Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search

Abstract Retrosynthetic planning problem is to analyze a complex molecule and give a synthetic route using simple building blocks. The huge number of chemical reactions leads to a combinatorial explosion of possibilities, and even the 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 such as rollout to guide the search. In this paper, we propose EG-MCTS, a novel MCTS-based retrosynthetic planning approach, to deal with retrosynthetic planning problem. Instead of exploiting rollout, we build an Experience Guidance Network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, our EG-MCTS gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness. Routes designed by EG-MCTS for real drugs or compounds exhibit the effectiveness of our approach on assisting chemists performing retrosynthetic analysis. Our EG-MCTS system solves almost a quarter more and twice times faster than the traditional computer-aided MCTS search method. In a comparative experiment with the literature, our computer-generated routes were generally viewed to be equivalent to reported literature routes by chemists.