  A fundamental problem in computational chemistry is to find a set of
reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction.
Existing state-of-the-art methods rely on matching the target molecule with a
large set of reaction templates, which are very computationally expensive and
also suffer from the problem of coverage. In this paper, we propose a novel
template-free approach called G2Gs by transforming a target molecular graph
into a set of reactant molecular graphs. G2Gs first splits the target molecular
graph into a set of synthons by identifying the reaction centers, and then
translates the synthons to the final reactant graphs via a variational graph
translation framework. Experimental results show that G2Gs significantly
outperforms existing template-free approaches by up to 63% in terms of the
top-1 accuracy and achieves a performance close to that of state-of-the-art
template based approaches, but does not require domain knowledge and is much
more scalable.
