To better understand how FART arrives at its predictions, we implemented an interpretability technique based on integrating the gradients of the neural network's output along the path from a baseline input (the zero embedding) to the input at hand , see SI. For FART, this enables the visualization of atoms and functional groups that contribute positively toward the prediction of a given label (e.g., "sour") in green, while those that detract from this prediction are highlighted in red. We found that in many cases this analysis reproduces previously known patterns in food chemistry.