A Handbook Of Artificial Intelligence In Drug Delivery
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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
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 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.
Inamuddin,Tariq A. Altalhi,Jorddy N. Cruz,Moamen Salah El-Deen Refat
Author : Inamuddin,Tariq A. Altalhi,Jorddy N. Cruz,Moamen Salah El-Deen Refat Publisher : John Wiley & Sons Page : 388 pages File Size : 40,8 Mb Release : 2022-11-15 Category : Medical ISBN : 9781394166282
Drug Design using Machine Learning by Inamuddin,Tariq A. Altalhi,Jorddy N. 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.
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.
Artificial Intelligence in Drug Discovery by Ankit Gangwal Pdf
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......
Artificial Intelligence for Drug Product Lifecycle Applications by Alberto Pais,Carla Vitorino,Sandra Nunes,Tânia Cova Pdf
Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and post-approval phase. The book gives methods for each of the drug development steps, from the Fundamentals up to Post-approval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular application in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout 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 will be 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 Covers timely literature review combined with clear artwork to improve understanding Examines deep learning, machine learning in drug discovery
Artificial Intelligence Platform For Molecular Targeted Therapy: A Translational Science Approach by Ariel Fernandez Pdf
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.
Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering by Sisodia, Dilip Singh,Pachori, Ram Bilas,Garg, Lalit Pdf
Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare engineering applications have been created that help to address patient healthcare and outcomes such as identifying diseases and gathering patient information. Advancements in AI applications in healthcare continue to be sought to aid rapid disease detection, health monitoring, and prescription drug tracking. TheHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering is an essential scholarly publication that provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. Featuring a wide range of topics such as genetic algorithms, mobile robotics, and neuroinformatics, this book is ideal for engineers, technology developers, IT consultants, hospital administrators, academicians, healthcare professionals, practitioners, researchers, and students.
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 Healthcare by Adam Bohr,Kaveh Memarzadeh Pdf
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Artificial Intelligence in Pharmaceutical Sciences (Drug Discovery) by Ankit Gangwal Pdf
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.
Artificial Neural Network for Drug Design, Delivery and Disposition by Munish Puri,Yashwant Pathak,Vijay Kumar Sutariya,Srinivas Tipparaju,Wilfrido Moreno Pdf
Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs. This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences. Written by leading academic and industry scientists who have contributed significantly to the field and are at the forefront of artificial neural network (ANN) research Focuses on ANN in drug design, discovery and delivery, as well as adopted methodologies and their applications to the treatment of various diseases and disorders Chapters cover important topics across the pharmaceutical process, such as ANN in structure-based drug design and the application of ANN in modern drug discovery Presents the future potential of ANN-based strategies in biomedical image analysis and much more
