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MolAICal

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[资源] 一篇扩散模型的Nature Machine Intelligence论文和Elsevier上的深度学习药物书籍

各位好，

1. 之前在论坛中推荐了 “使用几何深度学习进行3d药物分子设计的方法和应用”：https://muchong.com/t-16189317-1，本课题组设计了新的自适应扩散模型，提出自适应自回归扩散方法（adaptive autoregressive diffusion approach），开发并训练出hudiff深度学习模型，包含针对常规抗体的hudiff-ab与纳米抗体的 hudiff-nb两个核心模块。发表在nature machine intelligence上，文章题目：an adaptive autoregressive diffusion approach to design active humanized antibodies and nanobodies, Nature Machine Intelligence (2025), https://www.nature.com/articles/s42256-025-01120-9 ，文章可以从附件中下载，欢迎讨论。

2. 并编著一本深度学习药物设计书籍《deep learning in drug design: methods and applications》，链接：https://doi.org/10.1016/c2023-0-52311-0 或 https://www.sciencedirect.com/book/9780443329081 或 https://shop.elsevier.com/books/deep-learning-in-drug-design/bai/978-0-443-32908-1

书籍有版权问题，应该可以通过学校订阅的数据库下载。欢迎讨论。

全书分为23章：

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

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