The DOI matches its Crossref record.
Chemical Name to Structure: OPSIN, an Open Source Solution
Abstract
We have produced an open source, freely available, algorithm (Open Parser for Systematic IUPAC Nomenclature, OPSIN) that interprets the majority of organic chemical nomenclature in a fast and precise manner. This has been achieved using an approach based on a regular grammar. This grammar is used to guide tokenization, a potentially difficult problem in chemical names. From the parsed chemical name, an XML parse tree is constructed that is operated on in a stepwise manner until the structure has been reconstructed from the name. Results from OPSIN on various computer generated name/structure pair sets are presented. These show exceptionally high precision (99.8%+) and, when using general organic chemical nomenclature, high recall (98.7-99.2%). This software can serve as the basis for future open source developments of chemical name interpretation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Mordred: a molecular descriptor calculator
Moriwaki, H., Tian, Y.-S., Kawashita, N., & Takagi, T. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0258-y · Cited by 1,543
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy
Djoumbou Feunang, Y., Eisner, R., Knox, C., Chepelev, L., Hastings, J., Owen, G., Fahy, E., Steinbeck, C., Subramanian, S., Bolton, E., et al. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0174-y · Cited by 1,525
Augmenting large language models with chemistry tools
M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00832-8 · Cited by 794
The LOTUS initiative for open knowledge management in natural products research
Rutz, A., Sorokina, M., Galgonek, J., Mietchen, D., Willighagen, E., Gaudry, A., Graham, J. G., Stephan, R., Page, R., Vondrášek, J., et al. (2022) · eLife · doi:10.7554/elife.70780 · Cited by 429
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
The IUPHAR/BPS Guide to PHARMACOLOGY in 2024
Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Spedding, M., & Davies, J. A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad944 · Cited by 226
Williams, A. J., Gaines, L. G. T., Grulke, C. M., Lowe, C. N., Sinclair, G. F. B., Samano, V., Thillainadarajah, I., Meyer, B., Patlewicz, G., & Richard, A. M. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.850019 · Cited by 77
Computational screening of umami tastants using deep learning
Dutta, P., Gajula, K., Verma, N., Jain, D., Gupta, R., & Rai, B. (2025) · Molecular Diversity · doi:10.1007/s11030-024-11006-4 · Cited by 6
Metadata
- Authors
- Lowe, D. M., Corbett, P. T., Murray-Rust, P., & Glen, R. C.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-03-09
- Volume
- 51
- Issue
- 3
- Pages
- 739-753
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2034354062
- Cited by
- 236
- References
- 0
- FWCI
- 6.50
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.