Papers › G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
Zaiyun Lin, Shiqiu Yin, Lei Shi, Wenbiao Zhou, YingSheng Zhang
Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields. The goal is to find reactants molecules that can synthesize product molecules. To solve this task, we propose a new graph-to-graph transformation model, G2GT, in which the graph encoder and graph decoder are built upon the standard transformer structure. We also show that self-training, a powerful data augmentation method that utilizes unlabeled molecule data, can significantly improve the model's performance. Inspired by the reaction type label and ensemble learning, we proposed a novel weak ensemble method to enhance diversity. We combined beam search, nucleus, and top-k sampling methods to further improve inference diversity and proposed a simple ranking algorithm to retrieve the final top-10 results. We achieved new state-of-the-art results on both the USPTO-50K dataset, with top1 accuracy of 54%, and the larger data set USPTO-full, with top1 accuracy of 50%, and competitive top-10 results.
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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 | G2GT (reaction class unknown) | Top-1 accuracy | 54.1 | #19 of 35 | Archive leaderboard | report |
| Single-step retrosynthesis | USPTO-50k | G2GT (reaction class unknown) | Top-10 accuracy | 77.7 | #19 of 35 | Archive leaderboard | report |
| Single-step retrosynthesis | USPTO-50k | G2GT (reaction class unknown) | Top-3 accuracy | 69.9 | #19 of 35 | Archive leaderboard | report |
| Single-step retrosynthesis | USPTO-50k | G2GT (reaction class unknown) | Top-5 accuracy | 74.5 | #19 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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