Artificial Intelligence Platform For Molecular Targeted Therapy: A Translational Science Approach PDF Download
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Author: Ariel Fernandez
Publisher: World Scientific
ISBN: 9811232326
Category : Science
Languages : en
Pages : 469
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Book Description
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.
Author: Ariel Fernandez
Publisher: World Scientific
ISBN: 9811232326
Category : Science
Languages : en
Pages : 469
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Book Description
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.
Author: Ariel Fernández
Publisher: CRC Press
ISBN: 1000925293
Category : Computers
Languages : en
Pages : 173
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Book Description
As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
Author: Ariel Fernández
Publisher: CRC Press
ISBN: 1000806472
Category : Computers
Languages : en
Pages : 198
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Book Description
The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level. Dealing with artificial intelligence, this book delineates AI’s role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science. Key Features: Introduces new and advanced methods of model discovery for time series data using artificial intelligence Implements topological approaches to distill "machine-intuitive" models from complex dynamics data Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations Heralds a new era in data-driven science and engineering based on the operational concept of "computational intuition" Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects.
Author: Ariel Fernández
Publisher: Cambridge Scholars Publishing
ISBN: 152753118X
Category : Science
Languages : en
Pages : 203
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Book Description
This book explores the possibility of the use of artificial intelligence (AI) to solve one of the cosmos’ biggest mysteries: the nature of undetectable forms of matter, namely dark matter and dark energy, which make up 95% of the universe. The book describes the outcome of this quest in terms of an entangled ur-universe that admits no observer, and incorporates an extra dimension to encode space-time as a latent manifold. A cosmic engine fueled by dark energy that maintains the topology of the universe during its expansion, involving autocatalytic vacuum creation, is identified. The physical picture of the cosmos presented in the book paves the way for a solution to the cosmological constant problem and provides a cogent explanation for the huge gap between the predicted and measured values that has troubled physicists for decades.
Author: Ariel Fernández
Publisher: CRC Press
ISBN: 1040100910
Category : Science
Languages : en
Pages : 240
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Book Description
The dark universe contains matter and energy unidentifiable with current physical models, accounting for 95% of all the matter and energetic equivalent in the universe. The enormous surplus brings up daunting enigmas, such as the cosmological constant problem and the apparent distortions in the dynamics of deep space, and so coming to grips with the invisible universe has become a scientific imperative. This book addresses this need, reckoning that no cogent physical model of the dark universe can be implemented without first addressing the metaphysical hurdles along the way. The foremost problem is identifying the topology of the universe which, as argued in the book, is highly relevant to unveil the secrets of the dark universe. Artificial Intelligence (AI) is a valuable tool in this effort since it can reconcile conflicting data from deep space with the extant laws of physics by building models to decipher the dark universe. This book explores the applications of AI and how it can be used to embark on a metaphysical quest to identify the topology of the universe as a prerequisite to implement a physical model of the dark sector that enables a meaningful extrapolation into the visibile sector. The book is intended for a broad readership, but a background in college-level physics and computer science is essential. The book will be a valuable guide for graduate students as well as researchers in physics, astrophysics, and computer science focusing on AI applications to elucidate the nature of the dark universe. Key Features: · Provides readers with an intellectual toolbox to understand physical arguments on dark matter and energy. · Up to date with the latest cutting-edge research. · Authored by an expert on artificial intelligence and mathematical physics.
Author: Nathan Brown
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425
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Book Description
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.
Author: Ariel Fernández Stigliano
Publisher: Springer
ISBN: 3319168509
Category : Science
Languages : en
Pages : 380
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Book Description
The book focuses on the aqueous interface of biomolecules, a vital yet overlooked area of biophysical research. Most biological phenomena cannot be fully understood at the molecular level without considering interfacial behavior. The author presents conceptual advances in molecular biophysics that herald the advent of a new discipline, epistructural biology, centered on the interactions of water and bio molecular structures across the interface. The author introduces powerful theoretical and computational resources in order to address fundamental topics such as protein folding, the physico-chemical basis of enzyme catalysis and protein associations. On the basis of this information, a multi-disciplinary approach is used to engineer therapeutic drugs and to allow substantive advances in targeted molecular medicine. This book will be of interest to scientists, students and practitioners in the fields of chemistry, biophysics and biomedical engineering.
Author: Ariel Fernandez
Publisher: Springer Science & Business Media
ISBN: 3642117929
Category : Technology & Engineering
Languages : en
Pages : 235
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Book Description
In spite of the enticing promises of the post-genomic era, the pharmaceutical world is in a state of disarray. Drug discovery seems now riskier and more uncertain than ever. Thus, projects get routinely terminated in mid-stage clinical trials, new targets are getting harder to find, and successful therapeutic agents are often recalled as unanticipated side effects are discovered. Exploiting the huge output of genomic studies to make safer drugs has proven to be much more difficult than anticipated. More than ever, the lead in the pharmaceutical industry depends on the ability to harness innovative research, and this type of innovation can only come from one source: fundamental knowledge. This book squarely addresses this crucial problem since it introduces fundamental discoveries in basic biomolecular research that hold potential to broaden the technological base of the pharmaceutical industry. The book takes a fresh and fundamental look at the problem of how to design an effective drug with controlled specificity. Since the novel transformative concepts are unfamiliar to most practitioners, the first part of this book explains matters very carefully starting from a fairly elementary physico-chemical level. The second part of the book is devoted to practical applications, aiming at nothing less than a paradigm shift in drug design. This book is addressed to scientists working at the cutting edge of research in the pharmaceutical industry, but the material is at the same time accessible to senior undergraduates or graduate students interested in drug discovery and molecular design.
Author: Ankur Choudhary
Publisher: Springer Nature
ISBN: 9811630674
Category : Computers
Languages : en
Pages : 738
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Book Description
The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
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
