In this work we propose a novel approach to de novo design which crucially provides chemically accessible compounds. Our method utilizes a CRKG comprising reactions from USPTO. In a two-step approach, we first use a link prediction algorithm for predicting a reaction between pairs of reactants which have not yet been observed to react. Secondly, we apply a transformer model to predict the product for a given reactant pair. We have showed that we can obtain satisfying results for these tasks individually, on USPTO reaction data. To predict reactivity between pairs of reactants, we have applied the SEAL model for link prediction which significantly outperforms the heuristic baselines on the USPTO reaction data. Secondly, we have applied the Chemformer model for predicting the product given the reactant pairs, for which our results are aligned with the previously published results. Finally, we have demonstrated how the two parts can be combined and used for generating promising DRD2 modulators analogues using the reactants in the graph. The evaluation has been done on the predicted de novo compounds in terms of the QED score, reaction classes, molecular diversity and finally DRD2 activity. We have observed that the predicted compounds are mostly novel.