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Using Artificial Intelligence in Chemistry and Biology

Using Artificial Intelligence in Chemistry and Biology
Author: Hugh Cartwright
Publisher: CRC Press
Total Pages: 358
Release: 2008-05-05
Genre: Science
ISBN: 0849384141

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Possessing great potential power for gathering and managing data in chemistry, biology, and other sciences, Artificial Intelligence (AI) methods are prompting increased exploration into the most effective areas for implementation. A comprehensive resource documenting the current state-of-the-science and future directions of the field is required to


Using Artificial Intelligence in Chemistry and Biology

Using Artificial Intelligence in Chemistry and Biology
Author: Hugh Cartwright
Publisher: CRC Press
Total Pages: 356
Release: 2019-08-30
Genre:
ISBN: 9780367387334

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Possessing great potential power for gathering and managing data in chemistry, biology, and other sciences, Artificial Intelligence (AI) methods are prompting increased exploration into the most effective areas for implementation. A comprehensive resource documenting the current state-of-the-science and future directions of the field is required to furnish the working experimental scientist and newcomer alike with the background necessary to utilize these methods. In response to the growing interest in the potential scientific applications of AI, Using Artificial Intelligence in Chemistry and Biology explains in a lucid, straightforward manner how these methods are used by scientists and what can be accomplished with them. Designed for those with no prior knowledge of AI, computer science, or programming, this book efficiently and quickly takes you to the point at which meaningful scientific applications can be investigated. The approach throughout is practical and direct, employing figures and illustrations to add clarity and humor to the topics at hand. Unique in scope, addressing the needs of scientists across a range of disciplines, this book provides both a broad overview and a detailed introduction to each of the techniques discussed. Chapters include an introduction to artificial intelligence, artificial neural networks, self-organizing maps, growing cell structures, evolutionary algorithms, cellular automata, expert systems, fuzzy logic, learning classifier systems, and evolvable developmental systems. The book also comes with a CD containing a complete version of the EJS software with which most of the calculations were accomplished. Encouraging a broader application of AI methods, this seminal work gives software designers a clearer picture of how scientists use AI and how to address those needs, and provides chemists, biologists, physicists, and others with the tools to increase the speed and efficiency of their work.


Machine Learning in Chemistry

Machine Learning in Chemistry
Author: Hugh M Cartwright
Publisher: Royal Society of Chemistry
Total Pages: 564
Release: 2020-07-15
Genre: Science
ISBN: 1839160241

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Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.


Machine Learning in Chemistry

Machine Learning in Chemistry
Author: Hugh M. Cartwright
Publisher: Royal Society of Chemistry
Total Pages: 564
Release: 2020-07-15
Genre: Science
ISBN: 1788017897

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Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.


Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery
Author: Nathan Brown
Publisher: Royal Society of Chemistry
Total Pages: 425
Release: 2020-11-04
Genre: Computers
ISBN: 1839160543

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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.


Machine Learning in Chemistry

Machine Learning in Chemistry
Author: Jon Paul Janet
Publisher: American Chemical Society
Total Pages: 189
Release: 2020-05-28
Genre: Science
ISBN: 0841299005

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Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important


Artificial Intelligence in Drug Design

Artificial Intelligence in Drug Design
Author: Alexander Heifetz
Publisher:
Total Pages: 529
Release: 2021
Genre: Artificial intelligence
ISBN: 9781071617878

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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.


Artificial Intelligence in Bioinformatics and Chemoinformatics

Artificial Intelligence in Bioinformatics and Chemoinformatics
Author: Yashwant Pathak
Publisher: CRC Press
Total Pages: 275
Release: 2023-10-11
Genre: Science
ISBN: 1000952754

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The authors aim to shed light on the practicality of using machine learning in finding complex chemoinformatics and bioinformatics applications as well as identifiying AI in biological and chemical data points. The chapters are designed in such a way that they highlight the important role of AI in chemistry and bioinformatics particularly for the classification of diseases, selection of features and compounds, dimensionality reduction and more. In addition, they assist in the organization and optimal use of data points generated from experiments performed using AI techniques. This volume discusses the development of automated tools and techniques to aid in research plans. Features Covers AI applications in bioinformatics and chemoinformatics Demystifies the involvement of AI in generating biological and chemical data Provides an Introduction to basic and advanced chemoinformatics computational tools Presents a chemical biology based toolset for artificial intelligence usage in drug design Discusses computational methods in cancer, genome mapping, and stem cell research


Artificial Intelligence in Chemical Engineering

Artificial Intelligence in Chemical Engineering
Author: Thomas E. Quantrille
Publisher: Elsevier
Total Pages: 634
Release: 2012-12-02
Genre: Technology & Engineering
ISBN: 0080571212

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Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. Allows the reader to learn AI quickly using inexpensive personal computers Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions Includes a computer diskette for an illustrated case study Demonstrates an expert system for separation synthesis (EXSEP) Presents a detailed review of published literature on expert systems and neural networks in chemical engineering


Artificial Neural Networks in Biological and Environmental Analysis

Artificial Neural Networks in Biological and Environmental Analysis
Author: Grady Hanrahan
Publisher: CRC Press
Total Pages: 206
Release: 2011-01-18
Genre: Mathematics
ISBN: 1439812594

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Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound