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Quantitative structure-activity relationships (QSARs) represent predictive models derived from the application of statistical tools correlating biological activity or other properties of chemicals with descriptors representative of molecular structure and/or property. Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment discusses recent advancements in the field of QSARs with special reference to their application in drug development, predictive toxicology, and chemical risk analysis. Focusing on emerging research in the field, this book is an ideal reference source for industry professionals, students, and academicians in the fields of medicinal chemistry and toxicology.
The object of this series is to provide interesting and timely reviews covering all aspects of the field. It is our hope that this will encourage the transfer of new methods, techniques, and parameterizations from the field in which they were developed to other areas that can make good use of them.·Quantitative Structure Property Relationships (QSPR) have developed into a major method of chemical research in many scientific disciplines·Provides much needed cross fertilization between disciplines researching QSPR
Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. - Includes numerous practical examples related to QSAR methods and applications - Follows the Organization for Economic Co-operation and Development principles for QSAR model development - Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools
This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is no standard or introductory publication available that introduces this important topic to students of chemistry and pharmacy. With this in mind, the authors have carefully compiled this brief in order to provide a thorough and painless introduction to the fundamental concepts of QSAR/QSPR modelling. The brief is aimed at novice readers.
Quantitative structure property relationships (QSPR) have become a major method of chemical research. In the course of this development the field has suffered from fragmentation. Applications of QSPR are found in all major chemical disciplines including physical organic, physical, medicinal, agricultural, biological, enviromental, and polymer chemistry. Frequently workers in one area are unaware of parameterizations and models used in other ares which they might well find useful. The is a common thread which runs through these widely diverse areas. The basic principles, parameterizations and methodology are the same or similar throughout. The object of this series is to provide interesting and timely reviews covering all aspects of the field. It encourages the transfer of new methods, techniques, and parameterizations from the area in which they were developed to other areas that can make good use of them. In view of the widespread use of QSPR we believe that this is an important objective. This series will provide the cross-fertilization which is sorely needed.
This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.
The book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included – two areas which have only recently been able to exploit this versatile tool. This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR.
For more than five decades, scientists and researchers have relied on the Advances in Chromatography series for the most up-to-date information on a wide range of developments in chromatographic methods and applications. For Volume 54, the series editors have invited established, well-known chemists to offer cutting-edge reviews of chromatographic methods applied in the life sciences that emphasize the underlying principle of separation science. The clear presentation of topics and vivid illustrations for which this series has become known makes the material accessible and engaging to analytical, biochemical, organic, polymer, and pharmaceutical chemists at all levels of technical skill.
The Chemistry of Cyclobutanes provides an in depth and comprehensive review of cyclobutanes and includes chapters on the theoretical and computational foundations; on analytical and spectroscopical aspects with dedicated chapters on Mass Spectrometry, NMR and IR/UV. There are also extensive application examples enabling the reader to collect both a theoretical and practical understanding. The Chemistry of Functional Groups Series was originally founded by Saul Patai (1918-1998) and in the 39 years of publishing has produced more than 100 volumes, providing outstanding reviews on all aspects of functional groups including analytical, physical and synthetic and applied chemistry. Saul Patai has been helped by outstanding editors, especially Zvi Rappoport who has now taken responsibility for the series to continue the tradition of producing high quality reviews with editors such as Y. Apeloig, I. Marek and J. Liebman.