  Recently, template-based (TB) and template-free (TF) molecule graph learning
methods have shown promising results to retrosynthesis. TB methods are more
accurate using pre-encoded reaction templates, and TF methods are more scalable
by decomposing retrosynthesis into subproblems, i.e., center identification and
synthon completion. To combine both advantages of TB and TF, we suggest
breaking a full-template into several semi-templates and embedding them into
the two-step TF framework. Since many semi-templates are reduplicative, the
template redundancy can be reduced while the essential chemical knowledge is
still preserved to facilitate synthon completion. We call our method SemiRetro,
introduce a new GNN layer (DRGAT) to enhance center identification, and propose
a novel self-correcting module to improve semi-template classification.
Experimental results show that SemiRetro significantly outperforms both
existing TB and TF methods. In scalability, SemiRetro covers 98.9\% data using
150 semi-templates, while previous template-based GLN requires 11,647 templates
to cover 93.3\% data. In top-1 accuracy, SemiRetro exceeds template-free G2G
4.8\% (class known) and 6.0\% (class unknown). Besides, SemiRetro has better
training efficiency than existing methods.
