Artificial Intelligence in Precision Drug Design: Advanced Applications showcases how artificial intelligence (AI) is revolutionizing modern drug discovery and development. Building upon the foundational principles established in Volume 1, this book dives into real-world applications where AI accelerates innovation, enhances predictive accuracy, and enables breakthrough therapeutics. Featuring contributions from leading global researchers and practitioners, the book explores machine learning, deep learning, and network-based approaches applied to complex biomedical challenges. Key areas include AI driven drug repurposing, combination therapies, immunotherapy, vaccine design, quantum computing, and the integration of large language models in drug discovery. Additional chapters highlight predictive modeling using electronic health records, AI-powered medical imaging, and explainable AI for structure-based drug design. What sets this volume apart is its emphasis on practical impact, demonstrating how data, computation, and interdisciplinary collaboration converge to advance precision medicine. Designed for scientists, clinicians, educators, and students, it serves as both a comprehensive reference and a source of inspiration for leveraging AI to transform healthcare.
• Focuses on real-world AI applications in drug design and development provides comprehensive coverage of advanced techniques: deep learning, network-based models, and quantum computing.
• Includes case studies on drug repurposing, combination therapies, immunotherapy, and vaccine development.
• Provides insights into predictive modeling, AI-driven medical imaging, and explainable AI.
• Highlights practical impact and interdisciplinary collaboration in precision medicine.
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 in drug design: A historical and future perspective - p. 1 13. Chapter 2. Can machines truly know? Epistemological challenges in AI-driven drug discovery - p. 15 14. Chapter 3. Ethical implications of AI in precision drug design: A philosophical inquiry - p. 33 15. Chapter 4. Metaphors of medicine: a literary perspective of the involvement of AI in drug discovery, design, and target precision - p. 47 16. Chapter 5. Artificial intelligence in molecular screening: Advances, challenges, and future perspectives - p. 61 17. Chapter 6. AI for predicting pharmacokinetics and pharmacodynamics - p. 75 18. Chapter 7. Artificial Intelligence for predicting drug-likeness and bioavailability - p. 101 19. Chapter 8. AI-powered In silico ADMET modeling and optimization in drug design - p. 121 20. Chapter 9. Artificial Intelligence based toxicity prediction: Advancing drug safety and risk assessment - p. 149 21. Chapter 10. Leveraging AI for integrating genomics, transcriptomics, and proteomics - p. 167 22. Chapter 11. Artificial intelligence in multi-omics integration for precision drug design - p. 199 23. Chapter 12. AI and machine learning for disease pathway modeling [case study: malaria infection] - p. 213 24. Chapter 13. AI-powered genomic medicine: Technologies and challenges - p. 223 25. Chapter 14. PGP-miner: An AI and machine learning tool in cancer drug development and immunotherapy - p. 249 26. Chapter 15. Artificial intelligence for drug repurposing: Opportunities and challenges - p. 263 27. Chapter 16. Generative artificial intelligence for de novo drug design - p. 297 28. Chapter 17. Bias and transparency in AI and machine learning models for drug design - p. 325 29. Chapter 18. Blockchain and AI in drug development: Securing data integrity and transparency - p. 347 30. Chapter 19. Counterfactual explainability in AI-driven drug discovery: Enhancing transparency and decision-making - p. 357 31. Chapter 20. Integrating AI in pharmacovigilance and clinical trial monitoring: Enhancing drug safety and efficacy in Kyrgyzstan’s and LMIC’s evolving healthcare landscape - p. 381 32. Index - p. 395
Artificial Intelligence in Precision Drug Design: Advanced Applications showcases how artificial intelligence (AI) is revolutionizing modern drug discovery and development. Building upon the foundational principles established in Volume 1, this book dives into real-world applications where AI accelerates innovation, enhances predictive accuracy, and enables breakthrough therapeutics. Featuring contributions from leading global researchers and practitioners, the book explores machine learning, deep learning, and network-based approaches applied to complex biomedical challenges. Key areas include AI driven drug repurposing, combination therapies, immunotherapy, vaccine design, quantum computing, and the integration of large language models in drug discovery. Additional chapters highlight predictive modeling using electronic health records, AI-powered medical imaging, and explainable AI for structure-based drug design. What sets this volume apart is its emphasis on practical impact, demonstrating how data, computation, and interdisciplinary collaboration converge to advance precision medicine. Designed for scientists, clinicians, educators, and students, it serves as both a comprehensive reference and a source of inspiration for leveraging AI to transform healthcare.
• Focuses on real-world AI applications in drug design and development provides comprehensive coverage of advanced techniques: deep learning, network-based models, and quantum computing.
• Includes case studies on drug repurposing, combination therapies, immunotherapy, and vaccine development.
• Provides insights into predictive modeling, AI-driven medical imaging, and explainable AI.
• Highlights practical impact and interdisciplinary collaboration in precision medicine.
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 in drug design: A historical and future perspective - p. 1 13. Chapter 2. Can machines truly know? Epistemological challenges in AI-driven drug discovery - p. 15 14. Chapter 3. Ethical implications of AI in precision drug design: A philosophical inquiry - p. 33 15. Chapter 4. Metaphors of medicine: a literary perspective of the involvement of AI in drug discovery, design, and target precision - p. 47 16. Chapter 5. Artificial intelligence in molecular screening: Advances, challenges, and future perspectives - p. 61 17. Chapter 6. AI for predicting pharmacokinetics and pharmacodynamics - p. 75 18. Chapter 7. Artificial Intelligence for predicting drug-likeness and bioavailability - p. 101 19. Chapter 8. AI-powered In silico ADMET modeling and optimization in drug design - p. 121 20. Chapter 9. Artificial Intelligence based toxicity prediction: Advancing drug safety and risk assessment - p. 149 21. Chapter 10. Leveraging AI for integrating genomics, transcriptomics, and proteomics - p. 167 22. Chapter 11. Artificial intelligence in multi-omics integration for precision drug design - p. 199 23. Chapter 12. AI and machine learning for disease pathway modeling [case study: malaria infection] - p. 213 24. Chapter 13. AI-powered genomic medicine: Technologies and challenges - p. 223 25. Chapter 14. PGP-miner: An AI and machine learning tool in cancer drug development and immunotherapy - p. 249 26. Chapter 15. Artificial intelligence for drug repurposing: Opportunities and challenges - p. 263 27. Chapter 16. Generative artificial intelligence for de novo drug design - p. 297 28. Chapter 17. Bias and transparency in AI and machine learning models for drug design - p. 325 29. Chapter 18. Blockchain and AI in drug development: Securing data integrity and transparency - p. 347 30. Chapter 19. Counterfactual explainability in AI-driven drug discovery: Enhancing transparency and decision-making - p. 357 31. Chapter 20. Integrating AI in pharmacovigilance and clinical trial monitoring: Enhancing drug safety and efficacy in Kyrgyzstan’s and LMIC’s evolving healthcare landscape - p. 381 32. Index - p. 395
