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| chemistry and artificial intelligence: Machine Learning in Chemistry Hugh M. Cartwright, 2020-07-15 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. |
| chemistry and artificial intelligence: Machine Learning in Chemistry Jon Paul Janet, Heather J. Kulik, 2020-05-28 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 |
| chemistry and artificial intelligence: Artificial Intelligence in Chemistry Z. Hippe, 2013-10-22 This comprehensive overview of the application of artificial intelligence methods (AI) in chemistry contains an in-depth summary of the most interesting achievements of modern AI, namely, problem-solving in molecular structure elucidation and in syntheses design. The book provides a brief history of AI as a branch of computer science. It also gives an overview of the basic methods employed for searching the solution space (thoroughly exemplified by chemical problems), together with a profound and expert discussion on many questions that may be raised by modern chemists wishing to apply computer-assisted methods in their own research. Moreover, it includes a survey of the most important literature references, covering all essential research in automated interpretation of molecular spectra to elucidate a structure and in syntheses design. A glossary of basic terms from computer technology for chemists is appended. This book is intended to make the emerging field of artificial intelligence understandable and accessible for chemists, who are not trained in computer methods for solving chemical problems. The author discusses step-by-step basic algorithms for structure elucidation and many aspects of the automated design of organic syntheses in order to integrate this fascinating technology into current chemical knowledge. |
| chemistry and artificial intelligence: Artificial Intelligence in Drug Discovery Nathan Brown, 2020-11-04 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. |
| chemistry and artificial intelligence: Using Artificial Intelligence in Chemistry and Biology Hugh Cartwright, 2008-05-05 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 |
| chemistry and artificial intelligence: Artificial Intelligence in Chemistry José S. Torrecilla, John C. Cancilla, Jose Omar Valderrama, Charalampos Vasilios Proestos, 2020-07-17 |
| chemistry and artificial intelligence: Data Science in Chemistry Thorsten Gressling, 2020-11-23 The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing. |
| chemistry and artificial intelligence: Machine Learning in Chemistry Hugh M Cartwright, 2020-07-15 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. |
| chemistry and artificial intelligence: Applications of Artificial Intelligence in Chemistry Hugh M. Cartwright, 1993 It is clear that the techniques of artificial intelligence are useful for more than just the development of thinking machines; they constitute powerful problem-solving tools in their own right and expand the range of problems in science that can be tackled. AI methods can now be used on a routine basis by scientists in academic research as well as the commercial world, it is therefore vital that science students are exposed to, and understand these techniques. This is the first book topresent an introduction to AI methods for science undergraduates. The examples are drawn mainly from chemistry but the book is suited to a general scientific audience wanting to know more about how computers can help to understand and interpret science. |
| chemistry and artificial intelligence: Computational and Data-Driven Chemistry Using Artificial Intelligence Takashiro Akitsu, 2021-10-08 Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. - Provides an accessible introduction to the current state and future possibilities for AI in chemistry - Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI - Highlights the interdisciplinary and broad applicability of AI tools across a wide range of chemistry fields |
| chemistry and artificial intelligence: Machine Learning in Chemistry Edward O. Pyzer-Knapp, Teodoro Laino, 2020-10-22 Atomic-scale representation and statistical learning of tensorial properties -- Prediction of Mohs hardness with machine learning methods using compositional features -- High-dimensional neural network potentials for atomistic simulations -- Data-driven learning systems for chemical reaction prediction: an analysis of recent approaches -- Using machine learning to inform decisions in drug discovery : an industry perspective -- Cognitive materials discovery and onset of the 5th discovery paradigm. |
| chemistry and artificial intelligence: Applications of Artificial Intelligence in Chemistry Hugh M. Cartwright, 1993 Artificial intelligence is not just about making machines think; it is also a powerful problem-solving tool. Many scientific problems can be solved only with difficulty using conventional methods, yet these same problems may be ideally suited to attack using artificial intelligence. This book, for college-level students in any science discipline, introduces these intriguing and powerful techniques, and discusses their growing impact upon science. |
