The %topA in the dataset we are working with is somewhat imbalanced as we mostly see very high levels of enantioselectivity in the DAAA products. This leads to a bimodal distribution in the dataset. Generally, this would lead to a categorisation approach being favoured over regression. However, a longer-term aim of this ML workflow is de novo ligand design. Therefore, the regression approach was maintained here. However, we also present the results of the models on predicting the face of the addition for a more insightful study on the capability of these approaches to understand the reaction representation and predict stereoselectivity outcomes. This way, we also target the variable 'face of addition', which determines whether the addition is done on the 'top' side (%top > 50%), or not (%top < 50%). To analyse the selected descriptors, a correlation matrix was generated from the above bespoke features is generated (Figure ). This heatmap allows for visualisation of the relationship between features. Isolation of the %topA row (boxed in Figure ) highlights which descriptors are likely to be impactful in the ML process. For example, the correlation for %topA is strongly positive for UL and LR volumes of the catalyst and strongly negative for LL and UR volumes. These correlations are expected, as the volumes of these quadrants align with either the (S,S)-or (R,R)hand of the Trost ligands, which ultimately control whether the products of the DAAA are high or low %topA. However, other descriptors such as electronics and sterics of certain fragments and solvent effects did show strong correlation with the %topA outcome. The reactions were represented into four different versions (V1-V4), Table . Each version contained identical entries, except for the values of the electronics of the aryl unit 'A', which was defined by different methods. V1 contains values obtained from the DFT method developed by Ziegler, V2 contains values defined by the summed Hammett value method, V3 contains results by the averaged Hammett value method, and V4 contains values defined from the C NMR spectroscopic method. As the number of exemplars in the collated dataset was relatively small, it was decided to look at the dataset as a whole and not break it down into smaller subsets for analysis.