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This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. - Presents chemometrics, cheminformatics and machine learning methods under a single reference - Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design - Highlights special topics of computational drug design and available tools and databases

A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals. Provides a perspective of what is currently achievable with computational toxicology and a view to future developments Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference Includes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling Features coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data

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.

Volume 5 of the 5-volume Quantum Nanochemistry focuses on modeling and predicting of the enzyme kinetics and quantitative structure-activity relationships. It reveals the quantum implications to bio-organic and bio-inorganic systems, to enzyme kinetics, and to pharmacophore binding sites of chemical-biological interaction of molecules through cell

Volume 2 of the 5-volume Quantum Nanochemistry presents in a balanced manner the fundamental and advanced concepts, principles, and models as well as their first and novel combinations and applications in quantum (physical) and chemical theory of atomic structure. It exposes the atom's perspective of quantum structures, spanning its diverse analyti

Volume 1 of the 5-volume Quantum Nanochemistry set presents an overall perspective of nuclear, atomic, molecular, and solids structures, and the observability and quantum properties as based on the quantum principles in their various levels of applications, from Planck, Bohr, Einstein, Schrodinger, Hartree-Fock, up to Feynman Path Integral approach

Volume 3 of the 5-volume Quantum Nanochemistry presents the chemical reactivity throughout the molecular structure in general and chemical bonding in particular by introducing the bondons as the quantum bosonic particles of the chemical field, localization, from Huckel to Density Functional expositions, especially in relation to how chemical princi

Volume 4 of the 5-volume Quantum Nanochemistry covers quantum (physical) chemical theory of solids and orderability and addresses the electronic order problems in the solid state viewed as a huge molecule in special quantum states, including also the bondonic treatment of the graphene nano-ribbons, along basic crystallographic principles, from geom

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-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.

This important new book provides innovative material, including peer-reviewed chapters and survey articles on new applied research and development, in the scientifically important field of QSAR in medicinal chemistry. QSAR is a growing field because available computing power is continuously increasing, QSAR's potential is enormous, limited only by

Fundamental QSARs for Metal Ions describes the basic and essential applications of quantitative structure-activity relationships (QSARs) for regulatory or industrial scientists who need to predict metal ion bioactivity. It includes 194 QSARs that have been used to predict metal ion toxicity and 86 QSARs that have been used to predict metal ion bioc

The purpose of Ligand Efficiency Indices for Drug Discovery: Towards an Atlas-Guided Paradigm is to introduce in a concise and self-contained form the concepts, ideas, applications and examples of efficiency-driven drug discovery to the biomedical community at large. The book emphasizes the use of'new variables'and more objective numerical methods to drive drug discovery in an encompassing way. These'new variables'are based on Ligand Efficiency Indices (LEIs) formulated in a way that permits mapping Chemico-Biological Space (CBS) in an Atlas-like representation. It provides a practical and timely discussion of the concepts, ideas, applications and examples of efficiency-driven drug discovery. This book emphasizes the use of a graphical representation and objective numerical methods to drive drug discovery more effectively. It presents the definition of LEIs and the corresponding efficiency planes within an atlas-like environment to provide a robust graphical and numerical framework for medicinal chemists and drug-discoverers. - Provides a practical and timely discussion of the concepts, ideas, applications and examples of efficiency-driven drug discovery - Emphasizes the use of'new variables'and more objective numerical methods to drive quicker and more effective drug discovery - Presents the definition of Ligand Efficiency Indices (LEIs) and the corresponding efficiency planes as key concepts to provide a graphical and numerical framework

