  The main target of retrosynthesis is to recursively decompose desired
molecules into available building blocks. Existing template-based
retrosynthesis methods follow a template selection stereotype and suffer from
limited training templates, which prevents them from discovering novel
reactions. To overcome this limitation, we propose an innovative retrosynthesis
prediction framework that can compose novel templates beyond training
templates. As far as we know, this is the first method that uses machine
learning to compose reaction templates for retrosynthesis prediction. Besides,
we propose an effective reactant candidate scoring model that can capture
atom-level transformations, which helps our method outperform previous methods
on the USPTO-50K dataset. Experimental results show that our method can produce
novel templates for 15 USPTO-50K test reactions that are not covered by
training templates. We have released our source implementation.
