Revolutionizing Drug Development: Harnessing AI and Computational Biology presents cutting-edge artificial intelligence (AI) tools, such as machine- and deep-learning models and generative AI, to assist structure-based drug design and clinical trial design and integrate with drug development programs. This book summarizes technical advancements of AI-based technologies and computational biology approaches, highlighting their applications in developing new drugs through discovery, repurposing, and designing, for advancing R&D in the pharmaceutical industry and benefiting precision medicine. This book serves as an ideal reference for students, teachers, professors, and researchers in biological and biomedical sciences, particularly the topics related to bioinformatics, systems biology, pharmacology, and drug development. The readers can efficiently and precisely overview this burning field, which might inspire their future directions of research in drug development and AI-based digital biology.
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. List of contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1. Artificial intelligence and accelerated computing in drug discovery: an updated overview - p. 1 9. Chapter 2. AI strategies for drug discovery and bioactivity prediction: opportunities and challenges - p. 13 10. Chapter 3. AI technologies for drug repurposing: methods and applications - p. 25 11. Chapter 4. Data science and databases in drug discovery: technical development and applications - p. 47 12. Chapter 5. Graph neural networks for drug discovery: protocols and applications - p. 61 13. Chapter 6. Deep learning and generative models for drug discovery: techniques and current achievements - p. 81 14. Chapter 7. Artificial intelligence-enabled personalized medicine: strategies and challenges - p. 99 15. Chapter 8. Artificial intelligence-based technologies for advancing research and development in the pharmaceutical industry: current developments and challenges - p. 117 16. Chapter 9. Artificial intelligence powered drug response prediction against cancer - p. 133 17. Chapter 10. Artificial intelligence in drug discovery for neurological disorders: progress, challenges, and future directions - p. 143 18. Chapter 11. AI applications in drug discovery for promoting longevity - p. 161 19. Chapter 12. Transforming drug development with AI-driven models - p. 187 20. Chapter 13. AI technologies for precision and personalized medicine - p. 199 21. Chapter 14. AI technologies for smart pharmacology - p. 211 22. Chapter 15. AI-powered immunopharmacological strategies to combat inflammatory diseases - p. 229 23. Chapter 16. AI and nanotechnology for advancing drug development - p. 243 24. Chapter 17. Integration of artificial intelligence and machine learning technologies in antiviral drug discovery: strategies and challenges - p. 261 25. Chapter 18. Artificial intelligence in the discovery of new antibiotics - p. 279 26. Chapter 19. AI-designed drugs: clinical development and future directions - p. 293 27. Index - p. 327
Revolutionizing Drug Development: Harnessing AI and Computational Biology presents cutting-edge artificial intelligence (AI) tools, such as machine- and deep-learning models and generative AI, to assist structure-based drug design and clinical trial design and integrate with drug development programs. This book summarizes technical advancements of AI-based technologies and computational biology approaches, highlighting their applications in developing new drugs through discovery, repurposing, and designing, for advancing R&D in the pharmaceutical industry and benefiting precision medicine. This book serves as an ideal reference for students, teachers, professors, and researchers in biological and biomedical sciences, particularly the topics related to bioinformatics, systems biology, pharmacology, and drug development. The readers can efficiently and precisely overview this burning field, which might inspire their future directions of research in drug development and AI-based digital biology.
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. List of contributors - p. xiii 7. Preface - p. xvii 8. Chapter 1. Artificial intelligence and accelerated computing in drug discovery: an updated overview - p. 1 9. Chapter 2. AI strategies for drug discovery and bioactivity prediction: opportunities and challenges - p. 13 10. Chapter 3. AI technologies for drug repurposing: methods and applications - p. 25 11. Chapter 4. Data science and databases in drug discovery: technical development and applications - p. 47 12. Chapter 5. Graph neural networks for drug discovery: protocols and applications - p. 61 13. Chapter 6. Deep learning and generative models for drug discovery: techniques and current achievements - p. 81 14. Chapter 7. Artificial intelligence-enabled personalized medicine: strategies and challenges - p. 99 15. Chapter 8. Artificial intelligence-based technologies for advancing research and development in the pharmaceutical industry: current developments and challenges - p. 117 16. Chapter 9. Artificial intelligence powered drug response prediction against cancer - p. 133 17. Chapter 10. Artificial intelligence in drug discovery for neurological disorders: progress, challenges, and future directions - p. 143 18. Chapter 11. AI applications in drug discovery for promoting longevity - p. 161 19. Chapter 12. Transforming drug development with AI-driven models - p. 187 20. Chapter 13. AI technologies for precision and personalized medicine - p. 199 21. Chapter 14. AI technologies for smart pharmacology - p. 211 22. Chapter 15. AI-powered immunopharmacological strategies to combat inflammatory diseases - p. 229 23. Chapter 16. AI and nanotechnology for advancing drug development - p. 243 24. Chapter 17. Integration of artificial intelligence and machine learning technologies in antiviral drug discovery: strategies and challenges - p. 261 25. Chapter 18. Artificial intelligence in the discovery of new antibiotics - p. 279 26. Chapter 19. AI-designed drugs: clinical development and future directions - p. 293 27. Index - p. 327
