Computational and Data-Driven Chemistry Using Artificial Intelligence
Fundamentals, Methods and Applications
- 1st Edition - October 8, 2021
- Imprint: Elsevier
- Editor: Takashiro Akitsu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 2 4 9 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 2 7 2 - 9
Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developme… Read more
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Request a sales quoteComputational 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 graduates, post-graduates and researchers across both academia and industry interested in developing and/or applying artificial intelligence approaches to enhance their own work. AI researchers and data scientists interested in chemistry as a source of new problems for them to address through their work
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the editor
- Preface
- Chapter 1: Introductory chapter
- Abstract
- Acknowledgment
- 1: Chemistry using the “inductive” research style
- 2: Cyano-bridged copper complexes
- 3: Cyano-bridged 3d-4f complexes
- 4: Hybrid materials of copper complexes and photocatalysts
- 5: Hybrid materials of azobenzene and metal complexes
- 6: Hybrid materials of metal complexes and proteins
- 7: Conclusion
- Chapter 2: Goal-directed generation of new molecules by AI methods
- Abstract
- 1: Introduction
- 2: Molecular representation
- 3: Guiding and evaluating generative methods
- 4: Generative methods
- 5: Conclusion
- Chapter 3: Salen-type metal complexes based on structural database of X-ray crystallography
- Abstract
- Acknowledgment
- 1: Background
- 2: Docking calculation of protein and salen complexes
- 3: Systematic structure analysis of 3d-4f salen complexes
- 4: Failure research on photoelectric conversion dyes
- 5: Conclusion
- Chapter 4: Approaches using AI in medicinal chemistry
- Abstract
- 1: History of machine learning applications in medicinal chemistry
- 2: QSAR
- 3: De novo design
- 4: AI-assisted synthesis prediction
- 5: Closing the loop: Active learning and autonomous design
- Chapter 5: Application of machine learning algorithms for use in material chemistry
- Abstract
- 1: Introduction
- 2: ML algorithms
- 3: Connecting ML with HTE
- 4: Examples
- 5: Outlook
- Chapter 6: Predicting conformers of flexible metal complexes using deep neural network
- Abstract
- 1: Tremendous Rubik's snake versus polyhedral puzzle
- 2: Enumerating conformers of metal complexes
- 3: Machine learning and deep neural network
- 4: Tools for deep learning and related studies
- 5: Conformational analysis for [M(dmso)6]2 + complexes
- 6: Time saving by AI in conformational search for [Mg(dmso)6]2 +
- 7: Conformational prediction for [VO(dmso)5]2 + without using AI
- 8: Conformational prediction for [VO(dmso)5]2 + using DNN
- 9: Concluding remarks
- Chapter 7: Predicting activity and activation factor of catalytic reactions using machine learning
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Extraction of metal cluster catalytic activity factors using sparse modeling [11]
- 3: Activity prediction of heterogeneous catalyst using the surface adsorption calculation database [19]
- 4: Concluding remarks
- Chapter 8: Convolutional neural networks for the design and analysis of nonfullerene acceptors
- Abstract
- Acknowledgment
- 1: Background
- 2: Models
- 3: Dataset
- 4: Results
- 5: Conclusion
- Index
- Edition: 1
- Published: October 8, 2021
- No. of pages (Paperback): 278
- No. of pages (eBook): 278
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780128222492
- eBook ISBN: 9780128232729
TA
Takashiro Akitsu
Takashiro Akitsu is a full Professor of Chemistry at Tokyo University of Science. He completed his under graduate school training (chemistry) at Osaka University, Japan and his graduate school training (physical & inorganic chemistry, especially coordination, crystal and bioinorganic chemistry) at Osaka University (Ph.D. 2000). Following positions at Keio University, Japan, and Stanford University, USA, he moved to his current affiliation in 2008. He has published almost 220 articles in peer-reviewed journals and has presented multiple posters at international exhibitions. Prof Akitsu has been a peer reviewer of many journals and acted as an organizing committee of several international conferences.
Affiliations and expertise
Professor of Chemistry, Tokyo University of Science, JapanRead Computational and Data-Driven Chemistry Using Artificial Intelligence on ScienceDirect