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PreprintCC BYChemRxiv

OPRD-100: An Expert-Curated Dataset of Organic Reactions for Benchmarking Reaction Data Mining Tools

  • George A. Hodgin1
  • Alexander P. Atkins2
  • Jago F. Deane1
  • Ching Wai (Andy) Fong1
  • Alister S. Goodfellow1
  • Samuel A. Munday3
  • Rachel Napier1
  • Abigail E. Tranter1
  • Jeremy G. Frey3
  • King Kuok (Mimi) Hii2
  • Bao N. Nguyen1
  1. 1School of Chemistry, University of Leeds, Woodhouse Lane, Leeds, United Kingdom, LS2 9JT
  2. 2Molecular Sciences Research Hub, Imperial College London, 82 Wood Lane, London, United Kingdom, W12 0BZ
  3. 3School of Chemistry, University of Southampton, University Road, Highfield, Southampton, SO17 1BJ
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Abstract

The increasing use of AI in chemical research requires reliable reaction data to support efficient synthetic route design and reaction discovery. We present OPRD-100, an expert-curated and cross-validated dataset of 3,878 reactions extracted from 100 Organic Process Research & Development articles published between 1999 and 2024. Each entry captures detailed reactivity information, including yield, stereochemistry, temperature, time, solvents, and reagents. Analysis of the dataset reveals the relative importance of reaction data reported in tables versus text—particularly for reactions with low or no yields—and highlights the challenges of consistently capturing information from complex studies. Comparison with Reaxys showed systematic omissions and a strong bias toward high-yield reactions, underscoring the risks of relying on uncurated data. OPRD-100 thus provides a robust benchmark for training and evaluating AI tools in industrially relevant organic synthesis. Reaction data, data mining, benchmarking dataset

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Cite

BibTeXRIS
BibTeX
@misc{hodgin2026oprd100,
  title = {{OPRD-100: An Expert-Curated Dataset of Organic Reactions for Benchmarking Reaction Data Mining Tools}},
  author = {George A. Hodgin and Alexander P. Atkins and Jago F. Deane and Ching Wai (Andy) Fong and Alister S. Goodfellow and Samuel A. Munday and Rachel Napier and Abigail E. Tranter and Jeremy G. Frey and King Kuok (Mimi) Hii and Bao N. Nguyen},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15011178/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011178/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - OPRD-100: An Expert-Curated Dataset of Organic Reactions for Benchmarking Reaction Data Mining Tools
AU  - George A. Hodgin
AU  - Alexander P. Atkins
AU  - Jago F. Deane
AU  - Ching Wai (Andy) Fong
AU  - Alister S. Goodfellow
AU  - Samuel A. Munday
AU  - Rachel Napier
AU  - Abigail E. Tranter
AU  - Jeremy G. Frey
AU  - King Kuok (Mimi) Hii
AU  - Bao N. Nguyen
PY  - 2026
DA  - 2026/10/28
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15011178/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011178/v1
AB  - The increasing use of AI in chemical research requires reliable reaction data to support efficient synthetic route design and reaction discovery. We present OPRD-100, an expert-curated and cross-validated dataset of 3,878 reactions extracted from 100 Organic Process Research & Development articles published between 1999 and 2024. Each entry captures detailed reactivity information, including yield, stereochemistry, temperature, time, solvents, and reagents. Analysis of the dataset reveals the relative importance of reaction data reported in tables versus text—particularly for reactions with low or no yields—and highlights the challenges of consistently capturing information from complex studies. Comparison with Reaxys showed systematic omissions and a strong bias toward high-yield reactions, underscoring the risks of relying on uncurated data. OPRD-100 thus provides a robust benchmark for training and evaluating AI tools in industrially relevant organic synthesis. Reaction data, data mining, benchmarking dataset
ER  -