| chemistry and artificial intelligence: Knowledge-based Expert Systems in Chemistry Philip Judson, 2019-02-07 There have been significant developments in the use of knowledge-based expert systems in chemistry since the first edition of this book was published in 2009. This new edition has been thoroughly revised and updated to reflect the advances. The underlying theme of the book is still the need for computer systems that work with uncertain or qualitative data to support decision-making based on reasoned judgements. With the continuing evolution of regulations for the assessment of chemical hazards, and changes in thinking about how scientific decisions should be made, that need is ever greater. Knowledge-based expert systems are well established in chemistry, especially in relation to toxicology, and they are used routinely to support regulatory submissions. The effectiveness and continued acceptance of computer prediction depends on our ability to assess the trustworthiness of predictions and the validity of the models on which they are based. Written by a pioneer in the field, this book provides an essential reference for anyone interested in the uses of artificial intelligence for decision making in chemistry. |
| chemistry and artificial intelligence: Applications of Artificial Intelligence in Process Systems Engineering Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong, 2021-06-05 Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering |
| chemistry and artificial intelligence: Applications of Artificial Intelligence for Organic Chemistry Robert K. Lindsay, 1980 |
| chemistry and artificial intelligence: Artificial Intelligence in Drug Discovery Nathan Brown, 2020-11-12 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. |
| chemistry and artificial intelligence: Artificial Intelligence Applications in Chemistry Thomas H. Pierce, Bruce A. Hohne, 1986 |
| chemistry and artificial intelligence: Machine Learning in Biotechnology and Life Sciences Saleh Alkhalifa, 2022-01-28 Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key FeaturesLearn the applications of machine learning in biotechnology and life science sectorsDiscover exciting real-world applications of deep learning and natural language processingUnderstand the general process of deploying models to cloud platforms such as AWS and GCPBook Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP. What you will learnGet started with Python programming and Structured Query Language (SQL)Develop a machine learning predictive model from scratch using PythonFine-tune deep learning models to optimize their performance for various tasksFind out how to deploy, evaluate, and monitor a model in the cloudUnderstand how to apply advanced techniques to real-world dataDiscover how to use key deep learning methods such as LSTMs and transformersWho this book is for This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book. |
| chemistry and artificial intelligence: Artificial Intelligence in Chemical Engineering Thomas E. Quantrille, Y. A. Liu, 2012-12-02 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 |
| chemistry and artificial intelligence: Machine Learning and Artificial Intelligence in Chemical and Biological Sensing Jeong-Yeol Yoon, Chenxu Yu, 2024-07-07 Machine learning (ML) has recently become popular in chemical and biological sensing applications. ML is a subset of artificial intelligence (AI) and other AI techniques have been used in various chemical and biological sensing. Machine Learning and Artificial Intelligence in Chemical and Biological Sensing covers the theoretical background and practical applications of various ML/AI methods toward chemical and biological sensing. No comprehensive reference text has been available previously to cover the wide breadth of this topic. The Editors have written the first three chapters to firmly introduce the reader to fundamental ML theories that can be used for chemical/biosensing. The subsequent chapters then cover the practical applications with contributions by various experts in the field. They show how ML and AI-based techniques can provide solutions for: 1) identifying and quantifying target molecules when specific receptors are unavailable 2) analyzing complex mixtures of target molecules, such as gut microbiome and soil microbiome 3) analyzing high-throughput and high-dimensional data, such as drug screening, molecular interaction, and environmental toxicant analysis, 4) analyzing complex data sets where fingerprinting approach is needed This book is written primarily for upper undergraduate students, graduate students, research staff, and faculty members at teaching and research universities and colleges who are working on chemical sensing, biosensing, analytical chemistry, analytical biochemistry, biomedical imaging, medical diagnostics, environmental monitoring, and agricultural applications. - Presents the first comprehensive reference text on the use of ML and AI for chemical and biological sensing - Provides a firm grounding in the fundamental theories on ML and AI before covering the practical applications with contributions by various experts in the field - Includes a wide array of practical applications covered, including: E-nose, Raman, SERS, lens-free imaging, multi/hyperspectral imaging, NIR/optical imaging, receptor-free biosensing, paper microfluidics, single molecule analysis in biomedicine, in situ protein characterization, microbial population dynamics, and all-in-one sensor systems |
| chemistry and artificial intelligence: Quantum Chemistry in the Age of Machine Learning Pavlo O. Dral, 2022-09-16 Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry |
| chemistry and artificial intelligence: Common Sense, the Turing Test, and the Quest for Real AI Hector J. Levesque, 2017 What kind of AI? -- The big puzzle -- Knowledge and behavior -- Making it and faking it -- Learning with and without experience -- Book smarts and street smarts -- The long tail and the limits to training -- Symbols and symbol processing -- Knowledge-based systems -- AI technology |
