As mentioned previously, the unaligned LC-MS 2 -generated data are also suited for MEMO analysis to compare samples' MEMO vectors . The following example shows that MEMO-based visualizations and the ENPKG architecture are complementary ways to extract knowledge from such a dataset. By using UMAP to visualize the MEMO vector similarity of the 1,600 extracts screened against T. cruzi, we can observe, among the 8 active extracts against T. cruzi, a cluster of 6 samples (Figure A) . These 6 samples originate from 4 different species: Desmodium heterophyllum (Q10770714, Fabaceae), Chadsia grevei (Q15528494, Fabaceae), Pachyrhizus erosus (Q517283, Fabaceae), and Cnestis palala (Q15231964, Connaraceae). Following this observation, one can hypothesize that this clustering is due to structurally similar compounds in these extracts that could explain the similar bioactivity profiles. Using the following SPARQL query, it is possible to retrieve the 841 compounds annotated in at least one of these 6 selected extracts and quantify their occurrence both in the cluster of active extracts and among the whole set (Figure ). By visualizing these chemical annotations and their relationships in the form of a TMAP, it is possible to spot a cluster of compounds, mainly rotenoids derivatives, that are both shared among active extracts (high "count in-group") and specific to this group (high "group specificity") . This