This book brings together drug design practitioners, all leaders in their field, who are actively advancing the field of quantitative methods to guide drug discovery, from structure-based design to empirical statistical models - from rule-based approaches to toxicology to the fields of bioinformatics and systems biology. The aim of the book is to show how various facets of the drug discovery process can be addressed in a quantitative fashion (ie: numerical analysis to enable robust predictions to be made). Each chapter includes a brief review of the topic showing the historical development of quantitative approaches, a survey/summary of the current state-of-the-art, a selection of well chosen examples with some worked through and an appreciation of what problems remain to be overcome as well as an indication of how the field may develop. After an overview of quantitative approaches to drug design the book describes the development of concepts of'drug-like properties', of quantitative structure-activity relationships and molecular modelling, and in particular, structure-based design approaches to guide lead optimisation. How to manage and describe chemical structures, underpins all quantitative approaches to drug design and these are described in the following chapters. The next chapter covers the value of a quantitative approach, and also the challenge which is to describe the confidence in any prediction, and methods to assess predictive model quality. The later chapters describe the application of quantitative approaches to describing and optimising potency, selectivity, drug metabolism and pharmacokinetic properties and toxicology, and the design of chemical libraries to feed the screening approaches to lead generation that underpin modern drug discovery. Finally the book describes the impact of bioinformatics, current status of predicting ligand affinity direct from the protein structure, and the application of quantitative approaches to predicting environmental risk. The book provides a summary of the current state-of-the-art in quantitative approaches to drug design, and future opportunities, but it also provides inspiration to drug design practitioners to apply careful design, to make best use of the quantitative methods that are available, while continuing to improve them. Drug discovery still relies heavily on random screening and empirical screening cascades to identify leads and drugs and the process has many failures to deliver only a small handful of drugs. With the rapidly escalating costs of drug discovery and development together with spiralling delivery, quantitative approaches hold the promise of shifting the balance of success, to enable drug discovery to maintain its economic viability.

This book documents the latest research into the theory and application of force-fields, semi-empirical molecular orbital, density functional and ab initio calculations, Quantum Mechanical (QM) based modelling, Atoms in Molecules (AIM) approach, and biomolecular dynamics. It also covers theory and application of 2D cheminformatics, QSAR/QSPR, ADME properties of drugs, drug docking/scoring protocols and approaches, topological methodology, and modelling accurate inhibition constants of enzymes. Finally, the book gives the theory and applications of multiscale modelling of proteins and biomolecular systems. The information need for a book in this area is due to the continuing rapid advance of firstly theoretical approaches, secondly software/hardware and lastly the successful application of the technology and this book fills a gap in the literature. The co-editors have extensive experience of teaching and researching in the field and the book includes contributions from cutting-edge academic and industrial researchers in their respective fields. It is essential reading for medicinal chemists, computational chemists and those in the pharmaceutical industry.

Molecular similarity searching is fast becoming a key tool in organic chemistry. In this book, the editor has brought together an international team of authors, each working at the forefront of this technology, providing a timely and concise overview of current research. The chapters focus principally on those methods which have reached sufficient maturity to be of immediate practical use in molecular design.

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 authors introduce the concept of Molecular Quantum Similarity, developed in their laboratory, in a didactic form. The basis of the concept combines quantum theoretical calculations with molecular structure and properties even for large molecules. They give definitions and procedures to compute similarities molecules and provide graphical tools for visualization of sets of molecules as n-dimensional point charts.

Quantitative Structure-Activity Relationship (QSAR) is a field where true multidisciplinary approaches are being used. This volume titled Recent Trends on QSAR in the Pharmaceutical Perceptions offers an overview on the latest advancements in the field. The e-book explains both basic approaches and new approaches and ideas in QSAR research, providing readers with an impression of recent inclinations and advances in different aspects of the QSAR strategies, such as descriptors, methods of modeling and validation. This e-book is a valuable reference for pharmacologists, medicinal chemists, drug designers, biotechnologists and industry (pharmaceutical and chemical) professionals. It should serve as an important reference material to stimulate interactions and bridge the gap between participants in academia and industries.

Drug discovery is all about finding small molecules that interact in a desired way with larger molecules, namely proteins and other macromolecules in the human body. If the three-dimensional structures of both the small and large molecule are known, their interaction can be tested by computer simulation with a reasonable degree of accuracy. Alternatively, if active ligands are already available, molecular similarity searches can be used to find new molecules. This virtual screening can even be applied to compounds that have yet to be synthesized, as opposed to'real'screening that requires cost- and labor-intensive laboratory testing with previously synthesized drug compounds. Unique in its focus on the end user, this is a real'how to'book that does not presuppose prior experience in virtual screening or a background in computational chemistry. It is both a desktop reference and practical guide to virtual screening applications in drug discovery, offering a comprehensive and up-to-date overview. Clearly divided into four major sections, the first provides a detailed description of the methods required for and applied in virtual screening, while the second discusses the most important challenges in order to improve the impact and success of this technique. The third and fourth, practical parts contain practical guidelines and several case studies covering the most important scenarios for new drug discovery, accompanied by general guidelines for the entire workflow of virtual screening studies. Throughout the text, medicinal chemists from academia, as well as from large and small pharmaceutical companies report on their experience and pass on priceless practical advice on how to make best use of these powerful methods.

