Computers

Artificial Intelligence in Drug Discovery

Author: Nathan Brown

Publisher: Royal Society of Chemistry

ISBN:

Category: Computers

Page: 406

View: 317

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 for Drug Development, Precision Medicine, and Healthcare

Author: Mark Chang

Publisher: CRC Press

ISBN:

Category:

Page: 368

View: 453

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcarecovers 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 Pharmaceutical Sciences (Drug Discovery)

Author: Dr Amit Gangwal

Publisher:

ISBN:

Category:

Page: 558

View: 275

The book has been designed to cover all the basic topics and examples related to disruptive innovations and industry 4.0 in general and in particular, pharmaceutical sciences and other branches of healthcare sectors like medical and diagnostic. Major disruption is due to the advent of Artificial Intelligence, Machine Learning, Deep Learning, Blockchain, 3D Organ Printing and others. The book is ahead of its time in the sense that in entire country there is no such subject which is being taught in pharmacy, nursing or medical courses. By the time it becomes part of syllabus, this book is among the best resources in a compiled format for healthcare professionals, academicians and students of pharmacy besides those want to learn from the basic; as content beyond syllabus tool. 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 Artificial Intelligence) like Insilico, Google, Microsoft, INVIDIA, Novartis, Intel, IBM etc. All topics are explained in very simple language with clear aim and outcome using flow charts, tables and infographics of original creators. 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 biology group students so that they can easily and effortlessly understand the subject matter of this book, which requires mathematical skills to grasp the basics of AI. Recent examples from various corporates, universities and daily life have found place in this unique book in a very explicit manner. In initial two chapters, background information has been explained with various comparison and examples, while third chapter focuses on application of Artificial Intelligence in drug discovery, repurposing, in advance, faster and accurate diagnosis of diseases. Last chapter throws a light on insights pertaining to ethical issues in AI research; and laws related to intellectual property rights on products/services borne owing to success (partly or purely and fully) derived by machines or devices through AI programs/algorithms. At the end of each chapter, questions have been added for the readers, mainly students.

Artificial Intelligence in Drug Discovery

Author: Ankit Gangwal

Publisher: Independently Published

ISBN:

Category:

Page: 358

View: 571

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......
Medical

Artificial Intelligence in Oncology Drug Discovery and Development

Author: John Cassidy

Publisher: BoD – Books on Demand

ISBN:

Category: Medical

Page: 192

View: 160

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.
Science

Deep Learning for the Life Sciences

Author: Bharath Ramsundar

Publisher: O'Reilly Media

ISBN:

Category: Science

Page: 238

View: 353

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Computers

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

Author: Stephanie K. Ashenden

Publisher: Academic Press

ISBN:

Category: Computers

Page: 264

View: 651

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

AI for Drug Development and Well-being

Author: Mark Chang

Publisher:

ISBN:

Category:

Page: 134

View: 466

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.
Technology & Engineering

In Silico Dreams

Author: Brian S. Hilbush

Publisher: John Wiley & Sons

ISBN:

Category: Technology & Engineering

Page: 352

View: 914

Learn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolution An outline of the fundamental breakthroughs in AI and deep learning and their applications across medicine A compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeutics A summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic development A guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government.
Computers

Artificial Intelligence Platform for Molecular Targeted Therapy

Author: Ariel Fernández

Publisher: World Scientific Publishing Company

ISBN:

Category: Computers

Page: 468

View: 214

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.
Business & Economics

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare

Author: Mark Chang

Publisher: CRC Press

ISBN:

Category: Business & Economics

Page: 352

View: 882

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.
Science

Artificial Intelligence Platform For Molecular Targeted Therapy: A Translational Science Approach

Author: Ariel Fernandez

Publisher: World Scientific

ISBN:

Category: Science

Page: 468

View: 657

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.

AI for Medical Science and Well-Being

Author: Mark Chang

Publisher:

ISBN:

Category:

Page: 133

View: 617

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.
Business & Economics

Artificial Intelligence in Precision Health

Author: Debmalya Barh

Publisher: Academic Press

ISBN:

Category: Business & Economics

Page: 544

View: 999

Artificial Intelligence in Precision Health: From Concept to Applications provides a readily available resource to understand artificial intelligence and its real time applications in precision medicine in practice. Written by experts from different countries and with diverse background, the content encompasses accessible knowledge easily understandable for non-specialists in computer sciences. The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available. This book is a valuable source for clinicians, healthcare workers, and researchers from diverse areas of biomedical field who may or may not have computational background and want to learn more about the innovative field of artificial intelligence for precision health. Provides computational approaches used in artificial intelligence easily understandable for non-computer specialists Gives know-how and real successful cases of artificial intelligence approaches in predictive models, modeling disease physiology, and public health surveillance Discusses the applicability of AI on multiple areas, such as drug discovery, clinical trials, radiology, surgery, patient care and clinical decision support
Science

Molecular Dynamics and Machine Learning in Drug Discovery

Author: Sergio Decherchi

Publisher: Frontiers Media SA

ISBN:

Category: Science

Page:

View: 955

Dr. Sergio Decherchi and Dr. Andrea Cavalli are co-founders of BiKi Technologies s.r.l. - a company that commercializes a Molecular Dynamics-based software suite for drug discovery. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
COMPUTERS

Machine Learning

Author: Roger Inge

Publisher: Nova Science Publishers

ISBN:

Category: COMPUTERS

Page: 124

View: 244

In chapter one, Lei Jia, PhD and Hua Gao, PhD analyze machine learning applications in small molecule and macromolecule drug discovery and development while comparing the similarities and differences between the two. They also examine their advantages and limitations with the intent to encourage further creative machine learning applications in drug discovery and development. During chapter two, Oscar Claveria, Enric Monte, and Salvador Torra present a study on the extrapolative performance of several machine learning models in a multiple-input multiple-output setting that permits cross-correlation between the inputs. Bojan Ploj, Germano Resconi, and Ali Yaghoubi parallel the solution of a system by logic gates and by a neural network, in which considerations are computed by the designated one step method during chapter three. In chapter four, Loris Nannia, Nicol� Zaffonatoa, Christian Salvatoreb, Isabella Castiglionib, and the Alzheimers Disease Neuroimaging Initiative propose a method that could aid in the early diagnosis of Alzheimers disease. Afterwards, F. Dornaika and I. Kamal Aldine present and experimentally assess two non-linear data self-representativeness coding schemes based on Hilbert space and column generation. Lastly, Christos Chrysoulas, Grigorios Kalliatakis, and Georgios Stamatiadis give an overview of Apache Hadoop, an open-source software framework used to distribute storage and process big data using the MapReduce programming model.
Science

Deep Learning for the Life Sciences

Author: Bharath Ramsundar

Publisher: "O'Reilly Media, Inc."

ISBN:

Category: Science

Page: 238

View: 176

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Computers

Advanced AI Techniques and Applications in Bioinformatics

Author: Loveleen Gaur

Publisher: CRC Press

ISBN:

Category: Computers

Page: 282

View: 801

The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers