Computational and Data-Driven Chemistry Using Artificial Intelligence: Fundamentals, Methods and Applications
Editat de Takashiro Akitsuen Limba Engleză Paperback – 11 oct 2021
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
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Specificații
ISBN-13: 9780128222492
ISBN-10: 0128222492
Pagini: 278
Ilustrații: Approx. 180 illustrations
Dimensiuni: 152 x 229 mm
Greutate: 0.38 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128222492
Pagini: 278
Ilustrații: Approx. 180 illustrations
Dimensiuni: 152 x 229 mm
Greutate: 0.38 kg
Editura: ELSEVIER SCIENCE
Cuprins
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