To be most useful for food scientists, such a machine learning model should not behave as a black box and allow for at least some rationalization of its predictions. The transformer architecture offers opportunities for interpretability through an analysis of its attention mechanism or gradient-based interpretability methods. The latter evaluates how changes in the input (such as atomic structure) impact the model's predictions, attributing the result to specific features . FART is the first interpretable, chemical language model for taste prediction of small molecules across all A Chemical Language Model for Molecular Taste Prediction four common taste categories. In the future, robust and interpretable taste prediction could advance drug formulation and rational food design as well as help elucidate the interaction of molecules with their respective taste receptors.