RetroGraph: Retrosynthetic Planning with Graph Search
This work solves a specific efficiency problem in chemistry and drug discovery by reducing redundancies in retrosynthetic planning, representing an incremental improvement over existing methods.
The paper tackles the inefficiency in retrosynthetic planning by addressing redundant explorations of intermediate molecules in tree-based search methods, proposing a graph-based search policy that improves the search success rate to 99.47% on the USPTO benchmark, a 2.6-point gain over previous state-of-the-art.
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.