| chemistry and artificial intelligence: Materials Informatics Olexandr Isayev, Alexander Tropsha, Stefano Curtarolo, 2019-12-04 Provides everything readers need to know for applying the power of informatics to materials science There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials. Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others. -Bridges the gap between materials science and informatics -Covers all the known methodologies and applications of materials informatics -Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials -Examines the state-of-the-art software and tools being used today Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics. |
| chemistry and artificial intelligence: Artificial Intelligence Applications in Chemistry Thomas H. Pierce, Bruce A. Hohne, 1986 |
| chemistry and artificial intelligence: Knowledge-based Expert Systems in Chemistry Philip Judson, 2019-02-07 This new edition has been thoroughly revised and updated to reflect the advances in using knowledge-based expert systems for chemistry. |
| chemistry and artificial intelligence: Chemoinformatics Thomas Engel, Johann Gasteiger, 2018-12-10 Von den Grundlagen zu Methoden - dieses Fachbuch, übersichtlich und didaktisch klar gegliedert, ist eine maßgebliche Handreichung mit allem Wissenswerten und Erläuterungen der Tools in diesem Fachgebiet. |
| chemistry and artificial intelligence: Artificial Neural Networks in Biological and Environmental Analysis Grady Hanrahan, 2011-01-18 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 |
| chemistry and artificial intelligence: Intelligent Nanotechnology Yuebing Zheng, Zilong Wu, 2022-10-26 Intelligent Nanotechnology: Merging Nanoscience and Artificial Intelligence provides an overview of advances in science and technology made possible by the convergence of nanotechnology and artificial intelligence (AI). Sections focus on AI-enhanced design, characterization and manufacturing and the use of AI to improve important material properties, with an emphasis on mechanical, photonic, electronic and magnetic properties. Designing benign nanomaterials through the prediction of their impact on biology and the environment is also discussed. Other sections cover the use of AI in the acquisition and analysis of data in experiments and AI technologies that have been enhanced through nanotechnology platforms. Final sections review advances in applications enabled by the merging of nanotechnology and artificial intelligence, including examples from biomedicine, chemistry and automated research. - Includes recent advances on AI-enhanced design, characterization and the manufacturing of nanomaterials - Reviews AI technologies that have been enabled by nanotechnology - Discusses potentially world-changing applications that could ensue as a result of merging these two fields |
| chemistry and artificial intelligence: The Beauty of Chemistry Philip Ball, 2021-05-11 Images and text capture the astonishing beauty of the chemical processes that create snowflakes, bubbles, flames, and other wonders of nature. Chemistry is not just about microscopic atoms doing inscrutable things; it is the process that makes flowers and galaxies. We rely on it for bread-baking, vegetable-growing, and producing the materials of daily life. In stunning images and illuminating text, this book captures chemistry as it unfolds. Using such techniques as microphotography, time-lapse photography, and infrared thermal imaging, The Beauty of Chemistry shows us how chemistry underpins the formation of snowflakes, the science of champagne, the colors of flowers, and other wonders of nature and technology. We see the marvelous configurations of chemical gardens; the amazing transformations of evaporation, distillation, and precipitation; heat made visible; and more. |
| chemistry and artificial intelligence: The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry Stephanie K. Ashenden, 2021-04-23 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 |
| chemistry and artificial intelligence: De novo Molecular Design Gisbert Schneider, 2013-10-10 Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more. |
| chemistry and artificial intelligence: Artificial Intelligence in Drug Design Alexander Heifetz, 2022-11-05 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. |
| chemistry and artificial intelligence: Applications of Artificial Intelligence and Machine Learning Ankur Choudhary, Arun Prakash Agrawal, Rajasvaran Logeswaran, Bhuvan Unhelkar, 2021-07-27 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. |
| chemistry and artificial intelligence: Chemistry and Technology of Carbodiimides Henri Ulrich, 2008-03-11 Carbodiimides play an important role as condensation agents in the synthesis of polypeptides, polynucleotides, polysaccharides and numerous other chemical transformations. Chemistry and Technology of Carbodiimides is the first book to examine both the chemistry and technology of carbodiimides. This book provides a comprehensive and in-depth coverage of the synthesis and reactions of this industrially important class of chemicals while focusing on industrial applications, including the $M-sectors of biochemical synthesis, pharmaceuticals, polymers, ceramics, and herbicides. Written by a well-known authority in the field this book will prove a valuable reference tool for anyone working in this area of chemistry. |
