Buch, Englisch, 426 Seiten, Format (B × H): 190 mm x 236 mm, Gewicht: 885 g
Foundations and Core Techniques
Buch, Englisch, 426 Seiten, Format (B × H): 190 mm x 236 mm, Gewicht: 885 g
ISBN: 978-0-443-44415-9
Verlag: Elsevier Science
Artificial Intelligence in Precision Drug Design, Volume One: Foundations and Core Techniques offers a comprehensive introduction to the transformative role of AI in modern drug discovery. The book lays the groundwork for understanding how machine learning, deep learning, and generative models are reshaping the development of precision therapeutics. It explores foundational topics such as AI-driven molecular screening, pharmacokinetics, ADMET modeling, toxicity prediction, and omics integration. In addition, sections address ethical, philosophical, and epistemological dimensions, ensuring a well-rounded perspective. Each chapter, authored by global experts, combines theoretical insights with real-world case studies to bridge the gap between AI and life sciences.
Designed for graduate students, researchers, and professionals in bioinformatics, biomedical sciences, and pharmaceutical R&D, this volume equips readers with essential knowledge and tools to navigate the evolving landscape of AI in drug design. It empowers interdisciplinary learners to apply cutting-edge AI techniques to real-world biomedical challenges.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. AI in Drug Design: A Historical and Future Perspective
2. Can Machines Truly Know? Epistemological Challenges in AI-Driven Drug Discovery
3. Ethical Implications of AI in Precision Drug Design: A Philosophical Inquiry
4. Metaphors of Medicine: A Literary Perspective on AI in Drug Discovery, Design and Target Precision
5. Artificial Intelligence in Molecular Screening: Advances, Challenges, and Future Perspectives
6. AI for Predicting Pharmacokinetics and Pharmacodynamics
7. AI for Predicting Drug-Likeness and Bioavailability
8. AI-Powered In Silico ADMET Modeling and Optimization in Drug Design
9. AI-Based Toxicity Prediction: Advancing Drug Safety and Risk Assessment
10. Leveraging AI for Integrating Genomics, Transcriptomics, and Proteomics
11. Artificial Intelligence in Multi-Omics Integration for Precision Drug Design
12. AI and Machine Learning for Disease Pathway Modelling
13. AI-Powered Genomic Medicine: Technologies and Challenges
14. PGP-Miner: An AI and Machine Learning Tool in Cancer Drug Development and Immunotherapy
15. Artificial Intelligence for Drug Repurposing: Opportunities and Challenges
16. Generative Artificial Intelligence for De-novo Drug Design
17. Bias and Transparency in AI and Machine Learning Models for Drug Design
18. Blockchain and AI in Drug Development: Securing Data Integrity and Transparency
19. Counterfactual Explainability in AI-Driven Drug Discovery: Enhancing Transparency and Decision-Making
20. Integrating AI in Pharmacovigilance and Clinical Trial Monitoring: Enhancing Drug Safety and Efficacy in Kyrgyzstan’s and LMIC’s Evolving Healthcare Landscape
