As means for interpretability towards what structural information the proposed model has learned over the course of the study, in this section we aim to disentangle whether it has developed a preference for specific molecular motifs. In order to do so, we make use of the BRICS algorithm, as implemented in the RD-Kit software, and compute all available leaf fragments and associated model scores for each molecule present in the training set. Since the fragments contained an "attachment" atom type not seen during training, fragments were scored according to the average scores of the compounds they were substructures of in the training set. Additionally, to avoid biases related to uncommon motifs or unexplored areas of chemical space, only fragments appearing a minimum of 5 times in the training set were considered in this analysis. A small selection of the highest and lowest ranked fragments is presented in Figure . Among the worst ranked fragments we can observe undesirable groups such as phenols, free acids, ketones, thioureas, allyls, long alkyl chains, naphtyls, cumarines, Hantzsch esters, quaternary amines, sugar-like structures or highly-substituted rings. On the other hand, among the best-ranked groups we can find many commonlyused medicinal chemistry motifs such as pyrazine, pyrimidine, sulfones, imidazoles, oxadiazoles, phenyls, or bicyclic heteroaromatics. Qualitatively, this experiment suggest that the proposed score has learned patterns that are in line with groups present in existing drug-like molecules. The full set of fragments, the frequency of their occurrence, as well as their associated score is provided in the accompanying code repository to this work.