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.
1. Computational and Data-Driven Chemistry Using Artificial Intelligence - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. About the editor - p. Page vi 8. Preface - p. Page vii 9. List of Illustrations - p. Page viii 10. List of Tables - p. Page ix 11. Chapter 1 : Introductory chapter - p. Page ix 12. Chapter 2 : Goal-directed generation of new molecules by AI methods - p. Page 31 13. Chapter 3 : Salen-type metal complexes based on structural database of X-ray crystallography - p. Page 62 14. Chapter 4 : Approaches using AI in medicinal chemistry - p. Page 102 15. Chapter 5 : Application of machine learning algorithms for use in material chemistry - p. Page 146 16. Chapter 6 : Predicting conformers of flexible metal complexes using deep neural network - p. Page 189 17. Chapter 7 : Predicting activity and activation factor of catalytic reactions using machine learning - p. Page 214 18. Chapter 8 : Convolutional neural networks for the design and analysis of nonfullerene acceptors - p. Page 228 19. Index - p. Page 249
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.
1. Computational and Data-Driven Chemistry Using Artificial Intelligence - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. About the editor - p. Page vi 8. Preface - p. Page vii 9. List of Illustrations - p. Page viii 10. List of Tables - p. Page ix 11. Chapter 1 : Introductory chapter - p. Page ix 12. Chapter 2 : Goal-directed generation of new molecules by AI methods - p. Page 31 13. Chapter 3 : Salen-type metal complexes based on structural database of X-ray crystallography - p. Page 62 14. Chapter 4 : Approaches using AI in medicinal chemistry - p. Page 102 15. Chapter 5 : Application of machine learning algorithms for use in material chemistry - p. Page 146 16. Chapter 6 : Predicting conformers of flexible metal complexes using deep neural network - p. Page 189 17. Chapter 7 : Predicting activity and activation factor of catalytic reactions using machine learning - p. Page 214 18. Chapter 8 : Convolutional neural networks for the design and analysis of nonfullerene acceptors - p. Page 228 19. Index - p. Page 249
