Transformer-based artificial neural networks for the conversion between chemical notations
By Lev Krasnov, I. Khokhlov, M. Fedorov, S. Sosnin. 2021
35 citations recorded by the source corpus.
We developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Natural Language Processing
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Citations in the corpus
- Galactica: A Large Language Model for Science (2022, methodology): “Previous works such as STOUT and Struct2IUPAC have explored the possiblity of using RNNs and Transformers for this task (Rajan et al., 2021; Krasnov et al., 2021).”
- ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings (2025, unknown): “Of these 900 tokens, a curated subset consists of IUPAC nomenclature fragments identified with the rule-based IUPAC tokenizer of Krasnov et al. [62], while the remaining trained tokens were selected with preference for entries already present in SciBERT but absent from bert-base-uncased [43].”