Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

中文翻译：

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用于生物视网膜增强的强化学习。

代谢工程的目的是从生物体中产生感兴趣的化学物质，从而向更绿色的化学方向发展。尽管付出了很多努力，但研发过程仍然漫长且昂贵，因此需要有效的计算设计工具来探索化学生物合成空间。在这里，我们建议在化学相似性的指导下，使用基于蒙特卡洛树搜索强化学习方法的基于人工智能的方法来探索生物视网膜合成空间。我们在RetroPath RL（一种开源的模块化命令行工具）中实现此方法。我们在20个手动策划的实验途径的黄金数据集以及152个成功的代谢工程项目的较大数据集上对其进行了验证。而且，