Retrosynthesis aims to identify reactants for synthesizing target molecules, serving as a fundamental tool in advancing organic synthesis and drug discovery. Some methods formulate retrosynthesis as predicting a sequence of molecular edits on the product molecule graph to generate intermediate synthons, which ultimately lead to the final reactants. However, these methods typically capture only limited associations among synthetic units, especially the relationships between molecular edits, which are vital to guide both the content and positioning of subsequent edits. To address this limitation, we propose RetroLEE, a model that enhances single-step retrosynthesis by incorporating Last Edit Embedding (LEE). First, we introduce a Last Edit Embedding Graph, which integrates the information from the previous edit into each atom’s representation to facilitate more accurate inferences. Second, we present a neighborhood aggregator that leverages neighboring edits to improve the localization of reaction centers. Experiments on the USPTO-50K dataset show that RetroLEE outperforms existing semi-template methods, achieving a new state-of-the-art benchmark with 91.2% top-10 accuracy. Case studies further demonstrate that RetroLEE can accurately predict plausible reactants, even for products with complex structures.