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A Handbook Of Artificial Intelligence In Drug Delivery
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Book Synopsis A Handbook of Artificial Intelligence in Drug Delivery by : Anil K. Philip
Download or read book A Handbook of Artificial Intelligence in Drug Delivery written by Anil K. Philip and published by Academic Press. This book was released on 2023-03-27 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Artificial Intelligence in Drug Design by : Alexander Heifetz
Download or read book Artificial Intelligence in Drug Design written by Alexander Heifetz and published by Humana. This book was released on 2022-11-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial intelligence in Pharmaceutical Sciences by : Mullaicharam Bhupathyraaj
Download or read book Artificial intelligence in Pharmaceutical Sciences written by Mullaicharam Bhupathyraaj and published by CRC Press. This book was released on 2023-11-23 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence and Machine Learning in Drug Design and Development by : Abhirup Khanna
Download or read book Artificial Intelligence and Machine Learning in Drug Design and Development written by Abhirup Khanna and published by John Wiley & Sons. This book was released on 2024-06-21 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Drug Design using Machine Learning by : Inamuddin
Download or read book Drug Design using Machine Learning written by Inamuddin and published by John Wiley & Sons. This book was released on 2022-11-15 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence in Drug Discovery by : Nathan Brown
Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-04 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence in Drug Discovery by : Ankit Gangwal
Download or read book Artificial Intelligence in Drug Discovery written by Ankit Gangwal and published by Independently Published. This book was released on 2021-03-08 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Major disruption worldover is due to AI, blockchain, 3D organ printing and others. Almost all the industries are being affected by AI. Health sector, particularly pharmaceutical sciences is also not an exception. The book has been designed to cover basics and role of AI in drug discovery, including clinical trials and other departments of health and pharmaceutical sciences. All the content has been compiled after referring and mining hundreds of latest and original first-hand updates from inventors, experts, organizations (who/which are engaged in drug discovery research directly or indirectly through AI) like Insilico, Google, Microsoft, INVIDIA, Novartis, Intel, IBM, Exscientia, Berg, Atomwise, XtalPi, Recursion, H2OAi, Recursion, BenevolentAI, Minds.ai, Deep Genomics, AiCure, Trials.ai, GNS Healthcare, MIT, Okwin, Flatiron, Syapse etc. It was unavoidable to explore content from websites and newspapers as authors were interested to cover latest content. All topics are explained in very simple language with clear aim and outcome using flow charts, tables and infographics. Professionals from medical, pharmacy, nursing and dental and medical imaging arena will find this book very useful. Students of all levels will find book very beneficial as few topics have been just touched, few have been shallow in complexity and rest are covered in detail. Full precautions have been exercised to address the needs of pharmacy students so that they can easily and effortlessly understand the subject matter of this book. Recent examples from various corporates, universities and daily life have found place in this unique book in a very explicit manner. At the end, questions have been added for the readers, mainly students. Authors are always open to suggestions, comments from our valuable readers. We wish you a happy reading......
Book Synopsis Artificial Intelligence for Drug Product Lifecycle Applications by : Alberto Pais
Download or read book Artificial Intelligence for Drug Product Lifecycle Applications written by Alberto Pais and published by Elsevier. This book was released on 2024-09-20 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments. Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle Combines timely literature review with clear artworks to improve understanding Examines deep learning and machine learning in drug discovery
Book Synopsis Benefits and risks of using artificial intelligence for pharmaceutical development and delivery by : World Health Organization
Download or read book Benefits and risks of using artificial intelligence for pharmaceutical development and delivery written by World Health Organization and published by World Health Organization. This book was released on 2024-03-25 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: WHO recognizes that artificial intelligence (AI) holds great promise for pharmaceutical development and delivery. However, AI also presents risks and ethical challenges that must be addressed if societies, health systems and individuals are to fully reap its benefits. This discussion paper examines the expanding application of AI to each step of development and deployment of medicines and vaccines. AI is already used in most steps of pharmaceutical development, and, in the future, it is likely that nearly all pharmaceutical products that come to market will have been “touched” by AI at some point in their development, approval or marketing. Although these uses of AI may have a commercial benefit, it is imperative that use of AI also has public health benefit and appropriate governance.
