Computational Approaches For Novel Therapeutic And Diagnostic Designing To Mitigate Sars Cov2 Infection

Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection: Revolutionary Strategies to Combat Pandemics compiles information about various computational bioinformatic approaches that can help combat viral infection. The book includes working knowledge of various molecular docking and molecular dynamic simulation approaches that have been exploited for drug repurposing and drug designing purpose. In addition, it sheds light on reverse vaccinomics and immunoinformatic approaches for vaccine designing against SARS-CoV2 infection. This book is an essential resource for researchers, bioinformaticians, computational biologists, computational chemists and pharmaceutical companies who are working on the development of effective and specific therapeutic interventions and point-of-care diagnostic devices using various computational approaches. Covers computational based approaches for designing and repurposing drugs Discusses immunoinformatic and reverse vaccinomic approaches for effective vaccine design Categorizes information about artificial intelligence-based drug screening and diagnostic tools

Smart Diagnostics for Neurodegenerative Disorders: Neuro-sensors explores all available biosensor-based approaches and technologies as well as their use in the diagnosis, prognosis and therapeutic management of a variety of neurological disorders such as Alzheimer's, Parkinson's and epileptic disorders. The book also discusses contemporary and revolutionary biosensor platforms that are being used to produce a quantitative quick lab-on-a-chip point-of-care (POC) assay for several types of predictive and diagnostic biomarkers linked with neurodegenerative disorders. It offers a combinatorial strategy for learning recent advances and designing new biosensor-based technologies in the fields of medical science, engineering and biomedical technology. Early detection of neurological conditions has the potential to treat the disease and extend the life expectancy of patients. Recent improvements in biosensor-based approaches that target specific cell surface biomarkers can be used for early detection of neurodegenerative disease. Provides an in-depth understanding of biomarkers associated with neurodegenerative disease to build and create a variety of biosensors Presents biosensor-based strategies to create and construct enhanced platforms for quick diagnosis of biomarkers linked to a variety of neurological illnesses Discusses the current challenges and future trends in developing diagnostic devices for early detection of neurodegenerative disorders, presenting new avenues for more sensitive and selective point-of-care devices

This book presents a complete overview of COVID-19 and provides a series of in-depth analyses of the literature and a comprehensive discussion and perspectives on promising anti-SARS-CoV-2 agents based on the system of ethnopharmacology, which covers Chinese medicine, traditional medicines of India and Africa, Turkish folk medicine, essential oils, and some well-known medicinal plants. In this book, the current status of therapeutic strategies against COVID-19 was summarized and a perspective of potential options for the future was proposed. Based on ethnopharmacology, some well-established traditional herbal formulations and bioactive compounds with anti-virus activity were repurposed for managing COVID-19 and post-illness, including neurological disorders and kidney illness. Using drug discovery tools, promising immune enhancers were explored from phytochemicals against SARS-CoV-2. Nutraceuticals from dietary plants, spices, and fruits with anti-virus and antioxidant activities were recommended to prevent infection or severe illness caused by emerging variants. With the aid of systems pharmacology, researchers have gained insights into possible molecular mechanisms of anti-SARS-CoV-2 activity and have predicted a range of candidate compounds from medicinal plants to combat COVID-19. This book explores the current knowledge of drug discovery and ethnopharmacology for managing coronavirus diseases. The content proved the anti-SARS-CoV-2 activity from natural products and traditional herbal medicines. It contributes to the management of global public health and fits the goal of establishing “Good Health and Well-Being,” which is one of “The Sustainable Development Goals (SDGs) 2030".

Software and Programming Tools in Pharmaceutical Research is a detailed primer on the use for computer programs in the design and development of new drugs. Chapters offer information about different programs and computational techniques in pharmacology. The book will help readers to harness computer technologies in pharmaceutical investigations. Readers will also appreciate the pivotal role that software applications and programming tools play in revolutionizing the pharmaceutical industry. The book includes nine structured chapters, each addressing a critical aspect of pharmaceutical research and software utilization. From an introduction to pharmaceutical informatics and computational chemistry to advanced topics like molecular modeling, data mining, and high-throughput screening, this book covers a wide range of topics. Key Features: · Practical Insights: Presents practical knowledge on how to effectively utilize software tools in pharmaceutical research. · Interdisciplinary Approach: Bridges the gap between pharmaceutical science and computer science · Cutting-Edge Topics: Covers the latest advancements in computational drug development, including data analysis and visualization techniques, drug repurposing, pharmacokinetic modelling and screening. · Recommendations for Tools: Includes informative tables for software tools · Referenced content: Includes scientific references for advanced readers The book is an ideal primer for students and educators in pharmaceutical science and computational biology, providing a comprehensive foundation for this rapidly evolving field. It is also an essential resource for pharmaceutical researchers, scientists, and professionals looking to enhance their understanding of software tools and programming in drug development.

