Artificial Intelligence For Drug Discovery And Development
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Artificial intelligence for Drug Discovery and Development by Jianfeng Pei,Alex Zhavoronkov Pdf
Topic editor Alex Zhavoronkov is the founder of Insilico Medicine, a company specializing in AI research. He is also a professor at the Buck Institute for Research on Aging. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Artificial Intelligence in Drug Discovery by Nathan Brown Pdf
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
A Handbook of Artificial Intelligence in Drug Delivery by Anil K. Philip,Aliasgar Shahiwala,Mamoon Rashid,Md Faiyazuddin Pdf
A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more. This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies. Focuses on the use of Artificial Intelligence in drug delivery strategies and future impacts Provides insights into how artificial intelligence can be effectively used for the development of advanced drug delivery systems Written by experts in the field of advanced drug delivery systems and digital health
Author : John Cassidy,Belle Taylor Publisher : BoD – Books on Demand Page : 194 pages File Size : 49,8 Mb Release : 2020-09-09 Category : Medical ISBN : 9781789846898
Artificial Intelligence in Oncology Drug Discovery and Development by John Cassidy,Belle Taylor Pdf
There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence.
Artificial intelligence in Pharmaceutical Sciences by Mullaicharam Bhupathyraaj,K. Reeta Vijaya Rani,Musthafa Mohamed Essa Pdf
This cutting-edge reference book discusses the intervention of artificial intelligence in the fields of drug development, modified drug delivery systems, pharmaceutical technology, and medical devices development. This comprehensive book includes an overview of artificial intelligence in pharmaceutical sciences and applications in the drug discovery and development process. It discusses the role of machine learning in the automated detection and sorting of pharmaceutical formulations. It covers nanosafety and the role of artificial intelligence in predicting potential adverse biological effects. FEATURES Includes lucid, step-by-step instructions to apply artificial intelligence and machine learning in pharmaceutical sciences Explores the application of artificial intelligence in nanosafety and prediction of potential hazards Covers application of artificial intelligence in drug discovery and drug development Reviews the role of artificial intelligence in assessment of pharmaceutical formulations Provides artificial intelligence solutions for experts in the pharmaceutical and medical devices industries This book is meant for academicians, students, and industry experts in pharmaceutical sciences, medicine, and pharmacology.
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare by Mark Chang Pdf
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: · Covers broad AI topics in drug development, precision medicine, and healthcare. · Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. · Introduces the similarity principle and related AI methods for both big and small data problems. · Offers a balance of statistical and algorithm-based approaches to AI. · Provides examples and real-world applications with hands-on R code. · Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.
The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry by Stephanie K. Ashenden Pdf
The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide
Artificial Intelligence in Drug Discovery by Nathan Brown Pdf
Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation.
Data Science, AI, and Machine Learning in Drug Development by Harry Yang Pdf
The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise
AI for Drug Development and Well-being by Mark Chang Pdf
Artificial intelligence (AI) is transforming the practice of medicine. It is helping doctors diagnose patients more accurately, predict treatment effects on individuals, and recommend better treatments. AI is also transforming the drug discovery and development process, helping pharmaceutical researchers to identify and design active drug candidates, and reducing the cost of the clinical testing phase. Recently, the FDA moved toward a new, tailored review framework for artificial intelligence-based medical devices (Gottlieb, April 2019).This book is intended for a broad readership: sufficiently straightforward for college freshmen and informative enough for researchers. Chapter 1 gives a gentle introduction to the five ML categories of learning: supervised, unsupervised, reinforcement, evolutionary and swarm intelligence. Chapters 2 through 6 discuss the key concepts of the main methods in each of the five AI categories and their applications in pharmaceutical research & development and healthcare. Chapter 7 provides a state-of-the-art review of AI applications in prescription drug discovery, development, pharmacovigilance, and healthcare. Chapter 8 discusses artificial general intelligence and its controversies, challenges, and likely future directions. A few equations are included to effectively deliver key concepts and 100 key references are cited to meet researchers' needs. The book is a simplified version of my previous book: Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare. Readers who want to get hands on experiences may explore the book with computer code in R.
