HiTEA reveals that the heterogeneous alkene subreactome (3 unique reactants) places high negative importance on zinc dust and for the HTE deprotection subreactome (9 unique reactants) a high positive importance on temperature (Figure &). Whilst temperature-correlated deprotections do agree with the literature's reactome , the negative correlation with zinc dust is a HiTEA-specific insight. This exemplifies the value of the negative results in the dataset which enables HiTEA to confirm negative correlations. The literature's reactome is often unable to confirm such correlations as it lacks publications with the negative data required. Interestingly, the other three subreactomes have no standout variable. In the case of the homogeneous hydrogenations (11 unique reactants), this can be explained by a strong dependence upon the reactants, but dearomatizations (5 unique reactants) show little overall dependence upon any variable, including molecule identity, perhaps due to the diverse and subtle changes that govern the energetically demanding process of dearomatization (Fig. ) . For these three subdatasets, the HiTEA's best/worst-in-reaction-type reveal more information (Fig. ). Overall, higher loadings of Pd/C are better than lower loadings, and Pearlman's catalyst is an allaround good catalyst for heterogeneous hydrogenations, two observations which are mirrored in the literature's reactome 51 . The pH of hydrogenolysis deprotections have been reported to have a marked effect in selectivity of the reaction, although acidic conditions are usually preferential . Dearomatizations, primarily performed on nitrogen-containing heterocycles in this dataset, are partial to acidic conditions . For the yield of asymmetric carbonyl reductions (6 unique reactants), ligand structure was a key factor, with very little importance placed on the base. In the literature, preferential treatment is given to rigid-backboned ligands, as it is hypothesized that flexible backbones deform the chiral pocket, leading to lower stereoselectivities . Indeed, even yield in this subreactome is completely dominated by ligand structure, with the top performing ligand featuring a rigid 6-member ruthenium metallocycle of a Josiphos ligand. The middling ligands contain either 7-or 8-member metallocycles. The poorest performer is (dppb)RuCl2AMPY boasts a significantly more flexible backbone from the rotational bonds between the P-P bridge (Fig. ) . Once again, ligand visualization reveals pockets of best-and worst-in-class ligand scaffolds, with clear distinctions between the best of the best-in-class ((R)-Josiphos SL J009-1 and Naud's catalyst Ru -see SI for all structures of acronyms) and the worst of the worst-in-class (p-cymene & dppb/AMPY), further supporting our chemical understanding of ligand design in metalmediated asymmetric carbonyl reductions. Gratifyingly, even amongst the very structurally similar C1-3## family of catalysts from Johnson Matthey, a noticeable delineation between the good and poor performers is visible (Fig. ). Finally, homogeneous alkene hydrogenations' best / worstin-class analysis confirms its variable importances conclusions: across the subreactome as a whole, the choice of catalyst is not statistically relevant in the determination of yield. This is no doubt a case of dataset bias as all of HiTEA's techniques failed to produce reasonable results: the random forest had low out of bag accuracy, the resulting random forest importances not including any catalysts, and the ANOVA deeming the catalysts as not statistically significant. A broader selection of alkene substrates and catalysts, or a subset of this dataset with less noise, would likely improve the utility of this subdataset.