论文标题
retro*：通过神经指导的学习返回合成计划*搜索
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
论文作者
论文摘要
循环合成计划是有机化学中的一项关键任务，它识别一系列可以导致目标产物合成的反应。大量可能的化学转化使搜索空间的大小变得很大，即使对于经验丰富的化学家来说，逆合合成计划也有挑战性。但是，现有方法要么需要通过较高的差异来进行昂贵的回报估算，要么以搜索速度而不是质量来优化。在本文中，我们提出了Retro*，这是一种基于神经的A* - 样算法，可有效地发现高质量的合成路线。它将搜索视为一棵和树，并学习了具有非政策数据的神经搜索偏见。然后在这个神经网络的指导下，它在新的计划情节中有效地执行了最佳优先搜索。基准USPTO数据集的实验表明，在成功率和解决方案质量方面，我们提出的方法优于现有的最新方法，同时更有效。
Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possible chemical transformations makes the size of the search space very big, and retrosynthetic planning is challenging even for experienced chemists. However, existing methods either require expensive return estimation by rollout with high variance, or optimize for search speed rather than the quality. In this paper, we propose Retro*, a neural-based A*-like algorithm that finds high-quality synthetic routes efficiently. It maintains the search as an AND-OR tree, and learns a neural search bias with off-policy data. Then guided by this neural network, it performs best-first search efficiently during new planning episodes. Experiments on benchmark USPTO datasets show that, our proposed method outperforms existing state-of-the-art with respect to both the success rate and solution quality, while being more efficient at the same time.
