The identification of biocatalyzed reaction products plays a critical role in enzyme function prediction, drug discovery, and metabolic engineering. Uncovering the products of biocatalyzed reactions experimentally is both time-consuming and costly, which underscores the urgent need for computational methods. Previous machine learning methods have largely focused on spontaneous, non-biocatalyzed reactions but do not perform well when applied to biocatalyzed reactions specifically. We present a novel approach that harnesses graph-based deep learning to predict the primary products of enzyme-catalyzed reactions, considering both the protein sequence and substrates involved. On the recently published dataset EnzymeMap, we find that our method based on graph-editing outperforms existing transformer-based approaches.