ArticleDigital discovery2026
Data augmentation in a triple transformer loop retrosynthesis model.
Article in Digital discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Quantifying the failure modes of current one-step retrosynthesis models.Chemical science · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits their usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM → P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM + R by TTL transformer T3. We generated up to 5000 reactions per template, resulting in 27.5m validated fictive reactions covering the chemical space of the original USPTO dataset. To exemplify the use of this dataset, we demonstrate that a single-step retrosynthesis transformer model trained on a template equilibrated subset of 1 097 374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.