  Retrosynthesis, of which the goal is to find a set of reactants for
synthesizing a target product, is an emerging research area of deep learning.
While the existing approaches have shown promising results, they currently lack
the ability to consider availability (e.g., stability or purchasability) of the
reactants or generalize to unseen reaction templates (i.e., chemical reaction
rules). In this paper, we propose a new approach that mitigates the issues by
reformulating retrosynthesis into a selection problem of reactants from a
candidate set of commercially available molecules. To this end, we design an
efficient reactant selection framework, named RetCL (retrosynthesis via
contrastive learning), for enumerating all of the candidate molecules based on
selection scores computed by graph neural networks. For learning the score
functions, we also propose a novel contrastive training scheme with hard
negative mining. Extensive experiments demonstrate the benefits of the proposed
selection-based approach. For example, when all 671k reactants in the USPTO
{database} are given as candidates, our RetCL achieves top-1 exact match
accuracy of $71.3\%$ for the USPTO-50k benchmark, while a recent
transformer-based approach achieves $59.6\%$. We also demonstrate that RetCL
generalizes well to unseen templates in various settings in contrast to
template-based approaches.