The MARCH-INSIDE approach is a computational method that can be used to seek Quantitative Structure-Property Relationships (QSAR) models in genes and their product RNA and/or proteins without to rely upon sequence alignment. This new book reviews previous applications of MARCH-INSIDE predict the function of new sequences experimentally discovered and discuss the legal issues related to using QSAR and in general Bioinformatics models in real research and development problems in Plant Genomics. From this book it is possible to conclude that MARCH-INSIDE models may be applied in Plant Genomics and Biotechnology to find new interesting enzymes without relying upon alignment techniques.

Since the publication of the first edition, the field has changed dramatically. Scientists can now explicitly consider 3D features in quantitative structure-activity relationship (QSAR) studies and often have the 3D structure of the macromolecular target to guide the 3D QSAR. Improvements in computer hardware and software have also made the methods

As a result of new statistical and mathematical approaches, improved visualization tools, and recognition by international regulatory groups, quantitative structure-activity relationships (QSARs) now play important roles in pharmacology for the design of new drugs as well as in toxicology and ecotoxicology for hazard identification and risk assessm

A comprehensive look at empirical approaches to molecular discovery, their relationships with rational design, and the future of both Empirical methods of discovery, along with serendipitous and rational design approaches, have played an important role in human history. Searching for Molecular Solutions compares empirical discovery strategies for biologically useful molecules with serendipitous discovery and rational design, while also considering the strengths and limitations of empirical pathways to molecular discovery. Logically arranged, this text examines the different modes of molecular discovery, empha-sizing the historical and ongoing importance of empirical strategies. Along with a broad overview of the subject matter, Searching for Molecular Solutions explores: The differing modes of molecular discovery Biological precedents for evolutionary approaches Directed evolutionary methods and related areas Enzyme evolution and design Functional nucleic acid discovery Antibodies and other recognition molecules General aspects of molecular recognition Small molecule discovery approaches Rational molecular design The interplay between empirical and rational strategies and their ongoing roles in the future of molecular discovery Searching for Molecular Solutions covers several major areas of modern research, development, and practical applications of molecular sciences. This text offers empirical-rational principles of broad relevance to scientists, professionals, and students interested in general aspects of molecular discovery, as well as the thought processes behind experimental approaches.

Uses Computational Tools to Simulate Endocrine Disruption PhenomenaEndocrine Disruption Modeling provides a practical overview of the current approaches for modeling endocrine activity and the related potential adverse effects they may induce on environmental and human health. Based on the extensive research of an international panel of contributor

An All-Inclusive Review of the Achievements and Trends in the Fast-Growing Protein Engineering Field From humble beginnings like making fire for mere survival, engineering now steadfastly penetrates all aspects of our lives and even life itself at the molecular level. Protein engineering is a molecular biological discipline focused on designing and

Recent Advances in QSAR Studies: Methods and Applications presents an interdisciplinary overview on the most recent advances in QSAR studies. The first part of this volume is handbook-esque and consists of a comprehensive review of QSAR methodology written by outstanding scientists and highly experienced lecturers. It focuses on methodology and new ideas, providing readers with an idea of recent trends and developments in each part of QSAR strategy (descriptors, methods of modelling, validation). The second part of this volume highlights the interdisciplinary aspects and new areas of QSAR modelling. It outlines the theoretical framework together with practical applications. The most optimal solutions (descriptors, mathematical/statistical methods, validation) in the individual areas of interest (environmental risk assessment, drug design, etc.) are also discussed in more detail. Recent Advances in QSAR Studies: Methods and Applications is targeted at scientists who are focussed on developing new methodologies as well as researchers who are engaged in trying to solve specific problems via QSAR. Additionally, this volume should serve as an essential reference to promote interactions between members of the academic community and industry.