| chemistry and artificial intelligence: A Handbook of Artificial Intelligence in Drug Delivery Anil K. Philip, Aliasgar Shahiwala, Mamoon Rashid, Md Faiyazuddin, 2023-03-27 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 |
| chemistry and artificial intelligence: Expert Systems in Chemistry Research Markus C. Hemmer, 2007-12-13 Expert systems allow scientists to access, manage, and apply data and specialized knowledge from various disciplines to their own research. Expert Systems in Chemistry Research explains the general scientific basis and computational principles behind expert systems and demonstrates how they can improve the efficiency of scientific workflows |
| chemistry and artificial intelligence: Intelligent Software for Chemical Analysis L.M.C. Buydens, P.J. Schoenmakers, 1993-09-03 Various emerging techniques for automating intelligent functions in the laboratory are described in this book. Explanations on how systems work are given and possible application areas are suggested. The main part of the book is devoted to providing data which will enable the reader to develop and test his own systems. The emphasis is on expert systems; however, promising developments such as self-adaptive systems, neural networks and genetic algorithms are also described. The book has been written by chemists with a great deal of practical experience in developing and testing intelligent software, and therefore offers first-hand knowledge. Laboratory staff and managers confronted with commercial intelligent software will find information on the functioning, possibilities and limitations thereof, enabling them to select and use modern software in an optimum fashion. Finally, computer scientists and information scientists will find a wealth of data on the application of contemporary artificial intelligence techniques. |
| chemistry and artificial intelligence: Applied Chemoinformatics Thomas Engel, Johann Gasteiger, 2018-06-05 Edited by world-famous pioneers in chemoinformatics, this is a clearly structured and applications-oriented approach to the topic, providing up-to-date and focused information on the wide range of applications in this exciting field. The authors explain methods and software tools, such that the reader will not only learn the basics but also how to use the different software packages available. Experts describe applications in such different fields as structure-spectra correlations, virtual screening, prediction of active sites, library design, the prediction of the properties of chemicals, the development of new cosmetics products, quality control in food, the design of new materials with improved properties, toxicity modeling, assessment of the risk of chemicals, and the control of chemical processes. The book is aimed at advanced students as well as lectures but also at scientists that want to learn how chemoinformatics could assist them in solving their daily scientific tasks. Together with the corresponding textbook Chemoinformatics - Basic Concepts and Methods (ISBN 9783527331093) on the fundamentals of chemoinformatics readers will have a comprehensive overview of the field. |
| chemistry and artificial intelligence: Using Artificial Intelligence in Chemistry and Biology Hugh Cartwright, Physical and Theoretical Chemistry Laboratory Hugh Cartwright, 2019-08-30 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. |
| chemistry and artificial intelligence: Artificial Intelligence for Materials Science Yuan Cheng, Tian Wang, Gang Zhang, 2021-03-26 Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers. |
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Jun 2, 2021 · Hundreds of presentations from the best and brightest minds that chemistry has to offer are available to you on-demand. The Library is divided into 6 different sections to help …
Synthetic Organic Chemistry Driven by Artificial Intelligence
In this Review, we discuss the recent impact of AI on different tasks of synthetic chemistry and dissect selected examples from the literature.
Artificial-IntelligenceDriven Precision Chemistry - ACS …
Nov 5, 2024 · “Artificial-Intelligence Driven Precision Chemistry” features the latest research and perspectives from leading scientists in this rapidly developing field of precision and intelligent …
Generative artificial intelligence for computational chemistry: a ...
Sep 6, 2024 · AI for Chemistry explores common themes and characteristics of Generative AI tools that are particularly desirable for chemistry applications. Of particular note here is …
Synthetic organic chemistry driven by artificial intelligence
Aug 21, 2019 · Specifically, we discuss AI for the automation of retrosynthetic analyses, prediction of reaction outcomes, optimization of reaction condi-tions and identification of new chemistry.
Application of Artificial Intelligence (AI) in Predicting …
Abstract: Artificial Intelligence (AI) has emerged as a transformative tool in the field of chemistry, offering unprecedented capabilities in predicting reaction mechanisms and reaction rates.
Applications of artificial intelligence in chemistry
•What is meant by the applications of AI in Chemistry? •Application of AI in Chemistry refers to the use of artificial intelligence techniques and tools to solve complex problems, enhance …
Artificial Intelligence in Chemistry Research Implications for …
Aug 19, 2024 · ABSTRACT: Artificial intelligence (AI) has become an important tool in modern scientific research, particularly in chemistry and related disciplines. Despite its growing …
Artificial Intelligence in Chemistry: Current Trends and Future ...
