Modeling of the Sensitivities of Energetic Materials, Volume 22 introduces experimental aspects, explores the relationships between sensitivity, molecular structure and crystal structure, discusses insights from numerical simulations, and highlights applications of these approaches to the design of new materials. Providing practical guidelines for implementing predictive models and their application to the search for new compounds, this book is an authoritative guide to an exciting field of research that warrants a computer-aided approach for the investigation and design of safe and powerful explosives or propellants. Much recent effort has been put into modeling sensitivities, with most work focusing on impact sensitivity and leading to a lot of experimental data in this area. Models must therefore be developed to allow evaluation of significant properties from the structure of constitutive molecules. - Highlights a range of approaches for computational simulation and the importance of combining them to accurately understand or estimate different parameters - Provides an overview of experimental findings and knowledge in a quick and accessible format - Presents guidelines to implement sensitivity models using open-source python-related software, thus supporting easy implementation of flexible models and allowing fast assessment of hypotheses
