  Retrosynthesis prediction is one of the fundamental challenges in organic
synthesis. The task is to predict the reactants given a core product. With the
advancement of machine learning, computer-aided synthesis planning has gained
increasing interest. Numerous methods were proposed to solve this problem with
different levels of dependency on additional chemical knowledge. In this paper,
we propose Retroformer, a novel Transformer-based architecture for
retrosynthesis prediction without relying on any cheminformatics tools for
molecule editing. Via the proposed local attention head, the model can jointly
encode the molecular sequence and graph, and efficiently exchange information
between the local reactive region and the global reaction context. Retroformer
reaches the new state-of-the-art accuracy for the end-to-end template-free
retrosynthesis, and improves over many strong baselines on better molecule and
reaction validity. In addition, its generative procedure is highly
interpretable and controllable. Overall, Retroformer pushes the limits of the
reaction reasoning ability of deep generative models.
