Feature importance is a crucial aspect of ML models as it provides valuable insights into the contribution and relevance of different input features for making accurate predictions. The features selected during the model construction on which the predictions rely are 15 and include the molecular descriptors ATSC0c, ATSC0se, AATS0i, ATSC1p, AATSC2se, AATSC0m, AATSC1Z, AATSC2are, AATSC1pe, SpDiam_A, ATSC1c, ATSC1se, ATSC1Z, ATSC1m, and ATSC4s. The selected features are summarized in Fig. reporting the level of importance according to the SHAP values. The distributions of the 15 features for the bitter, sweet, other and umami samples are also represented in Supplementary Figure . In this connection, the correlation between the selected features holds great importance in understanding the underlying relationships and interactions within the taste prediction model.