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

In the dynamic environment of healthcare, the fusion of Computational Intelligence and Healthcare Industry 4.0 has enabled remarkable advancements in disease detection and analysis. However, a critical challenge persists – the limitations of current computational intelligence approaches in dealing with small sample sizes. This setback hampers the performance of these innovative models, hindering their potential impact on medical applications. As we stand at the crossroads of technological innovation and healthcare evolution, the need for a solution becomes paramount. Advances in Computational Intelligence for the Healthcare Industry 4.0 is a comprehensive guide addressing the very heart of this challenge. Designed for academics, researchers, healthcare professionals, and stakeholders in Healthcare Industry 4.0, this book serves as a source of innovation. It not only illuminates the complexities of computational intelligence in healthcare but also provides a roadmap for overcoming the limitations posed by small sample sizes. From fundamental principles to innovative concepts, this book offers a holistic perspective, shaping the future of healthcare through the lens of computational intelligence and Healthcare Industry 4.0.

Computational Phytochemistry, Second Edition, explores how recent advances in computational techniques and methods have been embraced by phytochemical researchers to enhance many of their operations, refocusing and expanding the possibilities of phytochemical studies. By applying computational aids and mathematical models to extraction, isolation, structure determination, and bioactivity testing, researchers can obtain highly detailed information about phytochemicals and optimize working approaches. This book aims to support and encourage researchers currently working with or looking to incorporate computational methods into their phytochemical work. Topics in this book include computational methods for predicting medicinal properties, optimizing extraction, isolating plant secondary metabolites, and building dereplicated phytochemical libraries. The roles of high-throughput screening, spectral data for structural prediction, plant metabolomics, and biosynthesis are all reviewed before the application of computational aids for assessing bioactivities and virtual screening is discussed. Illustrated with detailed figures and supported by practical examples, this book is an indispensable guide for all those involved with the identification, extraction, and application of active agents from natural products. This new edition captures remarkable advancements in mathematical modeling and computational methods that have been incorporated in phytochemical research, addressing, e.g., extraction, isolation, structure determination, and bioactivity testing of phytochemicals. - Includes step-by-step protocols for various computational and mathematical approaches applied to phytochemical research - Features clearly illustrated chapters contributed by highly reputable researchers - Covers all key areas in phytochemical research, including virtual screening and metabolomics

This book illustrates the importance of natural products as the source for the development of novel drugs for the treatment of neurodegenerative disorders, including Alzheimer's disease. It also highlights the role of reactive oxygen species and altered metal homeostasis in the progression of Alzheimer’s disease and examines the potential of antioxidants and anti-chelating agents in the clinical intervention of neurodegenerative diseases. The book also discusses the role of neuroinflammation in the pathogenesis of Alzheimer’s disease. The chapters provide information about the drug targets, progress in the development of natural product-based therapeutics, biomarkers, fluorescent diagnostic tools, and theranostic for Alzheimer's disease. The book also provides information about the design and synthesis of natural product-based derivatives against the various targets of Alzheimer's disease including epigenetic targets and the metal dyshomeostasis hypothesis. Cutting across different disciplines, this book is a valuable source for neuroscientists, chemical biologists, pharmaceutical researchers, and synthetic biologists.

This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.

BIOINFORMATICS TOOLS FOR Pharmaceutical DRUG PRODUCT DLEVELOPMENT A timely book that details bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies, for drug development in the pharmaceutical and medical sciences industries. The book contains 17 chapters categorized into 3 sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. The following 2 sections include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall. Readers will find in this book: Applications of bioinformatics tools for pharmaceutical drug product development like process development, pre-clinical development, clinical development, commercialization of the product, etc.; The ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach; The broad and deep background, as well as updates, on recent advances in both medicine and AI/ML that enable the application of these cutting-edge bioinformatics tools. Audience The book will be used by researchers and scientists in academia and industry including drug developers, computational biochemists, bioinformaticians, immunologists, pharmaceutical and medical sciences, as well as those in artificial intelligence and machine learning.

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics to foster future research and development in ensemble deep learning and inspire new bioinformatics applications that cannot be attained by using traditional machine learning models. - Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challenges - Presents integrative and multidisciplinary approaches to ubiquitous healthcare - Includes case studies to illustrate the concepts discussed

Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.