Papers › Retrosynthetic reaction prediction using neural sequence-to-sequence models
Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, Vijay Pande
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Single-step retrosynthesis | USPTO-50k | Expert System (reaction class as prior) | Top-1 accuracy | 35.2 | #35 of 35 | Archive leaderboard | report |
| Single-step retrosynthesis | USPTO-50k | Expert System (reaction class as prior) | Top-10 accuracy | 65.1 | #35 of 35 | Archive leaderboard | report |
| Single-step retrosynthesis | USPTO-50k | Expert System (reaction class as prior) | Top-3 accuracy | 52.3 | #35 of 35 | Archive leaderboard | report |
| Single-step retrosynthesis | USPTO-50k | Expert System (reaction class as prior) | Top-5 accuracy | 59.1 | #35 of 35 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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