Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. - Provides practical information on how to choose and use appropriate computational tools - Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format - Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics

Stem Cells: An Alternative Therapy for COVID-19 and Cytokine Storm provides mechanistic insights into the role of stem cells to combat COVID-19 outbreak and other pathologies where cytokines storm is the cause of concern for e.g., radiation exposure, multiple organ failure and sepsis. There has been an increase in number of cases of new diseases in the last decade, including mucormycosis, Zika virus, H1N1 influenza virus, among others. These diseases can be characterized by the induction of cytokine storm, which is mainly responsible for morbidity and mortality.Stem cell therapy has emerged as a potential treatment for viral diseases, including, but not limited to, COVID-19. Interestingly, clinical trials in patients with COVID-19 complications depicted faster recovery in patients post mesenchymal stem cells therapy owing to the decreased cytokines levels, anti-viral effects and regeneration of the infected tissue. - Evaluates the role of MSCs to combat cytokine storm, the challenges regarding COVID-19 therapy and how they can be countered using stem cells, and the risk of opportunistic infections post COVID-19 - Presents how stem cell therapy has emerged as a potential treatment for viral diseases, including, but not limited to, COVID-19 - Provides a detailed understanding of the novel coronavirus, with an emphasis on therapeutic aspects

Computational methods and understanding computational models are important in modern drug discovery. The book focuses on computational approaches that can improve the development of in silico methodologies. It includes lead hit methods, docking algorithms, computational chiral compounds, structure-based drug design, GROMACS and NAMD, structural genomics, toxicity prediction, enzyme inhibitors and peptidomimetic therapeutics

This book describes the processes that are involved in the development of new drugs. The authors discuss the history, role of natural products and concept of receptor interactions with regard to the initial stages of drug discovery. In a single, highly readable volume, it outlines the basics of pharmacological screening, drug target identification, and genetics involved in early drug discovery. The final chapters introduce readers to stem therapeutics, pharmacokinetics, pharmacovigilance, and toxicological testing. Given its scope, the book will enable research scholars, professionals and young scientists to understand the key fundamentals of drug discovery, including stereochemistry, pharmacokinetics, clinical trials, statistics and toxicology.

Translational Bioinformatics is an emerging field in the direction of biomedical research. High throughput technologies can be applied to the generated biological data to develop the vaccine and personalized medicine. This volume consists of the chapters from different stalwart of the field covering the topics such as drug development, vector engineering, vaccine development and translational genomics. Chapters covered in this volume discuss the translational research related with cancer, Alzheimer disease and cardiovascular diseases. This volume includes the chapter describing the importance of computational resources and chemoinformatics for the translational health research. How Omics studies are helping to translate the laboratory data into the development of tools which are beneficial in the clinics have been described. How translational bioinformatics helpful in plant genomics to improve the crops have also been included in this volume. This volume has a chapter which describes the secrets of resistance development and further how these resistance are associated with human infectious diseases. This volume will be useful to the early career researcher in the development of research idea and develop their methodologies in the direction of bioinformatics and it will also give the insight to translate their findings. - Translational Bioinformatics - Health Informatics - Multi-omics

Biochemical and Molecular Pharmacology in Drug Discovery comprises fundamental biochemical and molecular aspects of drug discovery and basic understanding of modern drug discovery approaches along with certain key topics related to molecular pharmacology of drugs and therapeutics. Molecular pharmacology has gained significant momentum among researchers, scientists, and academicians because of its increasing interest in drug discovery research across the globe. Molecular pharmacology involves a fundamental understanding of drug actions at the molecular level with the help of several tools and techniques of biochemical and molecular biology. It explains the phenomena of drug-target interactions considering different biochemical systems and cellular strategies. With the advent of technologies, current advances and research trends move toward molecular and/or target-based drug design and discovery. Through this book, readers will be able to gain skills and knowledge with a thorough understanding of the subject of biochemical and molecular pharmacology, in a comprehensive and systematic manner with special reference to recent advances in drug discovery research. - Highlights the fundamentals of biochemical and molecular aspects, with reference to drug discovery research - Depicts modern drug discovery approaches such as reverse pharmacology, drug repositioning, and CADD in the context of current research updates - Summarizes recent developments in the molecular pharmacology of novel drugs/ therapeutic molecules

Computational Biology for Stem Cell Research is an invaluable guide for researchers as they explore HSCs and MSCs in computational biology. With the growing advancement of technology in the field of biomedical sciences, computational approaches have reduced the financial and experimental burden of the experimental process. In the shortest span, it has established itself as an integral component of any biological research activity. HSC informatics (in silico) techniques such as machine learning, genome network analysis, data mining, complex genome structures, docking, system biology, mathematical modeling, programming (R, Python, Perl, etc.) help to analyze, visualize, network constructions, and protein-ligand or protein-protein interactions. This book is aimed at beginners with an exact correlation between the biomedical sciences and in silico computational methods for HSCs transplantation and translational research and provides insights into methods targeting HSCs properties like proliferation, self-renewal, differentiation, and apoptosis. Modeling Stem Cell Behavior: Explore stem cell behavior through animal models, bridging laboratory studies to real-world clinical allogeneic HSC transplantation (HSCT) scenarios. Bioinformatics-Driven Translational Research: Navigate a path from bench to bedside with cutting-edge bioinformatics approaches, translating computational insights into tangible advancements in stem cell research and medical applications. Interdisciplinary Resource: Discover a single comprehensive resource catering to biomedical sciences, life sciences, and chemistry fields, offering essential insights into computational tools vital for modern research.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

This unique volume presents AI in relation to ethical points of view in handling big data sets. Issues such as algorithmic biases, discrimination for specific patterns and privacy breaches may sometimes be skewed to affect research results so that certain fields to appear more appealing to funding agencies. The discussion on the ethics of AI is highly complex due to the involvement of many international stakeholders such as the UN, OECD, parliaments, industry groups, professional bodies, and individual companies. The issue of reliability is addressed including the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots and cyber security tools. Features Discusses artificial intelligence and ethics, the challenges and opportunities Presents the issue of reliability in the emergence of synthetic life, 5G networks, intermingling of human artificial intelligence, nano-robots, and cyber security tools Ethical responsibility and reasoning for using AI in Big Data Addresses practicing medicine and ethical issues when applying artificial intelligence