It is possible to expand the set of published interactions by using well-established target prediction methods. In this regard, we have used the SEA method (Similarity Ensemble Approach), which is based on testing the difference in the distribution of similarities of the query compound to the set of ligands of a target, when compared to the distribution of similarities to all the targets. This method has been successfully used in the past to, among others, identify the mechanism-of-action targets, predict bioactivities in large databases like ZINC, and identify bioactivities in inactive ingredients of drugs. In this regard, after applying this method to our dataset we were able to obtain a total of 8185 interactions, comprising 376 compounds and 1029 targets. Making the union of these predicted interactions with the published ones, we obtain a total of 9711 interactions (a bigger than four-fold increase); 7572 of these are strictly new interactions, which in turn split into 6343 interactions with human proteins and 2139 with bacterial targets. The merged set of published + predicted interactions now correspond to 426 compounds and 1227 proteins. Thus, although the percentage of compounds with at least one interaction remains in 6.4%, the number of targets has more than doubled and now we have 0.184 targets per metabolite, and 1.46 interactions per metabolite. In terms of biological origin, in the merged set the number of human vs bacterial proteins is 1047 vs 180, respectively (to be compared to the 451 vs 56 ones in the published data). Although the total number of compounds with at least one target annotation remains the same, the counts per biological group increase significantly as well, especially in the case of metabolites with bacterial targets: 422 vs 330, respectively for human and bacterial targets (they were 405 vs 128 metabolites, see above). Now the protein with the largest number of ligands (83) is peptidyl-glycine alpha-amidating monooxygenase, followed by aldehyde dehydrogenase 1A1 (the same 80 interactions as before), while the compound with the largest number of targets (137) is vanillic acid, a benzenoid, followed by luteolin (now with 107 interactions). Looking at Figure , after the incorporation of the predicted interactions, it can be seen a large boost in the number of unique human oxidoreductases (from 34 to 120), which now are the most populated target class, followed by 7TM1, that increases from 76 to 131 targets. Another class that has experimented a notable growth is human "Protease", which was before the 9 th most populated target class and now is the third one (from 51 to 99 proteins). In the case of bacterial proteins, the largest boost is in "Enzyme other" (from 4 to 33 proteins and now the most populated target class).