A multi-stream network for retrosynthesis prediction

Graphical abstract

References

[1] Coley C W, Rogers L, Green W H, Jensen K F . Computer-assisted retrosynthesis based on molecular similarity. ACS Central Science, 2017, 3( 12): 1237–1245

[2] Segler M H S, Waller M P . Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry-A European Journal, 2017, 23( 25): 5966–5971

[3] Zheng S, Rao J, Zhang Z, Xu J, Yang Y. Predicting retrosynthetic reactions using self-corrected transformer neural networks. Journal of Chemical Information and Modeling, 2022, 60 (1): 47 − 55

[4] Weininger D . SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information & Computer Sciences, 1988, 28( 1): 31–36

[5] Ye X B, Guan Q, Luo W, Fang L, Lai Z R, Wang J . Molecular substructure graph attention network for molecular property identification in drug discovery. Pattern Recognition, 2022, 128: 108659

[6] Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Nguyen Q L, Ho S, Sloane J, Wender P, Pande V . Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Central Science, 2017, 3( 10): 1103–1113

[7] Shi C, Xu M, Guo H, Zhang M, Tang J. A graph to graphs framework for retrosynthesis prediction. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 818

[8] Chen B, Shen T, Jaakkola T S, Barzilay R. Learning to make generalizable and diverse predictions for retrosynthesis. 2019, arXiv preprint arXiv: 1910.09688

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2019YFA0904303), and the National Natural Science Foundation of China (Grant No. 62072206).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

Supporting Information

The supporting information is available online at journal.hep.com.cn and link.springer.com.

RIGHTS & PERMISSIONS

2024 Higher Education Press