This book is not going to be an exhaustive survey covering all aspects of rational drug design. Instead, it is going to provide critical know-how through real-world examples. Relevant case studies will be presented and analyzed to illustrate the following: how to optimize a lead compound whether one has high or low levels of structural information; how to derive hits from competitors' active compounds or from natural ligands of the targets; how to springboard from competitors' SAR knowledge in lead optimization; how to design a ligand to interfere with protein-protein interactions by correctly examining the PPI interface; how to circumvent IP blockage using data mining; how to construct and fully utilize a knowledge-based molecular descriptor system; how to build a reliable QSAR model by focusing on data quality and proper selection of molecular descriptors and statistical approaches. A Practical Guide to Rational Drug Design focuses on computational drug design, with only basic coverage of biology and chemistry issues, such as assay design, target validation and synthetic routes. - Discusses various tactics applicable to daily drug design - Readers can download the materials used in the book, including structures, scripts, raw data, protocols, and codes, making this book suitable resource for short courses or workshops - Offers a unique viewpoint on drug discovery research due to the author's cross-discipline education background - Explores the author's rich experiences in both pharmaceutical and academic settings

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

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.

'Artificial Intelligence in Medicine' is a comprehensive guide exploring the transformative impact of artificial intelligence (AI) in healthcare. The book delves into the foundational concepts and historical development of AI in medicine, highlighting data collection, preprocessing, and feature extraction crucial for medical applications. It showcases the benefits of AI, such as accurate diagnoses and personalized treatments, while addressing ethical and regulatory considerations.The book examines the practical aspects of AI implementation in clinical practice and emphasizes the human aspect of AI in healthcare and patient engagement. Readers can gain insights into the role of AI in clinical decision support, collaborative learning, and knowledge sharing. It concludes with a glimpse into the future of AI-driven healthcare, exploring the emerging technologies and trends in the rapidly evolving field of AI in medicine.

In pharmaceutical research, solubility plays a key part in the assessment of pharmacokinetic risks. Poor drug absorption, reduced efficacy, excessive metabolism, and adverse reactions are frequently related to issues of drug solubility. During early discovery research at pharmaceutical companies, many thousands of molecules are considered. Most are rejected due to perceived unfavorable properties. Here the author uses the Wiki-pS0TM database, which forms the backbone of this unique handbook. Also discussed is the emerging class of therapeutically promising research molecules called PROTACs (proteolysis-targeting chimeras), showing a propensity for ‘undruggable’ targets. FEATURES • A comprehensive and unique listing of measured aqueous intrinsic solubility focusing on drug-like and drug-relevant molecules. • The database can be used to predict the solubility of research pharmaceutical molecules. • Includes downloadable files of the database (.csv format). • The mining of the database can result in a better design of solubility assay protocols, leading to better quality of measurements. • Artificial intelligence and Bayesian statistics will likely be key to this subject area in the future. Alex Avdeef has been an American Association of Pharmaceutical Scientists (AAPS) Fellow since 2014, a former visiting senior research fellow at King’s College London, and is the author of Absorption and Drug Development (2nd ed., Wiley, 2012). In 2021, the book was translated into Chinese, by translators affiliated with the China Food and Drug Administration. For nearly 50 years, he has been teaching, researching, and developing methods, instruments, and analysis software for the measurement of ionization constants, solubility, dissolution, and permeability of drugs. His accomplishments in the development of instrumentation include several well-known instruments that are or recently have been manufactured by leading companies in the instrument market, including Thermo Fisher Scientific, Sirius Analytical, and Pion Inc. He has over 200 technical publications in primary scientific journals and book chapters. He has written several comprehensive technical guides and is a co-inventor on six patents. He cofounded Sirius Analytical (UK) in 1989, pION Inc. (USA) in 1996, and founded in–ADME Research (New York City) in 2011. His other positions were at Orion Research, Syracuse University, UC Berkeley, and Caltech.

This book constitutes refereed proceedings of the First International First International Conference on Big Data, Machine Learning, and Applications, BigDML 2019, held in Silchar, India, in December. The 6 full papers and 3 short papers were carefully reviewed and selected from 152 submissions. The papers present research on such topics as computing methodology; machine learning; artificial intelligence; information systems; security and privacy.

This book presents various computer-aided drug discovery methods for the design and development of ligand and structure-based drug molecules. A wide variety of computational approaches are now being used in various stages of drug discovery and development, as well as in clinical studies. Yet, despite the rapid advances in computer software and hardware, combined with the exponential growth in the available biological information, there are many challenges that still need to be addressed, as this book shows. In turn, it shares valuable insights into receptor-ligand interactions in connection with various biological functions and human diseases. The book discusses a wide range of phylogenetic methods and highlights the applications of Molecular Dynamics Simulation in the drug discovery process. It also explores the application of quantum mechanics in order to provide better accuracy when calculating protein-ligand binding interactions and predicting binding affinities. In closing, the book provides illustrative descriptions of major challenges associated with computer-aided drug discovery for the development of therapeutic drugs. Given its scope, it offers a valuable asset for life sciences researchers, medicinal chemists and bioinformaticians looking for the latest information on computer-aided methodologies for drug development, together with their applications in drug discovery.

CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY Explore chemometric and cheminformatic techniques and tools in aquatic toxicology Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms. You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods. Readers will also benefit from the inclusion of: A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data mining An exploration of aquatic toxicity databases, chemometric software tools, and webservers Practical examples and case studies to highlight and illustrate the concepts contained within the book A concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity data Perfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.

Flow Batteries The premier reference on flow battery technology for large-scale, high-performance, and sustainable energy storage From basics to commercial applications, Flow Batteries covers the main aspects and recent developments of (Redox) Flow Batteries, from the electrochemical fundamentals and the materials used to their characterization and technical application. Edited by a team of leading experts, including the “founding mother of vanadium flow battery technology” Maria Skyllas-Kazacos, the full scope of this revolutionary technology is detailed, including chemistries other than vanadium and organic flow batteries. Other key topics covered in Flow Batteries include: Flow battery computational modeling and simulation, including quantum mechanical considerations, cell, stack, and system modeling, techno-economics, and grid behavior A comparison of the standard vanadium flow battery variant with new and emerging flow batteries using different chemistries and how they will change the field Commercially available flow batteries from different manufacturers, their technology, and application ranges The pivotal role of flow batteries in overcoming the global energy crisis Flow Batteries is an invaluable resource for researchers and engineers in academia and industry who want to understand and work with this exciting new technology and explore the full range of its current and future applications.

Pharmacological biotechnology is applied to and used to study drug development, working mechanisms, diagnosis, and therapies. This textbook covers the whole range of experiments related to pharmacology. It also contains basic laboratory safety guidelines along with the basic calculations and formulas used in a laboratory. Each chapter starts with an introduction/theory into the basic approach followed by detailed methods sections with easy-to-follow protocols and comprehensive troubleshooting, calculations and possible questions for examination. The target group is researchers who are studying pharmacological biotechnology in the laboratory.