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Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

Glioblastoma (GBM) is one of the most aggressive cancers, comprising 60–70% of all gliomas. The large G-protein-coupled receptor family includes cannabinoid receptors CB1, CB2, GPR55, and non-specific ion receptor protein transporters TRPs. First, we found up-regulated CNR1, GPR55, and TRPV1 expression in glioma patient-derived tissue samples and cell lines compared with non-malignant brain samples. CNR1 and GPR55 did not correlate with glioma grade, whereas TRPV1 negatively correlated with grade and positively correlated with longer overall survival. This suggests a tumour-suppressor role of TRPV1. With respect to markers of GBM stem cells, preferred targets of therapy, TRPV1 and GPR55, but not CNR1, strongly correlated with different sets of stemness gene markers: NOTCH, OLIG2, CD9, TRIM28, and TUFM and CD15, SOX2, OCT4, and ID1, respectively. This is in line with the higher expression of TRPV1 and GPR55 genes in GSCs compared with differentiated GBM cells. Second, in a panel of patient-derived GSCs, we found that CBG and CBD exhibited the highest cytotoxicity at a molar ratio of 3:1. We suggest that this mixture should be tested in experimental animals and clinical studies, in which currently used Δ9-tetrahydrocannabinol (THC) is replaced with efficient and non-psychoactive CBG in adjuvant standard-of-care therapy.

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