Reusability report: Assessment of reproducibility and applicability to external datasets for RXNGraphormer

Deep learning has substantially advanced reaction-yield prediction and synthesis-planning methodologies, yet achieving a unified architecture capable of transferring across these tasks remains a central challenge in chemical machine learning. Xu et al. recently developed RXNGraphormer, which combines a pretrained graph–transformer encoder with a delta-molecular reaction representation designed to support cross-task generalization. Here, in this reusability report, we independently assess the reproducibility and practical applicability of RXNGraphormer using the released implementation, pretrained checkpoint and benchmark datasets. All major regression and sequence-generation results reported in the original study were consistently reproduced, including the relative difficulty patterns in out-of-sample evaluations, demonstrating the stability and transparency of the published workflow. To evaluate reusability, we examined the model’s transfer to multiple high-throughput datasets generated under standardized experimental conditions. In these settings, the pretrained encoder adapted efficiently and delivered strong predictive performance with minimal fine-tuning. On an external sequence-prediction benchmark beyond the original USPTO setting, RXNGraphormer retained strong forward prediction capability, whereas retrosynthetic prediction was more sensitive to distributional shift. Overall, our results show that RXNGraphormer is a reproducible and practically reusable framework for unified reaction learning, while also highlighting the continued importance of harmonized reaction representations, curated data and domain-specific refinement.