Finally, the MITE repository would represent an attractive training dataset for predictive machine learning-based algorithms. This could include the prediction of substrate specificities or regioselectivities of uncharacterized enzymes, the functional prediction of enzymes, and even putative bioactivities of NPs, provided that they contain a bioactivity-associated substructure introduced by a tailoring enzyme/maturase. MITE also pursues integration with other databases and would utilize cross-references where possible. For protein information, MITE will rely on links to UniProt/UniProtKB and NCBI GenBank GenPept; for compound information, MITE will accept links to PubChem , ChEBI , CHEMBL , ChemSpider , NP Atlas , LOTUS , GNPS , and CyanoMetDB ; and for reaction information, it will cross-reference to Rhea and BRENDA . To simplify cross-linking and referencing identifiers, MITE will use the Bioregistry as means to resolve identifiers; in turn, MITE will also submit references to the Bioregistry to simplify referencing by other resources . Of special note is MITE's relationship with Rhea: