Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
This addresses the problem of selecting optimal synthetic routes for chemists, representing a strong specific gain in the domain of computational chemistry.
The paper tackles the combinatorial explosion in retrosynthetic planning for synthesizing complex molecules by proposing an experience-guided Monte Carlo tree search (EG-MCTS) method, which shows significant improvements in efficiency and effectiveness on benchmark datasets and matches reported routes in comparative experiments.
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.