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. Molecular Representations in Deep Learning
2. CNNs in Drug Design
3. GNNs in Drug Design
4. RNNs and LSTM in Drug Design
5. Deep Reinforcement Learning in Drug Design
6. Transformer and Drug Design
7. Generative Models for Drug Design
8. Geometric Graph Learning for Drug Design
9. Contrastive Learning and Pre-training Models for Drug Discovery
10. Transfer Learning, Knowledge Distillation, and Meta-Learning for Drug Discovery
11. Explainable Artificial Intelligence for Drug Design Models
12. Large Language Models for Drug Design
Part 2: Deep Learning Applications in Drug Design
13. Deep Learning for Protein Secondary Structure Prediction
14. Deep Learning in Protein Structure Prediction
15. Deep Learning in Molecular Interactions
16. Deep Learning in Chemical Synthesis and Retrosynthesis
17. Deep Learning for ADMET Prediction
18. Deep Learning for Toxicity Prediction
19. Deep Learning for TCR-pMHC Binding
20. Deep Learning for B Cell Epitope Prediction and Receptor
21. Deep Learning for Antigen-specific Antibody Design
