  Retrosynthesis is a major task for drug discovery. It is formulated as a
graph-generating problem by many existing approaches. Specifically, these
methods firstly identify the reaction center, and break target molecule
accordingly to generate synthons. Reactants are generated by either adding
atoms sequentially to synthon graphs or directly adding proper leaving groups.
However, both two strategies suffer since adding atoms results in a long
prediction sequence which increases generation difficulty, while adding leaving
groups can only consider the ones in the training set which results in poor
generalization. In this paper, we propose a novel end-to-end graph generation
model for retrosynthesis prediction, which sequentially identifies the reaction
center, generates the synthons, and adds motifs to the synthons to generate
reactants. Since chemically meaningful motifs are bigger than atoms and smaller
than leaving groups, our method enjoys lower prediction complexity than adding
atoms and better generalization than adding leaving groups. Experiments on a
benchmark dataset show that the proposed model significantly outperforms
previous state-of-the-art algorithms.
