Drug Discovery Approaches for the Treatment of Neurodegenerative Disorders: Alzheimer's Disease examines the drug discovery process for neurodegenerative diseases by focusing specifically on Alzheimer's Disease and illustrating the paradigm necessary to ensure future research and treatment success. The book explores diagnosis, epidemiology, drug discovery strategies, current therapeutics, and much more to provide a holistic approach to the discovery, development, and treatment of Alzheimer's Disease. Through its coverage of the latest research in targeted drug design, preclinical studies, and mouse and rat models, the book is a must-have resource for all pharmaceutical scientists, pharmacologists, neuroscientists, and clinical researchers working in this area. It illustrates why these drugs tend to fail at the clinical stage, and examines Alzheimer's Disease within the overall context of improving the drug discovery process for the treatment of other neurodegenerative disorders. - Provides a compilation of chemical considerations required in drug discovery for the treatment of neurodegenerative disorders - Examines different classes of compounds currently being used in discovery and development stages - Explores in vitro and in vivo models with respect to their ability to translate these models to human conditions - Distills the most significant information across multiple areas of Alzheimer's disease research to provide a single, comprehensive, and balanced resource

"AI and Biotech in Pharmaceutical Research: Synergies in Drug Discovery" offers a comprehensive exploration of the transformative role AI plays in modern drug discovery and development. The book delves into the integration of artificial intelligence with biotechnological advances, highlighting how these synergies are revolutionizing every stage of the pharmaceutical research process. From the basics of drug discovery to cutting-edge applications in personalized medicine and rare diseases, each chapter unravels the complexities of AI-driven approaches. It covers the impact of machine learning, predictive modeling, and computational biology, while also addressing ethical considerations, algorithmic bias, and regulatory challenges. Real-world case studies and success stories provide tangible examples of AI's potential to accelerate drug development and address unmet medical needs. The book also forecasts future trends, emphasizing the importance of interdisciplinary collaboration, innovative startups, and emerging technologies like blockchain. A must-read for professionals, researchers, and enthusiasts, this book presents a forward-looking view of how AI is reshaping the pharmaceutical landscape, driving innovation, and ultimately improving global health outcomes.

Knowledge of the basic mechanisms of human disease is essential for any student or professional engaged in drug research and development. Functional gene analysis (genomics), protein analysis (proteomics), and other molecular biological techniques have made it possible to understand these cellular processes, opening up exciting opportunities for no

Integrating various technologies with informational systems provides vast improvements to the overall research and development that occur in the biopharmaceutical industry. One of the first books to explore this area, Functional Informatics in Drug Discovery examines all aspects of technology integration and information flow in a biopharmaceutical

This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders. This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features: ● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection. ● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders. ● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

The multidisciplinary applications of natural science in drug discovery are essential for identifying and developing new drugs and represent a promising area of research. Drug discovery is a complex and challenging process that involves multiple disciplines, including biology, chemistry, pharmacology, and medicine. One of the most interesting areas of drug discovery is using natural science, which involves the study of natural products and their potential therapeutic benefits, in which the research literature has previously been lacking appropriate attention. Multidisciplinary Applications of Natural Science for Drug Discovery and Integrative Medicine is a research book that bridges the gap between traditional and natural science approaches to identify new drug molecules in treating various diseases. The book focuses on allopathic and complementary medicine. It takes a cross-disciplinary examination of biology, chemistry, pharmacology, mathematics, physics, and medicine. This book is suitable for researchers, post-doctoral fellows, graduate students, and post-graduate students interested in exploring natural science's multidisciplinary applications in drug discovery and integrative medicine.

Within the context of global health challenges posed by intractable neurodegenerative diseases like Alzheimer's and Parkinson's, the significance of early diagnosis is critical for effective intervention, and scientists continue to discover new methods of detection. However, actual diagnosis goes beyond detection to include a significant analysis of combined data for many cases, which presents a challenge of several complicated calculations. Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases stands as a groundbreaking work at the intersection of artificial intelligence and neuroscience. The book orchestrates a symphony of cutting-edge techniques and progressions in early detection by assembling eminent experts from the domains of deep learning and neurology. Through a harmonious blend of research areas and pragmatic applications, this monumental work charts the transformative course to revolutionize the landscape of early diagnosis and management of neurodegenerative disorders. Within the pages, readers will embark through the intricate landscape of neurodegenerative diseases, the fundamental underpinnings of deep learning, the nuances of neuroimaging data acquisition and preprocessing, the alchemy of feature extraction and representation learning, and the symphony of deep learning models tailored for neurodegenerative disease diagnosis. The book also delves into integrating multimodal data to augment diagnosis, the imperative of rigorously evaluating and validating deep learning models, and the ethical considerations and challenges entwined with deep learning for neurodegenerative diseases.

Frontiers in CNS Drug Discovery is a book series devoted to publishing reviews which highlight the latest advances in drug design and discovery for disorders of the central nervous system (CNS). Eminent scientists write contributions on all areas of CNS drug design and drug discovery, including medicinal chemistry, in-silico drug design, combinatorial chemistry, high-throughput screening, drug targets, recent important patents, and structure-activity relationships. The book series is essential reading for all pharmaceutical scientists involved in CNS drug design and discovery who wish to keep abreast of rapid and important developments in the field. The third volume of this series brings reviews on brain tumor treatment, neurodegeneration, hyperalgesia, cholinesterase inhibition and much more.