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OPRD-Imp: a dataset of experimentally observed impurities and side products in multi-step pharmaceutical syntheses — arXiv:2611.07488

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.07488
License (as archived): CC0-1.0
Archived: 2026-11-06T14:51:10.989Z
SHA-256 of the archived text: 17876a31ebec2166cac52a92a56670d330959012aae564f3dd89883f73d5b203
Stable URL of this page: https://metraipolis.com/documento/dc_4ee3bbdf5c2e

How to cite

OPRD-Imp: a dataset of experimentally observed impurities and side products in multi-step pharmaceutical syntheses — arXiv:2611.07488 — arXiv (en), https://arxiv.org/abs/2611.07488 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4ee3bbdf5c2e (Metraipolis archive dc_4ee3bbdf5c2e, sha256 17876a31ebec2166cac52a92a56670d330959012aae564f3dd89883f73d5b203)

BibTeX

@misc{dc4ee3bbdf5c2e,
  title = {OPRD-Imp: a dataset of experimentally observed impurities and side products in multi-step pharmaceutical syntheses — arXiv:2611.07488},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.07488},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4ee3bbdf5c2e; sha256 17876a31ebec2166cac52a92a56670d330959012aae564f3dd89883f73d5b203},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.07488. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Predicting reaction impurities is a critical yet persistently data-limited challenge in pharmaceutical process chemistry. While machine learning models for reaction outcome prediction have advanced rapidly, benchmarking their ability to predict impurities remains impractical due to the absence of curated, publicly accessible datasets. We present OPRD-Imp, a dataset of 229 organic reactions drawn from 62 publications in Organic Process Research & Development, each annotated with experimentally observed impurities and their mechanistic origins. A total of 784 impurity entries is provided, classified into four origin categories. Reactions are represented as reaction SMILES and impurities as individual SMILES strings, with multi-step synthetic route connectivity preserved. All data were manually extracted from the source publications by trained chemists following a standardized extraction protocol, with expert chemical validation and manual structure drawing. OPRD-Imp is intended to serve as a benchmark resource for evaluating models that predict reaction impurities, providing experimentally reported impurity annotations and mechanistic origin labels that are not systematically represented in conventional reaction datasets.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Chemical Physics (physics.chem-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.07488 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:21:42.660Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).