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

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.

With numerous examples and more than 1,000 references, this book presents the most recent QSAR methods and illustrates their scope, advantages, and limitations. It offers extended comparisons of 2.5D methods in which the 3D information is masked by data preprocessing and emphasizes the synergy of various aspects of chemoinformatics, including neural networks, genetic algorithms, support vector machines, data mining, and pharmacophore detection. The authors include actual calculations of the thermodynamic cycle and free energy perturbations. They also present the basic principles of molecular modeling methods and recent statistical/mathematical approaches.

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.

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.

QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment. Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment. Provides comprehensive content about the QSAR techniques and models in facilitating the safety evaluation of drugs and consumer products and risk assesment of environmental chemicals Includes some of the most cutting-edge methodologies such as deep learning and machine learning for QSAR Offers detailed procedures of modeling and provides examples of each model's application in real practice

This book is designed to provide an overview of the different genotoxicants and their effects on living organisms, including humans. The contributions made by the specialists in this field of research are gratefully acknowledged. We hope that the information presented in this book will meet the expectations and needs of all those interested in the different aspects of the genotoxicity field. The publication of this book is of great importance to those scientists, pharmacologists, physicians and veterinarians, as well as engineers, teachers, graduate students and administrators of environmental programmes, who make use of these investigations to understand both the basic and applied genotoxic aspects of known and new xenobiotics, and to guide them in their future investigations.

New Avenues in Drug Discovery and Bioactive Natural Products is the second volume of the Natural Medicine book series. It is devoted to current research in drug discovery from natural sources. The volume features 13 chapters that cover modern analytical and scientific approaches. The book starts with chapters on advanced analytical and research techniques, such as genomic mining, quality control of herbal drugs, DNA fingerprinting, high-throughput screening, molecular docking and extraction techniques. The contributors provide a summary of challenges for researchers and commercial applications where possible. The book also features chapters dedicated to specific medicinal agents that target a disease (glycosides, SARS-CoV2 spike protein inhibitors, and andrographolides. The collection of important research topics in natural product chemistry aims to help the scholars and researchers in the scientific community that are involved in the extraction and development of new medicines.

The role of technology in the medical field has resulted in significant developments within the pharmaceutical industry. Computational approaches have emerged as a crucial method in further advancing drug design and development. Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery presents emerging research on the application of computer-assisted design methods for drugs, emphasizing the benefits and improvements that molecular docking has caused within the pharmaceutical industry. Focusing on validation methods, search algorithms, and scoring functions, this book is a pivotal resource for professionals, researchers, students, and practitioners in the field of theoretical and computational chemistry.

Multivariate Analysis in the Pharmaceutical Industry provides industry practitioners with guidance on multivariate data methods and their applications over the lifecycle of a pharmaceutical product, from process development, to routine manufacturing, focusing on the challenges specific to each step. It includes an overview of regulatory guidance specific to the use of these methods, along with perspectives on the applications of these methods that allow for testing, monitoring and controlling products and processes. The book seeks to put multivariate analysis into a pharmaceutical context for the benefit of pharmaceutical practitioners, potential practitioners, managers and regulators. Users will find a resources that addresses an unmet need on how pharmaceutical industry professionals can extract value from data that is routinely collected on products and processes, especially as these techniques become more widely used, and ultimately, expected by regulators. - Targets pharmaceutical industry practitioners and regulatory staff by addressing industry specific challenges - Includes case studies from different pharmaceutical companies and across product lifecycle of to introduce readers to the breadth of applications - Contains information on the current regulatory framework which will shape how multivariate analysis (MVA) is used in years to come