The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Automated extraction of chemical synthesis actions from experimental procedures
Open accessAbstract
Experimental procedures for chemical synthesis are commonly reported in prose in patents or in the scientific literature. The extraction of the details necessary to reproduce and validate a synthesis in a chemical laboratory is often a tedious task requiring extensive human intervention. We present a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions. To achieve this, we design a set of synthesis actions with predefined properties and a deep-learning sequence to sequence model based on the transformer architecture to convert experimental procedures to action sequences. The model is pretrained on vast amounts of data generated automatically with a custom rule-based natural language processing approach and refined on manually annotated samples. Predictions on our test set result in a perfect (100%) match of the action sequence for 60.8% of sentences, a 90% match for 71.3% of sentences, and a 75% match for 82.4% of sentences.
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Cites
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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
A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679
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Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., & Olivetti, E. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b03500 · Cited by 473
Information retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281
Semi-supervised machine-learning classification of materials synthesis procedures
Huo, H., Rong, Z., Kononova, O., Sun, W., Botari, T., He, T., Tshitoyan, V., & Ceder, G. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0204-1 · Cited by 151
Cited by
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“In the past few years, DL approaches such as LSTMs and transformer-based models have been employed to extract various categories of information [307], and in particular materials synthesis information [302, 308, 317] from text sources.”
MethodPyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465
Self-citationDe novo molecular design and generative models
Meyers, J., Fabian, B., & Brown, N. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.05.019 · Cited by 292
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
Opportunities and challenges of text mining in materials research
Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience · doi:10.1016/j.isci.2021.102155 · Cited by 182
Metadata
- Authors
- Vaucher, A. C., Zipoli, F., Geluykens, J., Nair, V. H., Schwaller, P., & Laino, T.
- Venue
- Nature Communications
- Published
- 2020-07-17
- Volume
- 11
- Issue
- 1
- Pages
- 3601-3601
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3043647281
- Cited by
- 202
- References
- 8
- FWCI
- 10.32
- Type
- journal article
- Language
- en
- Open access
- gold
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- Vaucher, A. C., Zipoli, F., Geluykens, J., Nair, V. H., Schwaller, P., & Laino, T. (2020). Automated extraction of chemical synthesis actions from experimental procedures. Nature Communications. https://doi.org/10.1038/s41467-020-17266-6
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
