Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models, etc., as well as deep learning applications in various aspects of drug design. This book offers a comprehensive academic overview of deep learning in drug design. It begins with molecular representations, CNNs, GNNs, Transformers, generative models, explainable AI, large models, etc. Next, it covers deep learning applications like protein structure prediction, molecular interactions, ADMET prediction, antibody design, and so on. Finally, a separate chapter is dedicated to the introduction of the ethics and regulation of artificial intelligence in drug design. This book is ideal for readers aiming to learn and implement deep learning methods and applications in drug design and related fields.
Deep Learning in Drug Design: Methods and Applications is particularly helpful to undergraduate, graduate, and doctoral students in need of a practical guide to the principles of the discipline. Established researchers in the area will benefit from the detailed case studies and algorithms presented.
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. xvii 7. Preface - p. xxiii 8. Acknowledgments - p. xxv 9. Part 1: Deep learning theories and methods for drug design - p. P1 10. Chapter 1: Molecular representations in deep learning - p. 3 11. Chapter 2: CNNs in drug design - p. 19 12. Chapter 3: GNNs in drug design - p. 31 13. Chapter 4: RNNs and LSTM in drug design - p. 51 14. Chapter 5: Deep reinforcement learning in drug design - p. 73 15. Chapter 6: Transformer and drug design - p. 93 16. Chapter 7: Generative models for drug design - p. 109 17. Chapter 8: Geometric graph learning for drug design - p. 133 18. Chapter 9: Self-supervised learning for drug discovery - p. 153 19. Chapter 10: Transfer learning and meta-learning for drug discovery - p. 169 20. Chapter 11: Explainable artificial intelligence for drug design models - p. 187 21. Chapter 12: Large models in drug design - p. 203 22. Part 2: Deep learning applications in drug design - p. P2 23. Chapter 13: Deep learning for protein secondary structure prediction - p. 233 24. Chapter 14: Deep learning in protein structure prediction - p. 265 25. Chapter 15: Deep learning for affinity prediction and interface prediction in molecular interactions - p. 283 26. Chapter 16: Deep learning for complex structure prediction in molecular interactions - p. 297 27. Chapter 17: Deep learning in chemical synthesis and retrosynthesis - p. 309 28. Chapter 18: Deep learning for ADME prediction - p. 327 29. Chapter 19: Deep learning for toxicity prediction - p. 357 30. Chapter 20: Deep learning for TCR–pMHC binding prediction - p. 381 31. Chapter 21: Deep learning for B-cell epitope prediction and receptor–antigen binding prediction - p. 403 32. Chapter 22: Deep learning for antigen-specific antibody design - p. 425 33. Chapter 23: Ethical and regulatory of artificial intelligence in drug design - p. 443 34. Index - p. 459
Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models, etc., as well as deep learning applications in various aspects of drug design. This book offers a comprehensive academic overview of deep learning in drug design. It begins with molecular representations, CNNs, GNNs, Transformers, generative models, explainable AI, large models, etc. Next, it covers deep learning applications like protein structure prediction, molecular interactions, ADMET prediction, antibody design, and so on. Finally, a separate chapter is dedicated to the introduction of the ethics and regulation of artificial intelligence in drug design. This book is ideal for readers aiming to learn and implement deep learning methods and applications in drug design and related fields.
Deep Learning in Drug Design: Methods and Applications is particularly helpful to undergraduate, graduate, and doctoral students in need of a practical guide to the principles of the discipline. Established researchers in the area will benefit from the detailed case studies and algorithms presented.
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. xvii 7. Preface - p. xxiii 8. Acknowledgments - p. xxv 9. Part 1: Deep learning theories and methods for drug design - p. P1 10. Chapter 1: Molecular representations in deep learning - p. 3 11. Chapter 2: CNNs in drug design - p. 19 12. Chapter 3: GNNs in drug design - p. 31 13. Chapter 4: RNNs and LSTM in drug design - p. 51 14. Chapter 5: Deep reinforcement learning in drug design - p. 73 15. Chapter 6: Transformer and drug design - p. 93 16. Chapter 7: Generative models for drug design - p. 109 17. Chapter 8: Geometric graph learning for drug design - p. 133 18. Chapter 9: Self-supervised learning for drug discovery - p. 153 19. Chapter 10: Transfer learning and meta-learning for drug discovery - p. 169 20. Chapter 11: Explainable artificial intelligence for drug design models - p. 187 21. Chapter 12: Large models in drug design - p. 203 22. Part 2: Deep learning applications in drug design - p. P2 23. Chapter 13: Deep learning for protein secondary structure prediction - p. 233 24. Chapter 14: Deep learning in protein structure prediction - p. 265 25. Chapter 15: Deep learning for affinity prediction and interface prediction in molecular interactions - p. 283 26. Chapter 16: Deep learning for complex structure prediction in molecular interactions - p. 297 27. Chapter 17: Deep learning in chemical synthesis and retrosynthesis - p. 309 28. Chapter 18: Deep learning for ADME prediction - p. 327 29. Chapter 19: Deep learning for toxicity prediction - p. 357 30. Chapter 20: Deep learning for TCR–pMHC binding prediction - p. 381 31. Chapter 21: Deep learning for B-cell epitope prediction and receptor–antigen binding prediction - p. 403 32. Chapter 22: Deep learning for antigen-specific antibody design - p. 425 33. Chapter 23: Ethical and regulatory of artificial intelligence in drug design - p. 443 34. Index - p. 459
