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 could not 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 {\tt 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 {\tt MCTS} gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness.
翻译:重新合成规划的问题是分析复杂的分子,并用简单的构件提供合成路径。 大量的化学反应导致各种可能性的组合爆炸,甚至有经验的化学家也无法选择最有希望的转变。 目前的方法依赖于人类定义的或经过机器训练的得分功能,这些功能的化学知识有限,或使用昂贵的估计方法,如推出来指导搜索。 在本文中,我们建议采用基于新的 MCTS 的反转合成规划方法,解决反转合成规划问题。 我们不是利用推广,而是建立一个经验指导网络,从搜索过程中的合成经验中学习知识。 对USPTO数据集基准的实验显示,我们的~t MCTS在效率和有效性方面都取得了显著进步。