To infer novel reactions in the monopartite CRKG, i.e., add links that are currently not present in the graph, can potentially lead to the generation of de novo compounds. This task can be performed with link prediction, i.e., predicting a link between two, currently unconnected, nodes in a graph . There are several link prediction algorithms available, and these can roughly be divided into heuristic and non-heuristic methods . The heuristic link prediction methods typically use a scoring function to determine the probability of a link. These scoring functions introduce assumptions of when a link is likely to occur based on the nodes' similarities. The common neighbor's method measures the likelihood of two nodes being connected based on the number of common neighbors they have . Social networks are typical examples of when the common neighbor's heuristic is applicable, as two people with many common connections are also more likely to be connected themselves. In other contexts, such assumptions might not hold, and other link prediction algorithms are then preferable. In addition, methods that are only based on graph topology and consequently ignore the node information, i.e., structure of molecule, might not be suitable in creating meaningful new reactions and consequently could generate many false positives. Here, we use the SEAL algorithm for link prediction, which has shown to outperform heuristics and other non-heuristic methods on several benchmark graphs , . SEAL is a binary classification model which can be trained to discriminate between a positive and a negative link. In short, the algorithm first extracts a h-hop enclosing subgraph around a link (x, y). Thus, this subgraph contains x and y and all the neighbors, at a maximum distance of h. A node labeling scheme is thereafter applied on the subgraph and finally the subgraph, together with optional node features, is passed through a graph neural network to obtain the probability of the link. For details of the algorithm, see . The positive class is comprised of the observed links in the graph while the negative class can be constructed from the unobserved links. Hereafter observed links are referred to as links or positive links and the unobserved links as negative links. The training process and evaluation of the link prediction algorithm on the CRKG are discussed in the Section 3.1.