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

In Silico Drug Design: Repurposing Techniques and Methodologies explores the application of computational tools that can be utilized for this approach. The book covers theoretical background and methodologies of chem-bioinformatic techniques and network modeling and discusses the various applied strategies to systematically retrieve, integrate and analyze datasets from diverse sources. Other topics include in silico drug design methods, computational workflows for drug repurposing, and network-based in silico screening for drug efficacy. With contributions from experts in the field and the inclusion of practical case studies, this book gives scientists, researchers and R&D professionals in the pharmaceutical industry valuable insights into drug design. - Discusses the theoretical background and methodologies of useful techniques of cheminformatics and bioinformatics that can be applied for drug repurposing - Offers case studies relating to the in silico modeling of FDA-approved drugs for the discovery of antifungal, anticancer, antiplatelet agents, and for drug therapies against diseases - Covers tools and databases that can be utilized to facilitate in silico methods for drug repurposing

Organic Electronics is a rapidly evolving multidisciplinary research field at the interface between Organic Chemistry and Physics. Organic Electronics is based on the use of the unique optical and electrical properties of π-conjugated materials that range from small molecules to polymers. The wide activity of researchers in Organic Electronics is testament to the fact that its potential is huge and its list of potential applications almost endless. Application of these electronic and optoelectronic devices range from Organic Field Effect Transistors (OFETs) to Organic Light Emitting Diodes (OLEDs) and Organic Solar Cells (OSCs), sensors, etc. We invited a series of colleagues to contribute to this Special Issue with respect to the aforementioned concepts and keywords. The goal for this Special Issue was to describe the recent developments of this rapidly advancing interdisciplinary research field. We thank all authors for their contributions.

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

The development of a sustainable agricultural system is a critical concern for any nation in modern society. By implementing proper supply chain processes, available natural resources and food can be better utilized. Agri-Food Supply Chain Management: Breakthroughs in Research and Practice is a compendium of emerging perspectives on the development of an effective agricultural value chain and the optimization of supply chain management within the agriculture and food sectors. Highlighting theoretical frameworks, real-world applications, and future outlooks, this book is a primary reference source for professionals, students, practitioners, and managers actively involved in agricultural development.

The development of computational methods that support human health and environmental risk assessment of engineered nanomaterials has attracted great interest because the application of these methods enables us to fill existing experimental data gaps. However, considering the high degree of complexity and multifunctionality of engineered nanoparticles, computational methods originally developed for regular (i.e., classic) chemicals cannot always be applied explicitly in nanotoxicology. Thus, the main idea of this book is to discuss the current state of the art and future needs in the development of computational modeling techniques for nanotoxicology. The book focuses on methodology. Among various in silico techniques, special attention is given to (i) computational chemistry (quantum mechanics, semi-empirical methods, density functional theory, molecular mechanics, molecular dynamics); (ii) nanochemoinformatic methods (quantitative structure–activity relationship modeling, grouping, read-across); and (iii) nanobioinformatic methods (genomics, transcriptomics, proteomics, metabolomics).

This book updates knowledge on recent advances in computational, biophysical and bioinformatics tools/techniques and their practical applications in modern drug design and discovery paradigm. It also encompasses fundamental principles, advanced methodologies and applications of various CADD approaches including several cutting-edge areas; presenting recent developments covering ongoing trends in the field of computer-aided drug discovery. Having contributions by a global team of experts, the book is expected to be an ideal resource for drug discovery scientists, medicinal chemists, pharmacologists, toxicologists, phytochemists, biochemists, biologists, R&D personnel, researchers, students, teachers and those working in the field of drug discovery. It will fill the knowledge gaps that exist in the current CADD approaches and methodologies/ protocols being widely used in both academic and research practices. Further, a special focus on current status of various computational drug design approaches (SBDD, LBDD, de novo drug design, pharmacophore-based search), bioinformatics tools and databases, computational screening and modeling of phytochemicals/natural products, artificial intelligence and machine learning, and network pharmacology and systems biology would certainly guide researchers, students or readers to conduct their research in the emerging area(s) of interest. It is also expected to be highly beneficial to various stakeholders working in the pharmaceutical and biotechnology industries (R&D), the academic as well as research sectors.

This book describes the forcefields/interatomic potentials that are used in the atomistic-scale and molecular dynamics simulations. It covers mechanisms, salient features, formulations, important aspects and case studies of various forcefields utilized for characterizing various materials (such as nuclear materials and nanomaterials) and applications. This book gives many help to students and researchers who are studying the forcefield potentials and introduces various applications of atomistic-scale simulations to professors who are researching molecular dynamics.

This book explores the measurement of learning effectiveness and the optimization of knowledge retention by modeling the learning process and building the mathematical foundation of multi-space learning theory. Multi-space learning is defined in this book as a micro-process of human learning that can take place in more than one space, with the goal of effective learning and knowledge retention. This book models the learning process as a temporal sequence of concept learning, drawing on established principles and empirical evidence. It also introduces the matroid to strengthen the mathematical foundation of multi-space learning theory and applies the theory to vocabulary and mathematics learning, respectively. The results show that, for vocabulary learning, the method can be used to estimate the effectiveness of a single learning strategy, to detect the mutual interference that might exist between learning strategies, and to predict the optimal combination of strategies. In mathematical learning, it was found that timing is crucial in both first learning and second learning in scheduling optimization to maximize the intersection effective interval. The title will be of interest to researchers and students in a wide range of areas, including educational technology, learning sciences, mathematical applications, and mathematical psychology.