  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.