A comprehensive analysis of state-of-the-art molecular modeling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals This unique volume describes how the interaction of molecules with toxicologically relevant targets can be predicted using computer-based tools utilizing X-ray crystal structures or homology, receptor, pharmacophore, and quantitative structure activity relationship (QSAR) models of human proteins. It covers the in vitro models used, newer technologies, and regulatory aspects. The book offers a complete systems perspective to risk assessment prediction, discussing experimental and computational approaches in detail, with: • An introduction to toxicology methods and an explanation of computational methods • In-depth reviews of QSAR methods applied to enzymes, transporters, nuclear receptors, and ion channels • Sections on applying computers to toxicology assessment in the pharmaceutical industry and in the environmental arena • Chapters written by leading international experts • Figures that illustrate computational models and references for further information This is a key resource for toxicologists and scientists in the pharmaceutical industry and environmental sciences as well as researchers involved in ADMET, drug discovery, and technology and software development.

Quantitative Structure-Activity Relationship (QSAR) for Pesticide Regulatory Purposes stems from the experience of the EC funded project DEMETRA. This project combined institutes involved in the regulatory process of pesticides, industries of the sector and scientists to develop and offer original software for the prediction of ecotoxicity of pesticides. Then to be used within the dossier preparation for pesticide registration. The basis of this book is more than three-years of research activities, discussions, studies and successful models. This experience represents a useful example not only for the case of pesticides, but also for the prediction of ecotoxicity and toxicity in general. QSAR is used to link a given property of a chemical compound with some features related to its structure. The theoretical toxicological, chemical and information technology aspects will be treated considering the regulatory issues. Innovative hybrid systems will be described, for the toxicity prediction of pesticides and related compounds, directly useful for pesticide evaluation within the Dossier preparation for pesticide registration. Five endpoints will also be discussed, addressing issues as standardisation, verification, validation, accessibility, reproducibility.The driving force for Quantitative Structure-Activity Relationship (QSAR) for Pesticide Regulatory Purposes is that all the issues of concern for end-users are analysed, discussed and solutions proposed further. An innovative feature is that, in order to offer powerful QSAR models, the book discusses and reports on integrated QSAR models, combined into a unique hybrid system.• Assesses the needs of regulators for pesticide approval and how these needs affect QSAR models• Combines theoretical discussion with practical examples, including five worked examples of hybrid systems• Refers to original software available through the internet

This unique reference source, edited by the world's most respected expert on molecular interaction field software, covers all relevant principles of the GRID force field and its applications in medicinal chemistry. Entire chapters on 3D-QSAR, pharmacophore searches, docking studies, metabolism predictions and protein selectivity studies, among others, offer a concise overview of this emerging field. As an added bonus, this handbook includes a CD-ROM with the latest commercial versions of the GRID program and related software.

Quantitative Structure-Activity Relationships (QSARs) are increasingly used to predict the harmful effects of chemicals to humans and the environment. The increased use of these methods in a variety of areas (academic, industrial, regulatory) results from a realization that very little toxicological or fate data is available on the vast amount of chemicals to which humans and the environment are exposed.Predicting Chemical Toxicity and Fate provides a comprehensive explanation of the state-of-the-art methods that are available to predict the effects of chemicals on humans and the environment. It describes the use of predictive methods to estimate the physiochemical properties, biological activities, and fate of chemicals. The methods described may be used to predict the properties of drugs before their development, and to predict the environmental effects of chemicals. These methods also reduce the cost of product development and the need for animal testing. This book fills an obvious need by providing a comprehensive explanation of these prediction methods. It is a practical book that illustrates the use of these techniques in real life scenarios. This book will demystify QSARs for those students unsure of them, and professionals in environmental toxicology and chemistry will find this a useful reference in their everyday working lives.