In "AI Pharma: Artificial Intelligence in Drug Discovery and Development," readers embark on an eye-opening journey through the exciting intersection of two rapidly advancing fields - artificial intelligence (AI) and pharmaceuticals. This comprehensive guide demystifies the revolutionary potential of AI to transform every stage of drug discovery and development, promising unprecedented efficiency, cost-effectiveness, and innovation in an industry vital to global health. The narrative begins with a thorough introduction to AI and the pharmaceutical industry, guiding readers through the complex landscape of drug discovery and development. The history of AI, key concepts such as machine learning, and a wealth of real-world applications across industries set the stage for a deep exploration of AI's transformative potential within the realm of pharmaceuticals. In the heart of the book, the author delves into how AI is poised to disrupt the pharmaceutical industry, offering a detailed exploration of its impact on drug discovery, clinical trials, and drug repurposing. Real-world case studies illustrate the power of AI in accelerating target identification, optimizing lead discovery, streamlining patient recruitment in trials, and unlocking new opportunities in drug repurposing. But with great potential come challenges. This comprehensive guide does not shy away from examining the ethical and regulatory landscape surrounding the use of AI in pharma. It takes a hard look at pressing issues like patient privacy, data security, and regulatory compliance, offering strategies to overcome these challenges while maintaining a forward-thinking perspective. The book culminates in an ambitious forecast of the future. Drawing upon trends in predictive analytics, genomics, IoT, and blockchain, the author paints a vivid picture of the impending AI revolution in pharma, equipping readers with the insights and knowledge they need to navigate this rapidly evolving field. Complete with a glossary of key AI and pharma terms, recommended resources for further learning, thought leader interviews, and updates on recent developments and future trends, "AI Pharma" serves as both a comprehensive introduction for newcomers and a thoughtful exploration for seasoned professionals. This book is a roadmap to the future of pharmaceuticals, illuminating how AI is not just enhancing the industry - it's revolutionizing it.
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare by Mark Chang Pdf
Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: · Covers broad AI topics in drug development, precision medicine, and healthcare. · Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. · Introduces the similarity principle and related AI methods for both big and small data problems. · Offers a balance of statistical and algorithm-based approaches to AI. · Provides examples and real-world applications with hands-on R code. · Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.
Artificial Intelligence in Drug Design by Alexander Heifetz Pdf
This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.
Giant brains; or, Machines that think by Edmund Callis Berkeley Pdf
"Giant brains; or, Machines that think" by Edmund Callis Berkeley. Published by Good Press. Good Press publishes a wide range of titles that encompasses every genre. From well-known classics & literary fiction and non-fiction to forgotten−or yet undiscovered gems−of world literature, we issue the books that need to be read. Each Good Press edition has been meticulously edited and formatted to boost readability for all e-readers and devices. Our goal is to produce eBooks that are user-friendly and accessible to everyone in a high-quality digital format.
Drug Design using Machine Learning by Inamuddin,Tariq Altalhi,Jorddy Neves Cruz,Moamen Salah El-Deen Refat Pdf
DRUG DESIGN USING MACHINE LEARNING The use of machine learning algorithms in drug discovery has accelerated in recent years and this book provides an in-depth overview of the still-evolving field. The objective of this book is to bring together several chapters that function as an overview of the use of machine learning and artificial intelligence applied to drug development. The initial chapters discuss drug-target interactions through machine learning for improving drug delivery, healthcare, and medical systems. Further chapters also provide topics on drug repurposing through machine learning, drug designing, and ultimately discuss drug combinations prescribed for patients with multiple or complex ailments. This excellent overview Provides a broad synopsis of machine learning and artificial intelligence applications to the advancement of drugs; Details the use of molecular recognition for drug development through various mathematical models; Highlights classical as well as machine learning-based approaches to study target-drug interactions in the field of drug discovery; Explores computer-aided technics for prediction of drug effectiveness and toxicity. Audience The book will be useful for information technology professionals, pharmaceutical industry workers, engineers, university researchers, medical practitioners, and laboratory workers who have a keen interest in the area of machine learning and artificial intelligence approaches applied to drug advancements.