The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks
Open accessAbstract
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and cannot provide satisfactory results. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis using transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of the reactants. By coupling with a neural network-based syntax corrector, our method achieved an accuracy of 59.0% on a standard benchmark data set, which outperformed other deep learning methods by >21% and template-based methods by >6%. More importantly, our method was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set.
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Cites
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Machine Learning in Computer-Aided Synthesis Planning
Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704
Computer‐Assisted Synthetic Planning: The End of the Beginning
Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., & Grzybowski, B. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506101 · Cited by 611
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00303 · Cited by 536
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Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041676 · Cited by 353
Autonomous Discovery in the Chemical Sciences Part I: Progress
Coley, C. W., Eyke, N. S., & Jensen, K. F. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 317
Generative Models as an Emerging Paradigm in the Chemical Sciences
Anstine, D. M., & Isayev, O. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c13467 · Cited by 313
A review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274
Unleashing the power of generative AI in drug discovery
Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195
Metadata
- Authors
- Zheng, S., Rao, J., Zhang, Z., Xu, J., & Yang, Y.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-12-11
- Volume
- 60
- Issue
- 1
- Pages
- 47-55
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2994678679
- Cited by
- 249
- References
- 4
- FWCI
- 17.19
- Type
- journal article
- Language
- en
- Open access
- green
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- Zheng, S., Rao, J., Zhang, Z., Xu, J., & Yang, Y. (2020). Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.9b00949
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