Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, applications, 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 for drugs, and the deep learning applications in various aspects of drug design. This book will give readers an intuitive and simple understanding of the encoding and decoding of drugs for model training, while deep learning methods profile the different training perspectives for drug design including sequence-based, 2D, and 3D drug design based on geometric deep learning. This book is suitable for readers who are seeking to learn and use deep learning methods and applications for drug discovery and other related fields. Deep Learning in Drug Design: Methods and Applications is particularly helpful to graduate 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 cases of deep learning for drug design
- Presents the major application fields of drug design based on deep learning including protein folding, retrosynthesis prediction, molecular docking, and ADMET prediction, among others
- Explains the artificial intelligence of deep learning for drug design models
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ISBN-13
978-0-443-32909-8 (9780443329098)
Schweitzer Klassifikation
Part 1: Deep Learning Theories and Methods for Drug Design1. Molecular Representations in Deep Learning2. CNNs in Drug Design3. GNNs in Drug Design4. RNNs and LSTM in Drug Design5. Deep Reinforcement Learning in Drug Design6. Transformer and Drug Design7. Generative Models for Drug Design8. Geometric Graph Learning for Drug Design9. Contrastive Learning and Pre-training Models for Drug Discovery10. Transfer Learning, Knowledge Distillation, and Meta-Learning for Drug Discovery11. Explainable Artificial Intelligence for Drug Design Models12. Large Language Models for Drug DesignPart 2: Deep Learning Applications in Drug Design13. Deep Learning for Protein Secondary Structure Prediction14. Deep Learning in Protein Structure Prediction15. Deep Learning in Molecular Interactions16. Deep Learning in Chemical Synthesis and Retrosynthesis17. Deep Learning for ADMET Prediction18. Deep Learning for Toxicity Prediction19. Deep Learning for TCR-pMHC Binding20. Deep Learning for B Cell Epitope Prediction and Receptor21. Deep Learning for Antigen-specific Antibody Design
