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Chemoinformatics in Drug Discovery
This handbook provides the first-ever inside view of today's integrated approach to rational drug design. Chemoinformatics experts from large pharmaceutical companies, as well as from chemoinformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process. With the user rather than the developer of chemoinformatics software in mind, this book describes the successful application of computational tools to real-life problems and presents solution strategies to commonly encountered problems. It shows how almost every step of the drug discovery pipeline can be optimized and accelerated by using chemoinformatics tools -- from the management of compound databases to targeted combinatorial synthesis, virtual screening and efficient hit-to-lead transition. An invaluable resource for drug developers and medicinal chemists in academia and industry.
Fragment-based Approaches in Drug Discovery
This first systematic summary of the impact of fragment-based approaches on the drug development process provides essential information that was previously unavailable. Adopting a practice-oriented approach, this represents a book by professionals for professionals, tailor-made for drug developers in the pharma and biotech sector who need to keep up-to-date on the latest technologies and strategies in pharmaceutical ligand design. The book is clearly divided into three sections on ligand design, spectroscopic techniques, and screening and drug discovery, backed by numerous case studies.
Drug Design Strategies
This book shows how various facets of the drug discovery process can be addressed in a quantitative fashion.
Reviews in Computational Chemistry, Volume 11
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 ...
Delta Sustainability
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Computational Medicinal Chemistry for Drug Discovery
Observing computational chemistry's proven value to the introduction of new medicines, Computational Medicinal Chemistry for Drug Discovery offers the techniques most frequently utilized by industry and academia for ligand design. Featuring contributions from more than 50 preeminent scientists, this book surveys molecular structure computation, intermolecular behavior, ligand-receptor interaction, and modeling. It also examines molecular mechanics, semi-empirical methods, wave function-based quantum chemistry, density functional theory, 3-D structure generation, and hybrid methods.
Chemoinformatics Approaches to Virtual Screening
Chemoinformatics is broadly a scientific discipline encompassing the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information. It is distinct from other computational molecular modeling approaches in that it uses unique representations of chemical structures in the form of multiple chemical descriptors; has its own metrics for defining similarity and diversity of chemical compound libraries; and applies a wide array of statistical, data mining and machine learning techniques to very large collections of chemical compounds in order to establish robust relationships between chemical structure and its physical or biological pr...
3D QSAR in Drug Design
Significant progress has been made in the study of three-dimensional quantitative structure-activity relationships (3D QSAR) since the first publication by Richard Cramer in 1988 and the first volume in the series. 3D QSAR in Drug Design. Theory, Methods and Applications, published in 1993. The aim of that early book was to contribute to the understanding and the further application of CoMFA and related approaches and to facilitate the appropriate use of these methods. Since then, hundreds of papers have appeared using the quickly developing techniques of both 3D QSAR and computational sciences to study a broad variety of biological problems. Again the editor(s) felt that the time had come t...
Reviews in Computational Chemistry, Volume 18
Seit vielen Jahren praxisbewährt! Auch dieser 18. Band der Reihe Reviews in Computational Chemistry gibt Studenten und Forschern einen Einblick in Rechenverfahren, die sie anwenden wollen, ohne daß die theoretischen Grundlagen zu ihrem Arbeitsgebiet gehören. Das methodische Spektrum umfaßt Molecular Modeling, Quantenchemie, CAMD, QSAR, Molekülmechanik und -dynamik. Mit einem Autoren- und einem Stichwortverzeichnis sowie einer ausführlichen Softwareliste, die Hunderte von Programmen, Dienstleistungen und Anbietern umfaßt.
Reviews in Computational Chemistry, Volume 29
The Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered on molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (QSAR). This volume, like those prior to it, features chapters by experts in various fields of computational chemistry. Topics in Volume 29 include: Noncovalent Interactions in Density-Functional Theory Long-Range Inter-Particle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory Efficient Transition-State Modeling using Molecular Mechanics Force Fields for the Everyday Chemist Machine Learning in Materials Science: Recent Progress and Emerging Applications Discovering New Materials via a priori Crystal Structure Prediction Introduction to Maximally Localized Wannier Functions Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding
