As outlined in the Methods section, the statistical analysis to reduce the number of employed molecular descriptors was performed on the training set with the Kruskal-Wallis test since not all features followed normal distribution when tested with the Shapiro-Wilk test . Moreover, the correction of pvalues for multiple testing to get q-values was applied using the Benjamini-Hochberg FDR adjustment method . By setting the q-value threshold to 0.05, we identified 1306 statistically significant differentiated features. The entire set of molecular features and 1306 statistically significant differentiated ones were also analysed using the Principal Component Analysis (PCA) and represented in reduced dimensional spaces according to the first three principal components (see also Supplementary Figure ). The lack of clear separation among tastes in the PCA plots underlined the complexity of the multi-taste prediction problem and highlighted the limitations of linear dimensionality reduction techniques in discriminating between different tastes. This limitation pushes us towards more sophisticated machine learning algorithms. Pairwise comparisons of each taste versus the rest were further conducted using the Mann-Whitney test. Supplementary Figure presents a heatmap of the top 5 features differentiating each taste from the rest in our training dataset.