Drug repurposing (or drug repositioning) is defined as the process of identifying new pharmacological indications of old, existing, investigational, or FDA-approved drugs for use in the treatment of diseases other than the drugs’ original intended therapeutic use. Drug Repurposing - Advances, Scopes and Opportunities in Drug Discovery delivers up-to-date information on the identification of newer uses, molecular mechanisms, and novel targets of existing drug candidates through the application of various experimental, biophysical, and computational approaches and techniques. Chapters discuss recent advances in drug repurposing strategies that are currently being used in the discovery and development of drugs against difficult-to-treat, rare, and life-threatening diseases, including microbial infections, COVID-19, parasitic diseases, cardiovascular diseases, neurological disorders, and cancer. The book also discusses the modern experimental assays (HTS) and computational techniques including informatics and databases, molecular docking and dynamics, artificial intelligence and machine learning, virtual screening and pharmacophore modeling, proteomics and metabolomics, and network pharmacology and systems biology approaches. Some of the key features of the book are: • Presents the strategies available for the development of drugs by drug repurposing approaches through various experimental and computational techniques for the treatment of difficult-to-treat, rare, and deadly diseases • Summarizes the latest advances in the application of drug repurposing strategies, techniques, and approaches in the discovery and development of drugs • Depicts drug development approaches from existing drug candidates and/or lead molecules through modern experimental assays, biophysical tools, and computational techniques Written by a global team of experts, this book is useful for drug discovery scientists, drug developers, medicinal chemists, phytochemists, pharmacologists, clinicians, biochemists, biomedical scientists, healthcare professionals, researchers, teaching faculty, and students.

This book, Drug Metabolism and Pharmacokinetics, delivers the latest information on biotransformation and other pharmacokinetic parameters required to assess drug safety in drug discovery and development. Drug metabolism and pharmacokinetics (DMPK) is considered a rational strategy for the design, optimization, and selection of successful drug candidates in the pre-clinical stages of drug discovery. The evaluation of various pharmacokinetic parameters such as aqueous solubility, lipophilicity, cell permeability, bioavailability, protein binding, and metabolism and elimination half-life, drug–drug interactions (DDIs), and toxicokinetics is essential for a candidate drug to become a successful therapeutic agent. This book discusses recent advances in in-silico tools and in vitro/in vivo experimental techniques applied in pharmacokinetic, metabolomic, and bioanalytical studies. Key Features: • Presents pharmacokinetic and biotransformation parameters used for pre-clinical safety evaluations • Depicts experimental and biophysical techniques used in bioanalytical and metabolomic studies • Summarizes in silico, in vitro, and in vivo methods used in drug–enzyme interactions, drug– drug interactions, and toxicokinetics Written by a global team of experts, this book is a valuable resource for drug discovery scientists, drug developers, medicinal chemists, pharmacologists, biochemists, clinicians, biomedical scientists, healthcare professionals, researchers, teaching faculty, and students.

Artificial intelligence (AI) has the potential to significantly improve efficiency, reduce costs, and increase the speed and accuracy of financial decision-making, making it an increasingly important tool for financial professionals. One way that AI can improve efficiency in finance is by automating tasks and processes that are time-consuming and repetitive for humans. For example, AI algorithms can be used to analyze and process large amounts of data, such as financial statements and market data, in a fraction of the time that it would take a human to do so. This can allow financial professionals to focus on higher-value tasks, such as interpreting data and making strategic decisions, rather than being bogged down by mundane tasks. AI can also reduce costs in finance by increasing automation and eliminating the need for certain tasks to be performed manually. This can result in cost savings for financial institutions, which can then be passed on to customers in the form of lower fees or better services. AI can be used to identify unusual patterns of activity that may indicate fraudulent behavior. This can help financial institutions reduce losses from fraud and improve customer security. AI-powered chatbots and virtual assistants can help financial institutions provide faster, more efficient customer service, particularly when it comes to answering common questions and handling routine tasks. Some financial institutions are using AI to analyze market data and make trades in real-time. AI-powered trading algorithms can potentially make faster and more accurate trading decisions than humans. In terms of speed and accuracy, AI algorithms can analyze data and make decisions much faster than humans, and can do so with a high degree of accuracy. This can be particularly useful in fast-moving financial markets, where quick and accurate decision-making can be the difference between success and failure. This book highlights how AI in finance can improve efficiency, reduce costs, and increase the speed and accuracy of financial decision-making. Moreover, the book also focuses on how to ensure the responsible and ethical use of AI in finance. This book is a valuable resource for students, scholars, academicians, researchers, professionals, executives, government agencies, and policymakers interested in exploring the role of artificial intelligence (AI) in finance. Its goal is to provide a comprehensive overview of the latest research and knowledge in this area, and to stimulate further inquiry and exploration.