Palladium-catalyzed decarboxylative asymmetric allylic alkylation (DAAA, Scheme 1) is a mild, but powerful tool for the synthesis of compounds possessing α-quaternary stereocentres. The DAAA reaction has been developed to give high levels of both yields and enantioselectivities using allyl enol carbonate and β-keto allyl ester substrates (Scheme 1A). While the substrate class and allyl substitution pattern has been extensively varied, the identity of the α-substituent (R in Scheme 1A) has been much less studied. Traditionally, this is a small alkyl group such as a methyl or ethyl, or a substituted methyl such as a benzyl group. In 2016 Guiry and co-workers expanded the scope of DAAA reactions to include bulky α-aryl cyclopentanones (Scheme 1B) with high yields and excellent enantioselectivities (>99% ee). These studies could be generalized to other α-aryl containing related substrates (Scheme 1C). Scheme 1. (A) Traditional α-alkyl DAAA reactions (2004-05). (B) Initial α-aryl DAAA reaction reported by Guiry. (C) Recent motifs accessible by α-aryl DAAA reactions. The catalyst for the DAAA comprises two parts: a Pd(0) source, commonly Pd2(dba)3, and a suitable chiral ligand. In the case of α-aryl containing substrates the optimized choice of ligand is typically a Trost-type ligand (e.g. Figure ). The high levels of enantioselectivity seen when using Trost-type ligands is attributed to the H-bonding capabilities of the amide proton. As the enolate approaches the Pd-π-allyl complex, the steric clash between the bulky aryl group and the backbone of the ligand causes the enolate to orient itself with the aryl group pointing away from the steric clash (Figure , pathway A). It has been observed that di-ortho aryl substitution and electrondonating groups on the aryl group leads to the highest levels of enantioselectivity. It is proposed that this di-ortho substitution pattern breaks the co-planarity of the enolate-aryl group, leading to an unstabilized enolate that reacts more selectively. It should be noted from that the outset that results in the literature result from the use of a range of (R, R)and (S,S)-Trost ligands and this needs to be accounted for when applying any stereochemistry defining (ML) mechanistic model. Ligand controlled asymmetric methodologies, such as the DAAA reaction, can be treated as pattern recognition problems. Machine learning (ML) is a useful tool for interpreting and solving such problems. ML has found applications in predicting the bioactivities of new drugs, optimizing reaction conditions, predicting chemical yields, and in the development of new synthetic routes to desired molecules. ML has also found notable success in the area of asymmetric catalysis. Furthermore, the first Graph Neural Network (GNN) capable of understanding relations between a reaction graph representation and stereoselectivity outcome has been recently reported by Aguilar et al. Despite the insights that ML can provide to the area of catalytic chemistry, its uptake by synthetic chemists has been low due, in part, to limited awareness/examples of ML in the field. Herein we compare simple, but effective, methodologies for supervised ML modelling of the DAAA reaction.