ArticleJournal of cheminformatics2025
ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Interpretable Small-Sample Deep Learning with Approximate Attribution Enhancement for Atomically Precise Gold Nanoclusters Synthesis.Molecules (Basel, Switzerland) · 2026Article
- CLRe: A Synergistic Dual-Engine Framework for One-Step Retrosynthesis Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era.Journal of chemical theory and computation · 2026Review
- Reaction-conditioned generative model for catalyst design and optimization with CatDRX.Communications chemistry · 2025Article
- Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.Communications chemistry · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Accurate chemical reaction prediction is critical for reducing both cost and time in drug development. This study introduces ReactionT5, a transformer-based chemical reaction foundation model pre-trained on the Open Reaction Database-a large publicly available reaction dataset. In benchmarks for product prediction, retrosynthesis, and yield prediction, ReactionT5 outperformed existing models. Specifically, ReactionT5 achieved 97.5% accuracy in product prediction, 71.0% in retrosynthesis, and a coefficient of determination of 0.947 in yield prediction. Remarkably, ReactionT5, when fine-tuned with only a limited dataset of reactions, achieved performance on par with models fine-tuned on the complete dataset. Additionally, the visualization of ReactionT5 embeddings illustrates that the model successfully captures and represents the chemical reaction space, indicating effective learning of reaction properties.
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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.