Papers › Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
Siqi Hong, Hankz Hankui Zhuo, Kebing Jin, Guang Shao, Zhanwen Zhou
In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities. Even experienced chemists often have difficulty to select the most promising transformations. The current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge or use expensive estimation methods for guiding. Here we an propose experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem. Instead of rollout, we build an experience guidance network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, EG-MCTS gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness. In a comparative experiment with the literature, our computer-generated routes mostly matched the reported routes. Routes designed for real drug compounds exhibit the effectiveness of EG-MCTS on assisting chemists performing retrosynthetic analysis.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-step retrosynthesis | USPTO-190 | EG-MCTS | Success Rate (100 model calls) | 85.79 | #3 of 5 | Archive leaderboard | report |
| Multi-step retrosynthesis | USPTO-190 | EG-MCTS | Success Rate (500 model calls) | 96.84 | #3 of 5 | 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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