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

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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Journal
ChemRxiv
Published
2026-10-28
DOI
https://doi.org/10.26434/chemrxiv.15011178/v1
Primary Topic
Machine Learning in Materials Science
Type
preprint

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preprint

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

King Kuok Hii, Jeremy Graham Frey, Bao N. Nguyen, Samuel Munday et al.
2 links
ChemRxiv
Machine Learning in Materials Science
preprint

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

King Kuok Hii, Jeremy Graham Frey, Bao N. Nguyen, Samuel Munday, Alister S. Goodfellow, Ching Wai Fong, Alexander Atkins, George A. Hodgin, Rachel Napier, Jago F. Deane, Abigail E. Tranter
preprint en

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

ChemRxiv
University of Leeds (GB), University of Southampton (GB), Imperial College London (GB)
Engineering and Physical Sciences Research Council
Machine Learning in Materials Science
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OPRD-100: An Expert-Curated Dataset of Organic Reactions for Benchmarking Reaction Data Mining Tools — King Kuok Hii, Jeremy Graham Frey, et al. · ChemRxiv (2026) | TGRS Research Map | TGRS