  Retrosynthetic planning, which aims to find a reaction pathway to synthesize
a target molecule, plays an important role in chemistry and drug discovery.
This task is usually modeled as a search problem. Recently, data-driven methods
have attracted many research interests and shown promising results for
retrosynthetic planning. We observe that the same intermediate molecules are
visited many times in the searching process, and they are usually independently
treated in previous tree-based methods (e.g., AND-OR tree search, Monte Carlo
tree search). Such redundancies make the search process inefficient. We propose
a graph-based search policy that eliminates the redundant explorations of any
intermediate molecules. As searching over a graph is more complicated than over
a tree, we further adopt a graph neural network to guide the search over
graphs. Meanwhile, our method can search a batch of targets together in the
graph and remove the inter-target duplication in the tree-based search methods.
Experimental results on two datasets demonstrate the effectiveness of our
method. Especially on the widely used USPTO benchmark, we improve the search
success rate to 99.47%, advancing previous state-of-the-art performance for 2.6
points.
