Description
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.
Table of Contents
1. AI in Drug Design: A Historical and Future Perspective2. Can Machines Truly Know? Epistemological Challenges in AI-Driven Drug Discovery3. Ethical Implications of AI in Precision Drug Design: A Philosophical Inquiry4. Metaphors of Medicine: A Literary Perspective on AI in Drug Discovery, Design and Target Precision5. Artificial Intelligence in Molecular Screening: Advances, Challenges, and Future Perspectives6. AI for Predicting Pharmacokinetics and Pharmacodynamics7. AI for Predicting Drug-Likeness and Bioavailability8. AI-Powered In Silico ADMET Modeling and Optimization in Drug Design9. AI-Based Toxicity Prediction: Advancing Drug Safety and Risk Assessment10. Leveraging AI for Integrating Genomics, Transcriptomics, and Proteomics11. Artificial Intelligence in Multi-Omics Integration for Precision Drug Design12. AI and Machine Learning for Disease Pathway Modelling13. AI-Powered Genomic Medicine: Technologies and Challenges14. PGP-Miner: An AI and Machine Learning Tool in Cancer Drug Development and Immunotherapy15. Artificial Intelligence for Drug Repurposing: Opportunities and Challenges16. Generative Artificial Intelligence for De-novo Drug Design17. Bias and Transparency in AI and Machine Learning Models for Drug Design18. Blockchain and AI in Drug Development: Securing Data Integrity and Transparency19. Counterfactual Explainability in AI-Driven Drug Discovery: Enhancing Transparency and Decision-Making20. Integrating AI in Pharmacovigilance and Clinical Trial Monitoring: Enhancing Drug Safety and Efficacy in Kyrgyzstan's and LMIC's Evolving Healthcare Landscape
