The downstream results presented in Section 4 show that our proposed method can be used to generate a set of promising compounds. The predicted de novo compounds are diverse both in terms of scaffolds and Tanimoto similarity, has little overlap with the original graph, and includes drug-like compounds. In addition, there is a small proportion of the product which are also potentially active DRD2 modulators based on quantitative structure-activity relationship (QSAR) predictions. As there is no reason to except an enrichment in DRD2 modulators specifically, there is reason to anticipate that other potentially active compounds could be present in the predicted products. Considering that we were able to obtain promising results starting from randomly selected reactants in the CRKG, we suspect that this method could be used also for more targeted predictions. For example, the method could be used to find variants of a drug compound or for a specific drug target by handpicking the starting reactants. We also noted that amongst the compounds that were predicted as active DRD2 modulators, some reactants were overrepresented which further confirms that a careful selection of the starting reactant could increase the number of potentially active compounds obtained. However, the proposed method can be used to generate an overwhelming number of compounds, even though high cut-offs are used for each step. Finally, the generated product compounds will have to go through some filtering and screenings to find compounds with desired properties. The analysis performed in Section 4 can be viewed as an example on how this process can be performed.