Notably, when comparing the accuracy of these models using SOAP features derived from UFF-optimised geometries with RDkit versus those generated from DFT-relaxed structures (Table .8.1), we found no significant difference in performance. This underscores the robustness and efficiency of our approach, as it does not rely on time-consuming DFT calculations while maintaining high predictive accuracy. Building on the robust performance and efficiency of our model using SOAP features, we next sought to demonstrate the predictive power of the XGB classifier in a real-world setting. For this, we applied the classifier to predict the most active site for four completely new molecules to the model (Supplementary Sections 6.1 and 6.8). These predictions, made without the need of DFT calculations, were completed in a matter of seconds on a standard computer using the XGB classifier trained on the 30 known molecules, with SOAP features derived from SMILES strings. Experimental validation using 1 H NMR confirmed the accuracy of the model, which correctly identified the most active site for all four new substrates (Fig. .8.1). It is important to note, however, that while the model accurately predicts the most active site, it does not provide information on whether other sites in the molecule may also be active.