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.- Introduces the basic theories, current methods, and principles of deep learning for drug design- Presents the major application fields of drug design based on deep learning including protein folding, retrosynthesis prediction, molecular generation, molecular docking, and ADMET prediction, among others- Details explainable artificial intelligence for drug design models
Table of Contents
PART 1: Deep learning theories and methods for drug design1. CHAPTER 1 Molecular representations in deep learning2. CHAPTER 2 CNNs in drug design3. CHAPTER 3 GNNs in drug design4. CHAPTER 4 RNNs and LSTM in drug design5. CHAPTER 5 Deep reinforcement learning in drug design6. CHAPTER 6 Transformer and drug design7. CHAPTER 7 Generative models for drug design8. CHAPTER 8 Geometric graph learning for drug design9. CHAPTER 9 Self-supervised learning for drug discovery10. CHAPTER 10 Transfer learning and meta-learning for drug discovery11. CHAPTER 11 Explainable artificial intelligence for drug design models12. CHAPTER 12 Large models in drug designPART 2: Deep learning applications in drug design13. CHAPTER 13 Deep learning for protein secondary structure prediction14. CHAPTER 14 Deep learning in protein structure prediction15. CHAPTER 15 Deep learning for affinity prediction and interface prediction in molecular interactions16. CHAPTER 16 Deep learning for complex structure prediction in molecular interactions17. CHAPTER 17 Deep learning in chemical synthesis and retrosynthesis18. CHAPTER 18 Deep learning for ADME prediction19. CHAPTER 19 Deep learning for toxicity prediction20. CHAPTER 20 Deep learning for TCR-pMHC binding prediction21. CHAPTER 21 Deep learning for B-cell epitope prediction and receptor-antigen bindingprediction22. CHAPTER 22 Deep learning for antigen-specific antibody design23. CHAPTER 23 Ethical and regulatory of artificial intelligence in drug design
