Several text mining tools for chemical name recognition have been developed to support the extraction of chemical information from text. Still, manual validation is often needed to ensure that the chemical names have been recognized correctly and that no information has been omitted. Even the most accurate software packages used for text mining of chemical names report having precision and recall around 90%, indicating that without manual validation approximately 10% of the curated information would contain errors. This becomes even more complicated when using recognition of chemical names to extract potential TPs as observed by Krier et al. for the PubChem Metabolism and Metabolites section. This is due to that the fact that even correctly recognized chemical names may be irrelevant to the transformations occurring and thus lead to incorrect reactions if not manually validated. An example of this can be seen in Figure . This study introduces "ShinyTPs," an application designed to visualize and curate text-mined chemical reactions from PubChem. Its purpose is to aid in curating TPs and their associated reactions sourced from the Metabolism and Metabolites section of PubChem, and thus to enable their inclusion in downstream workflows or to serve as highly curated datasets for machine learning models.