Stereology is a valuable tool for neuroscientists, allowing them to obtain 3-Dimensional information from 2-Dimensional measurements made on appropriately sampled sections (usually obtained from histological sections or MRI/CT/PET scans). This 3-D information is invaluable in correlating structural/functional relationships in the pursuit of far greater understanding of the function of the central nervous system. However, in carrying out such measurements, often based on limited data sets, there is a risk of experimenter bias. An important feature of modern design based stereology is to be aware of potential sources of bias and eliminate them during the data collection. With many of the major neuroscience journals now insisting that quantitative data be presented, there is a greater need than ever for neuroscientists to understand the theory and practice behind quantitative methods, such as those offered by stereology. Quantitative Methods in Neuroscience is a cookbook of stereological methods written especially for neuroscientists. It provides clear and accessible advice about when and when not to use stereology. Throughout the book, the emphasis is on practical guidance, rather than discussions and formulae. Written by leading scientists in the field of stereology, with a Foreword by D.C. Sterio, the book will be a valuable introduction to these methods for neuroscientists, and all those involved in development of new drug programmes.

This advanced level textbook provides a comprehensive overview of recent developments in the area of molecular based diagnostics (including nucleic acids, biosensors and immunoessays) of disease markers. It also covers the impact of techniques such as in vitro nucleic acid amplifications (e.g. PCR) and other amplification methods, as well as gene and biochip production and automated techniques such as fluorescent sequencing. The book discusses key concepts where new and merging areas, including pharmacogenomics, proteomics and functional genomics, are being researched and developed. In addition, examples are given where this new area of bioscience has or may be successfully applied.

Research in the pharmaceutical industry today is in many respects quite different from what it used to be only fifteen years ago. There have been dramatic changes in approaches for identifying new chemical entities with a desired biological activity. While chemical modification of existing leads was the most important approach in the 1970s and 1980s, high-throughput screening and structure-based design are now major players among a multitude of methods used in drug discov­ ery. Quite often, companies favor one of these relatively new approaches over the other, e.g., screening over rational design, or vice versa, but we believe that an intelligent and concerted use of several or all methods currently available to drug discovery will be more successful in the medium term. What has changed most significantly in the past few years is the time available for identifying new chemical entities. Because of the high costs of drug discovery projects, pressure for maximum success in the shortest possible time is higher than ever. In addition, the multidisciplinary character of the field is much more pronounced today than it used to be. As a consequence, researchers and project managers in the pharmaceutical industry should have a solid knowledge of the more important methods available to drug discovery, because it is the rapidly and intelligently combined use of these which will determine the success or failure of preclinical projects.

Applied with success in a number of areas, QSAR studies have become particularly popular in the rational design of drugs and pesticides. Much has been published on the principles of QSAR in this area, but not on their application s to toxic chemicals. This book provides the first comprehensive, interdisciplinary presentation of QSAR studies on

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 to solicit reviews on published and new viewpoints to document the state of the art of 3D QSAR in its broadest definition and to provide visions of where new techniques will emerge or new appli- tions may be found. The intention is not only to highlight new ideas but also to show the shortcomings, inaccuracies, and abuses of the methods. We hope this book will enable others to separate trivial from visionary approaches and me-too methodology from in- vative techniques. These concerns guided our choice of contributors. To our delight, our call for papers elicited a great many manuscripts.

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 to solicit reviews on published and new viewpoints to document the state of the art of 3D QSAR in its broadest definition and to provide visions of where new techniques will emerge or new appli- tions may be found. The intention is not only to highlight new ideas but also to show the shortcomings, inaccuracies, and abuses of the methods. We hope this book will enable others to separate trivial from visionary approaches and me-too methodology from in- vative techniques. These concerns guided our choice of contributors. To our delight, our call for papers elicited a great many manuscripts.

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

Phytochemicals as Bioactive Agents focuses on the mechanisms of action of phytochemicals identified as displaying bioactivity in the prevention of cancer, heart disease and other diseases and the prospects for developing functional foods containing these bioactive compounds. Internationally recognized experts present the latest research findings on the antimutagenic and anticarcinogenic effects of tea and tea constituents; chemoprevention provided by plants in the family Cruciferae and genus Allium; anticarcinogenic effects of carotenoids and curcumins; the chemistry and application of alfalfa saponins; the bioactive components of rice bran and rice oil; the effects of garlic on lowering serum cholesterol; and using phytochemicals to optimize gastrointestinal tract health and function.

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.