As our objective is to compare these three approaches (manual chemical-informed features, RDKit features and HCat-GNet) in performance and interpretability for ligand optimization, we compared the best performing models using the different descriptors, which are RFR using handcrafted features with the V2 electronic descriptor, and the GBR using RDKit features. The three methods have been trained and evaluated using the exact same sets of reactions, which means that the results obtained per test fold can be compared directly. We present the results of the regression task using the RMSE (%) and of the classification task using the accuracy per test fold in a bar plot, where the value of the bar represents the mean and the error bars the standard deviation of the population of metrics obtained in each of the 9 times that each test set was used. We also show a strip plot to visualize the distribution of errors generated by each method. These results are shown in Figure . Figure shows the comparison between the handcrafted features proposed herein and HCat-GNet. In general, the performance of HCat-GNet was the poorest of all the methods evaluated. However, the results per test fold show that HCat-GNet can attain similar results than those obtained by the other methods. In the case of Accuracy, HCat-GNet is less accurate in folds 2, 5, 9, while in the other folds the performance is undistinguishable or better than the novel approach.