Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search

11 Dec 2021  ·  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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Multi-step retrosynthesis USPTO-190 EG-MCTS Success Rate (100 model calls) 85.79 # 3
Success Rate (500 model calls) 96.84 # 3


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