Deep Learning in Drug Design
Methods and Applications
by Qifeng Bai, Tingyang Xu, Junzhou Huang
1st Edition
Publisher: Academic Press
Book Details
| Print ISBN | 9780443329081 |
| eText ISBN | 9780443329098 |
| Publisher | Academic Press |
| Publishing Year | 2025 |
| Edition | 1st Edition |
| Language | English |
| Pages | 498 |
Deep Learning in Drug Design: Methods and Applications, 1st Edition, summarizes recent methods and technological advances in deep learning applied to drug design. Edited by Qifeng Bai, Tingyang Xu, and Junzhou Huang, this publication offers a structured overview for undergraduate, graduate, doctoral, and postdoctoral students, as well as university professors and established researchers in computational biology and medicinal chemistry.
The thematic progression introduces core deep learning concepts required for molecular modeling. Key architectural frameworks discussed include molecular representations, convolutional neural networks (CNNs), graph neural networks (GNNs), Transformers, generative models, geometric deep learning, and large models designed for complex biochemical data.
Application-focused sections examine major tasks such as protein structure prediction, molecular interactions, retrosynthesis prediction, antibody design, and ADMET prediction. Additionally, the volume details explainable artificial intelligence models, providing clarity for interpreting predictive outputs in drug discovery workflows.
Table of Contents
Chapter 1: Molecular representations in deep learning
Chapter 2: CNNs in drug design
Chapter 3: GNNs in drug design
Chapter 4: RNNs and LSTM in drug design
Chapter 5: Deep reinforcement learning in drug design
Chapter 6: Transformer and drug design
Chapter 7: Generative models for drug design
Chapter 8: Geometric graph learning for drug design
Chapter 9: Self-supervised learning for drug discovery
Chapter 10: Transfer learning and meta-learning for drug discovery
Chapter 11: Explainable artificial intelligence for drug design models
Chapter 12: Large models in drug design
Chapter 13: Deep learning for protein secondary structure prediction
Chapter 14: Deep learning in protein structure prediction
Chapter 15: Deep learning for affinity prediction and interface prediction in molecular interactions
Chapter 16: Deep learning for complex structure prediction in molecular interactions
Chapter 17: Deep learning in chemical synthesis and retrosynthesis
Chapter 18: Deep learning for ADME prediction
Chapter 19: Deep learning for toxicity prediction
Chapter 20: Deep learning for TCR-pMHC binding prediction
Chapter 21: Deep learning for B-cell epitope prediction and receptor-antigen binding prediction
Chapter 22: Deep learning for antigen-specific antibody design
Chapter 23: Ethical and regulatory of artificial intelligence in drug design
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