Full Description
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.
Contents
PART 1: Deep learning theories and methods for drug design
1. CHAPTER 1 Molecular representations in deep learning
2. CHAPTER 2 CNNs in drug design
3. CHAPTER 3 GNNs in drug design
4. CHAPTER 4 RNNs and LSTM in drug design
5. CHAPTER 5 Deep reinforcement learning in drug design
6. CHAPTER 6 Transformer and drug design
7. CHAPTER 7 Generative models for drug design
8. CHAPTER 8 Geometric graph learning for drug design
9. CHAPTER 9 Self-supervised learning for drug discovery
10. CHAPTER 10 Transfer learning and meta-learning for drug discovery
11. CHAPTER 11 Explainable artificial intelligence for drug design models
12. CHAPTER 12 Large models in drug design
PART 2: Deep learning applications in drug design
13. CHAPTER 13 Deep learning for protein secondary structure prediction
14. CHAPTER 14 Deep learning in protein structure prediction
15. CHAPTER 15 Deep learning for affinity prediction and interface prediction in molecular interactions
16. CHAPTER 16 Deep learning for complex structure prediction in molecular interactions
17. CHAPTER 17 Deep learning in chemical synthesis and retrosynthesis
18. CHAPTER 18 Deep learning for ADME prediction
19. CHAPTER 19 Deep learning for toxicity prediction
20. CHAPTER 20 Deep learning for TCR-pMHC binding prediction
21. CHAPTER 21 Deep learning for B-cell epitope prediction and receptor-antigen binding
prediction
22. CHAPTER 22 Deep learning for antigen-specific antibody design
23. CHAPTER 23 Ethical and regulatory of artificial intelligence in drug design
