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Retrosynthesis prediction is a fundamental problem in organic synthesis,where the task is to identify precursor molecules that can be used tosynthesize a target molecule. A key consideration in building neural models forthis task is aligning model design with strategies adopted by chemists.Building on this viewpoint, this paper introduces a graph-based approach thatcapitalizes on the idea that the graph topology of precursor molecules islargely unaltered during a chemical reaction. The model first predicts the setof graph edits transforming the target into incomplete molecules calledsynthons. Next, the model learns to expand synthons into complete molecules byattaching relevant leaving groups. This decomposition simplifies thearchitecture, making its predictions more interpretable, and also amenable tomanual correction. Our model achieves a top-1 accuracy of $53.7\%$,outperforming previous template-free and semi-template-based methods.

Learning Graph Models for Retrosynthesis Prediction