Automated extraction of chemical synthesis actions from experimental procedures
Extracting experimental operations for chemical synthesis from procedures reported in prose is a tedious task. Here the authors develop a deep-learning model based on the transformer architecture to translate experimental procedures from the field of organic chemistry into synthesis actions.
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17266-6 |