arXiv:2605.00222v2 Announce Type: replace Abstract: Chemical reaction datasets such as USPTO suffer from substantial incompleteness, frequently missing byproducts, co-reactants, and stoichiometric coefficients. This limits their applicability and reliability in downstream applications. Here, we introduce CompleteRXN, a large-scale supervised benchmark for reaction completion under realistic missing-data conditions. We construct a dataset of aligned incomplete and atom-balanced reactions by mapping USPTO records to curated mechanistic reactions. We evaluate representative baselines, including a
The increasing sophistication of AI models and demand for accurate chemical reaction data are enabling new approaches to improve existing databases.
This development improves the reliability of chemical reaction datasets, which are critical for drug discovery, materials science, and synthetic biology applications.
AI models can now more effectively complete and correct chemical reaction data, leading to faster and more accurate research and development in chemistry-dependent fields.
- · Pharmaceutical R&D
- · Materials science
- · AI/ML in chemistry
- · Synthetic biology
- · Manual data curation
- · Traditional chemistry simulation methods
More accurate and faster identification of novel chemical compounds and reaction pathways.
Accelerated development cycles for new drugs, chemicals, and materials with fewer experimental iterations.
Enhanced automation in chemical laboratories and a potential reduction in R&D costs across multiple industries.
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