Book Synopsis Artificial Intelligence in Pharmaceutical Sciences (Drug Discovery) by : Ankit Gangwal
Download or read book Artificial Intelligence in Pharmaceutical Sciences (Drug Discovery) written by Ankit Gangwal and published by . This book was released on 2021 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: Major disruption world over is due to artificial intelligence (AI), blockchain, 3D organ printing, precision medicines and others. Almost all the industries are being affected by AI. Pharmaceutical sciences is also not an exception. This book comprising four chapters. Chapter first deals with basics of disruptive innovations and reasons behind these disruptions along with examples from every walk of life. In this chapter industry 4.0 has been discussed along with blockchain, precision medicine, 3D organ printing etc. With this background, chapter number two deals with AI, machine learning and deep learning. This chapter has been designed to cover all the basic topics and examples related to AI, machine learning (ML) and deep learning (DL) and their application in drug discovery in detail. In this chapter, different types of tasks, ML can handle, have been described in a very easy-to-understand fashion, besides types of machine learning (like supervised, unsupervised and reinforcement learning), ML algorithms etc. Basics like definitions of machine learning model, features, vectors, weights, biases, training, testing, data processing etc. all are covered in detail. Various types of artificial neural networks like convolutional neural network, recurrent neural network, autoencoders and its types like variational autoencoder, adversarial autoencoder and much talked about that is generative adversarial network have also been covered in a significant manner. Chapter third has been designed to cover basics and role of AI in drug discovery, including clinical trials and other departments of health and pharmaceutical sciences. More and more pharma companies are using AI and its subsets for increasing productivity in terms of drug discovery (de novo drug design, repurposing), manufacturing, clinical trials (subject selection, data recording and analysing, minimizing dropping out of subjects etc.), synthesis and others. All the content has been compiled after referring and mining hundreds of latest and original first-hand updates from inventors, experts, organizations (who/which are engaged in drug discovery research directly or indirectly through AI) like Insilico, Google, Microsoft, INVIDIA, Novartis, Intel, IBM, Exscientia, Berg, Atomwise, XtalPi, Recursion, H2OAi, Recursion, BenevolentAI, Minds.ai, Deep Genomics, AiCure, Trials.ai, GNS Healthcare, MIT, Okwin, Flatiron, Syapse etc. It was unavoidable to explore content from websites and newspapers as authors were interested to cover latest content. All topics are explained in very simple language with clear aim and outcome using flow charts, tables and infographics. Students of all levels will find book very beneficial as few topics have been just touched, few have been shallow in complexity and rest are covered in detail. Full precaution has been exercised to address the needs of learners from non-maths background so that they can easily and effortlessly understand the subject matter of this book. Recent examples from various corporates, universities and daily life have found place in this unique book in a very explicit manner. At relevant section, coding that is programming basics have been shared for beginners who wants to write python codes on their own. This has been explained in step-by-step manner in a reproducible manner, starting from installing conda environment on their local machine to importing package like numpy, pandas etc. in their jupyter notebook. Famous examples of Iris database, Pima diabetes dataset, Wisconsin breast cancer database and others have been shared as screenshots so that learners can type exactly same codes in their jupyter notebook and learn how to import excel CSV file that is respective dataset, defining x and y variables, splitting and defining % of train and test dataset, running model and finally analysing the prediction. This has been done to bring non-maths learners as close as possible to these topics which are running the world.
Book Synopsis Artificial Intelligence and Machine Learning in Drug Design and Development by : Abhirup Khanna
Download or read book Artificial Intelligence and Machine Learning in Drug Design and Development written by Abhirup Khanna and published by John Wiley & Sons. This book was released on 2024-07-18 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence Platform For Molecular Targeted Therapy: A Translational Science Approach by : Ariel Fernandez
Download or read book Artificial Intelligence Platform For Molecular Targeted Therapy: A Translational Science Approach written by Ariel Fernandez and published by World Scientific. This book was released on 2021-03-12 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.
Book Synopsis Artificial Intelligence in Oncology Drug Discovery and Development by : John W. Cassidy
Download or read book Artificial Intelligence in Oncology Drug Discovery and Development written by John W. Cassidy and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare by : Mark Chang
Download or read book Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare written by Mark Chang and published by CRC Press. This book was released on 2020-05-07 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Drug Delivery by : Ashim Mitra
Download or read book Advanced Drug Delivery written by Ashim Mitra and published by John Wiley & Sons. This book was released on 2013-08-26 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides both fundamentals and new and emerging applications Advanced Drug Delivery brings readers fully up to date with the state of the science, presenting the basics, formulation strategies, and therapeutic applications of advanced drug delivery. The book demonstrates how core concepts of pharmaceutical sciences, chemistry, and molecular biology can be combined and applied in order to spark novel ideas to design and develop advanced drug delivery systems for the treatment of a broad range of human diseases. Advanced Drug Delivery features contributions from an international team of pharmaceutical scientists. Chapters reflect a thorough review and analysis of the literature as well as the authors' firsthand experience developing drug delivery systems. The book is divided into four parts: Part I, Introduction and Basics of Advanced Drug Delivery, explores physiological barriers, stability, transporters, and biomaterials in drug delivery Part II, Strategies for Advanced Drug Delivery, offers tested and proven strategies for advanced delivery of both small molecules and macromolecules Part III, Translational Research of Advanced Drug Delivery, focuses on regulatory considerations and translational applications of advanced drug delivery systems for the treatment of cardiovascular diseases, cancer, sexually transmitted diseases, ophthalmic diseases, and brain diseases Part IV, Future Applications of Advanced Drug Delivery in Emerging Research Areas, examines stem cell research, cell-based therapeutics, tissue engineering, and molecular imaging Each chapter provides objectives and assessment questions to help readers grasp key concepts and assess their knowledge as they progress through the book. Advanced Drug Delivery is recommended for graduates and upper-level undergraduates in the pharmaceutical sciences who need a solid foundation in the basics. It is also recommended for pharmaceutical professionals who want to take advantage of new and emerging applications in advanced drug delivery systems.
Book Synopsis Drug Delivery Systems by : Kewal K. Jain
Download or read book Drug Delivery Systems written by Kewal K. Jain and published by Springer Science & Business Media. This book was released on 2008-03-07 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this concise and systematic book, a team of experts select the most important, cutting-edge technologies used in drug delivery systems. They take into account significant drugs, new technologies such as nanoparticles, and therapeutic applications. The chapters present step-by-step laboratory protocols following the highly successful Methods in Molecular BiologyTM series format, offering readily reproducible results vital for pharmaceutical physicians and scientists.
Book Synopsis Data Science, AI, and Machine Learning in Drug Development by : Harry Yang
Download or read book Data Science, AI, and Machine Learning in Drug Development written by Harry Yang and published by CRC Press. This book was released on 2022-10-04 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