Jun 2, 2021 · Hundreds of presentations from the best and brightest minds that chemistry has to offer are available to you on-demand. The Library is divided into 6 different sections to help …
Artificial Intelligence and Machine Learning in Green …
Artificial Intelligence (AI) and Machine Learning (ML) have greatly improved green chemistry by making it easier to predict outcomes, improve reaction processes, and speed up the search for …
Chemistry in the Era of Artificial Intelligence - ACS Publications
In general, the power of AI is best represented by its ability to tackle high-dimensional problems, which fits very well with the demand of chemistry. The complexity of chemical structures and …
The Dawn of Generative Artificial Intelligence in Chemistry …
Aug 13, 2024 · As dawn breaks on generative AI, it heralds a new era in chemistry education, pushing the traditional boundaries and opening up new pathways for pedagogical innovation. …
Artificial Intelligence in Chemistry: Current Trends and …
In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent …
Chemistry in Times of Artificial Intelligence
Aug 19, 2020 · Chemo-informatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agri-chemical research, food science, regulatory …
Top 20 influential AI-based technologies in chemistry
Apr 14, 2024 · In this article, the top 20 highly influential AI technologies that have already transformed fundamental research and the industrial sector in chemistry or are expected to …
AI in Chemistry and Patent Law: Current and Future Issues
What can AI do in chemistry? Insilico Medicine: Developed 18 preclinical candidates since 2021 (in under 3 years). 5/25/2023: Insilico Medicine announced FDA investigational new drug (IND) …
Artificial intelligence is empowering chemistry research
Here we organize a special topic on “AI for Chemistry”, which includes seven high-quality papers covering the latest research and reviews of AI-enabled structure-property prediction models …
Bridging chemistry and artificial intelligence by a reaction ...
We foresee that ReactSeq can serve as a bridge to narrow the gap between chemistry and artificial intelligence. Artificial intelligence technologies, represented by large language models...
Top 20 Influential AI-Based Technologies in Chemistry
In this article, the top 20 highly influential AI technologies that have already transformed fundamental research and the industrial sector in chemistry or are expected to have profound …
The Role of Artificial Intelligence (AI) in Organic Chemistry
We aim to achieve this review paper by explaining AI's role in chemistry specifically in drug design and molec-ular modeling.
Artificial Intelligence in Chemistry: Current Trends and …
Jun 2, 2021 · Hundreds of presentations from the best and brightest minds that chemistry has to offer are available to you on-demand. The Library is divided into 6 different sections to help you …
Synthetic Organic Chemistry Driven by Artificial Intelligence
In this Review, we discuss the recent impact of AI on different tasks of synthetic chemistry and dissect selected examples from the literature.
Artificial-IntelligenceDriven Precision Chemistry - ACS …
Nov 5, 2024 · “Artificial-Intelligence Driven Precision Chemistry” features the latest research and perspectives from leading scientists in this rapidly developing field of precision and intelligent …
Generative artificial intelligence for computational …
Sep 6, 2024 · AI for Chemistry explores common themes and characteristics of Generative AI tools that are particularly desirable for chemistry applications. Of particular note here is …
Synthetic organic chemistry driven by artificial intelligence
Aug 21, 2019 · Specifically, we discuss AI for the automation of retrosynthetic analyses, prediction of reaction outcomes, optimization of reaction condi-tions and identification of new chemistry.
Application of Artificial Intelligence (AI) in Predicting …
Abstract: Artificial Intelligence (AI) has emerged as a transformative tool in the field of chemistry, offering unprecedented capabilities in predicting reaction mechanisms and reaction rates.
Applications of artificial intelligence in chemistry
•What is meant by the applications of AI in Chemistry? •Application of AI in Chemistry refers to the use of artificial intelligence techniques and tools to solve complex problems, enhance research, …
Artificial Intelligence in Chemistry Research Implications for …
Aug 19, 2024 · ABSTRACT: Artificial intelligence (AI) has become an important tool in modern scientific research, particularly in chemistry and related disciplines. Despite its growing …
Artificial Intelligence in Chemistry: Current Trends and …
Jun 2, 2021 · Hundreds of presentations from the best and brightest minds that chemistry has to offer are available to you on-demand. The Library is divided into 6 different sections to help you …
Artificial Intelligence and Machine Learning in Green …
Artificial Intelligence (AI) and Machine Learning (ML) have greatly improved green chemistry by making it easier to predict outcomes, improve reaction processes, and speed up the search for …
Chemistry in the Era of Artificial Intelligence - ACS …
In general, the power of AI is best represented by its ability to tackle high-dimensional problems, which fits very well with the demand of chemistry. The complexity of chemical structures and …
The Dawn of Generative Artificial Intelligence in Chemistry …
Aug 13, 2024 · As dawn breaks on generative AI, it heralds a new era in chemistry education, pushing the traditional boundaries and opening up new pathways for pedagogical innovation. …
