Description
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.- Presents foundational concepts in machine learning, deep learning, and cheminformatics, enabling readers to understand and apply AI techniques across the drug discovery pipeline- Bridges disciplines through interdisciplinary insights- Connects life sciences, computer science, and bioinformatics, offering a structured entry point for researchers from diverse academic backgrounds- Integrates real-world examples and applications to illustrate how AI tools are used in molecular screening, ADMET modeling, and pharmacokinetics prediction
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
