As a further example of applicability, this platform can be used also for food screening, enabling the analysis of the chemical composition of desired foods. Therefore, we examined coffee and chocolate using their relative composition as reported in FooDB. The taste analysis was conducted directly from our platform in the Foods tab (), where we provided an interactive exploration of the FooDB and enabled the direct execution of the multi-taste prediction for each of the compounds present in the investigated foods. Regarding the screening of coffee, 130 compounds were predicted as bitter, 44 as sweet, 4 as umami and 14 as other tastes, thus pointing out that coffee is characterized by mainly bitter compounds. Chocolate also presented a predominance of bitter compounds, with a taste profile consisting of 96 bitter compounds, 33 sweet compounds, 4 umami compounds and 13 other taste compounds. Both food screenings seem in accordance with the primary taste perceptions for these foods, which are indeed bitter. However, it is crucial to mention that this model can only predict the taste of individual chemical compounds and lacks the capability to predict the overall taste of food, which is influenced by various factors such as the specific concentrations of its ingredients, the food processing techniques (e.g. baking, frying, dehydration, steam cooking, among others), visual aspect (colour, shape, dimension), touch, taste, and odour. This type of feature could be employed in future holistic models able to consider not only the chemical structures of food ingredients but also other crucial factors underlying the overall taste. umami, and 781 other -all with their SMILES representation.