As ChemBERTa, the chemical language model used in this work, was pre-trained mostly on SMILES of small molecules, we curated our database accordingly with an average molecular weight of 374 Da ± 228, see Fig. as well as the SI for extended methodology. This notably excludes longer peptides which we do not consider in our approach. Previous work has explored this chemical space particularly in the context of umami and bitter prediction . Entries with invalid SMILES, charged molecules, and multiple neutral fragments, e.g. SMILES which included solvents, were removed. Data curation was performed with the cheminformatics package RDKit . All SMILES were canonicalized and subjected to the default RDKit standardization procedure. Duplicate removal based on canonicalized SMILES reduced the dataset by almost half to a final size of 15,025 entries. The large number of duplicates underlines the significant overlap among the databases used.