When comparing the handcrafted and the (medicinal chemistry derived) RDKit features in Figure , no major differences are found in the accuracy when prediction the face of addition. In the case of RMSE, in general, the novel features provide lower errors, where only folds 3, 6, and 10 show similarities between methods. Also, no major differences were found in the distribution of errors in the strip plot. The results obtained show that there is not much difference between using the handcrafted features and the RDKit features. However, the latter makes use of descriptors that, for the most part, lack of meaning to human chemists, while the handcrafted features are highly interpretable and meaningful. This small difference is of importance, as de novo design can be inspired from the interpretation that can be potentially applied to the model, which ultimately will explain the effect of each feature in the model's final prediction. To demonstrate this, we have used SHAP analysis to understand the impact of each variable to the model's final prediction to the RFR and GBR models using the RDKit descriptors and to the RFR using V2 handcrafted features. The results are shown in Figure .