An advantage of using language models pre-trained on chemical data is that they typically generalize better to unseen molecules which is particularly relevant when applying these models for the discovery of novel small molecule tastants. In the future, FART and methods like it may be used to perform in-silico screenings of large chemical spaces to discover novel tastants with desirable properties similar to modern drug discovery approaches . Future work should also focus on improving predictions for molecules with multiple associated tastes as well as making FART more sensitive toward stereochemistry. We appreciate that the use of synthetic chemicals to enhance or alter food flavor is not universally desirable. However, machine learning tools such as FART may also plausibly aid food scientists during the analysis of natural foodstuffs by shortlisting flavor-rich small molecules from a mixture of compounds. In this way, only a small number of promising compounds would need to be experimentally validated. Such an approach is highly dependent on the robustness of prediction and would motivate extending these predictions to proteins and other macromolecules as well.