On the other hand, the RMSE values show that HCat-GNet delivers less accurate predictions for most folds, where only folds 6, and 8 show similar errors between methods. From the strip plot, it can be noticed that HCat-GNet generated higher errors, particularly on folds 2, 5, 6, and 9 where the error was more than 50% (meaning that the prediction of facial selectivity was incorrect). We hypothesize that the poor performance HCat-GNet is due to the requirement for larger quantities of data in deep learning methods to perform well compared to RFR and GBR. Although the performance is not as good as the handcrafted features, it is remarkable that despite the size of the database, HCat-GNet was able to accurately predict with significant accuracy some of the data points and obtain comparable results to the other approaches for certain folds, while maintaining the advantage of not requiring human input to generate high level features. This can overcome modelling problems in asymmetric catalysis, particularly when transition states are completely unknown, and the design of handcrafted features is more challenging.