Computational and Data-Driven Chemistry Using Artificial Intelligence
Elsevier Science Publishing Co Inc (Verlag)
978-0-12-822249-2 (ISBN)
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.
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.
1. Introduction to Computational and Data-Driven Chemistry Using AI 2. Goal-directed generation of new molecules by AI methods 3. Compounds based on structural database of X-ray crystallography 4. Approaches using AI in Medicinal Chemistry 5. Application of Machine learning algorithms for use in material chemistry 6. Predicting Conformers of Flexible Metal Complexes using Deep Neural Network 7. Predicting Activity and Activation Factor of Catalytic Reactions Using Machine Learning 8. Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors
Erscheinungsdatum | 15.10.2021 |
---|---|
Zusatzinfo | Approx. 180 illustrations; Illustrations |
Sprache | englisch |
Maße | 152 x 229 mm |
Gewicht | 500 g |
Themenwelt | Naturwissenschaften ► Chemie ► Physikalische Chemie |
ISBN-10 | 0-12-822249-2 / 0128222492 |
ISBN-13 | 978-0-12-822249-2 / 9780128222492 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich