Artificial Intelligence in Precision Drug Design, Volume 2: Advanced Applications explores the transformative role of AI in modern drug discovery and development, presenting cutting-edge research and methodologies that integrate machine learning, network pharmacology, and computational modeling to accelerate therapeutic innovation. It covers a wide range of applications, including AI-driven drug repurposing, combination therapies, cancer immunotherapy, neuroscience, vaccine development, and molecular dynamics, but also delves into emerging technologies such as quantum computing, large language models, and graph neural networks. Designed for academics, this volume provides researchers, clinicians, and graduate students with a resource on the latest AI methodologies in precision medicine.
Users will find this to be a great resource that supports evidence-based innovation, fosters interdisciplinary collaboration, and equips them with insights to advance personalized healthcare solutions.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Contributors - p. xiii 8. About the editor - p. xvii 9. Preface - p. xix 10. Acknowledgments - p. xxi 11. About the book - p. xxiii 12. Chapter 1. AI-driven drug repurposing and combination therapies: Revolutionizing treatment strategies and overcoming complex challenges - p. 1 13. Chapter 2. AI-driven drug repurposing in precision medicine: Transforming existing therapies into targeted solutions - p. 29 14. Chapter 3. AI-driven network pharmacology for drug repurposing - p. 49 15. Chapter 4. AI-driven network-based drug repurposing: A roadmap for precision medicine in Kyrgyzstan's and low and middle income countries' resource-limited healthcare systems - p. 75 16. Chapter 5. AI in cancer drug development and immunotherapy - p. 93 17. Chapter 6. AI-driven innovations in neuroscience: Rewiring precision drug design for neurological disorders - p. 125 18. Chapter 7. Harnessing artificial intelligence for accelerated vaccine development: Innovations, applications, and case studies - p. 157 19. Chapter 8. AI-driven advancements in vaccine development: From discovery to global deployment - p. 173 20. Chapter 9. The role of artificial intelligence in overcoming challenges in vaccine development - p. 199 21. Chapter 10. Toward precision Alzheimer's treatment: AdGenius–CogMol–VAE for smart drug discovery - p. 217 22. Chapter 11. Patient stratification and precision medicine approaches—key to identifying patient subgroups for personalized treatment - p. 233 23. Chapter 12. Large language models for automated drug target discovery and literature mining - p. 263 24. Chapter 13. Predictive modeling of drug suitability using electronic health records and machine learning - p. 279 25. Chapter 14. AI-powered medical imaging for precision drug design: Applications in therapeutic targeting and optimization - p. 297 26. Chapter 15. Revolutionizing drug design: the convergence of AI and quantum computing - A systematic review - p. 317 27. Chapter 16. AI for antiviral and antibacterial drug discovery to combat AMR surge - p. 327 28. Chapter 17. AI for monitoring clinical outcomes and adverse events - p. 345 29. Chapter 18. Revolutionizing the power of AI's role in precision medicine: Transforming biologics, cell and gene therapy - p. 371 30. Chapter 19. Artificial Intelligence in fragment-based and scaffold-hopping approaches for drug discovery - p. 393 31. Chapter 20. AI-driven drug–drug interaction modeling with graph neural networks: A new era in safer drug development - p. 413 32. Chapter 21. Explainable AI for structure-based drug design: Concepts and case studies - p. 433 33. Index - p. 457
Artificial Intelligence in Precision Drug Design, Volume 2: Advanced Applications explores the transformative role of AI in modern drug discovery and development, presenting cutting-edge research and methodologies that integrate machine learning, network pharmacology, and computational modeling to accelerate therapeutic innovation. It covers a wide range of applications, including AI-driven drug repurposing, combination therapies, cancer immunotherapy, neuroscience, vaccine development, and molecular dynamics, but also delves into emerging technologies such as quantum computing, large language models, and graph neural networks. Designed for academics, this volume provides researchers, clinicians, and graduate students with a resource on the latest AI methodologies in precision medicine.
Users will find this to be a great resource that supports evidence-based innovation, fosters interdisciplinary collaboration, and equips them with insights to advance personalized healthcare solutions.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Contributors - p. xiii 8. About the editor - p. xvii 9. Preface - p. xix 10. Acknowledgments - p. xxi 11. About the book - p. xxiii 12. Chapter 1. AI-driven drug repurposing and combination therapies: Revolutionizing treatment strategies and overcoming complex challenges - p. 1 13. Chapter 2. AI-driven drug repurposing in precision medicine: Transforming existing therapies into targeted solutions - p. 29 14. Chapter 3. AI-driven network pharmacology for drug repurposing - p. 49 15. Chapter 4. AI-driven network-based drug repurposing: A roadmap for precision medicine in Kyrgyzstan's and low and middle income countries' resource-limited healthcare systems - p. 75 16. Chapter 5. AI in cancer drug development and immunotherapy - p. 93 17. Chapter 6. AI-driven innovations in neuroscience: Rewiring precision drug design for neurological disorders - p. 125 18. Chapter 7. Harnessing artificial intelligence for accelerated vaccine development: Innovations, applications, and case studies - p. 157 19. Chapter 8. AI-driven advancements in vaccine development: From discovery to global deployment - p. 173 20. Chapter 9. The role of artificial intelligence in overcoming challenges in vaccine development - p. 199 21. Chapter 10. Toward precision Alzheimer's treatment: AdGenius–CogMol–VAE for smart drug discovery - p. 217 22. Chapter 11. Patient stratification and precision medicine approaches—key to identifying patient subgroups for personalized treatment - p. 233 23. Chapter 12. Large language models for automated drug target discovery and literature mining - p. 263 24. Chapter 13. Predictive modeling of drug suitability using electronic health records and machine learning - p. 279 25. Chapter 14. AI-powered medical imaging for precision drug design: Applications in therapeutic targeting and optimization - p. 297 26. Chapter 15. Revolutionizing drug design: the convergence of AI and quantum computing - A systematic review - p. 317 27. Chapter 16. AI for antiviral and antibacterial drug discovery to combat AMR surge - p. 327 28. Chapter 17. AI for monitoring clinical outcomes and adverse events - p. 345 29. Chapter 18. Revolutionizing the power of AI's role in precision medicine: Transforming biologics, cell and gene therapy - p. 371 30. Chapter 19. Artificial Intelligence in fragment-based and scaffold-hopping approaches for drug discovery - p. 393 31. Chapter 20. AI-driven drug–drug interaction modeling with graph neural networks: A new era in safer drug development - p. 413 32. Chapter 21. Explainable AI for structure-based drug design: Concepts and case studies - p. 433 33. Index - p. 457
