  Retrosynthesis -- the process of identifying a set of reactants to synthesize
a target molecule -- is of vital importance to material design and drug
discovery. Existing machine learning approaches based on language models and
graph neural networks have achieved encouraging results. In this paper, we
propose a framework that unifies sequence- and graph-based methods as
energy-based models (EBMs) with different energy functions. This unified
perspective provides critical insights about EBM variants through a
comprehensive assessment of performance. Additionally, we present a novel dual
variant within the framework that performs consistent training over Bayesian
forward- and backward-prediction by constraining the agreement between the two
directions. This model improves state-of-the-art performance by 9.6% for
template-free approaches where the reaction type is unknown.
