The algorithm developed in this work can be used for translating many other syntheticallydesigned CRNs into species-species networks, providing ample opportunities to extend the analysis beyond the clustering coefficient and betweenness centrality and apply it to a larger set of out-of-equilibrium networks. Most of the current measures are built on the occurrence of specific network patterns of specified sizes, while in systems chemistry, similar structures such as feedback loops may result from differently sized network patterns. Characterizing CRNs with more complex feedback loops will require network measures capable of including longer and more diverse cycles. Furthermore, it may require building them on more complex graph-like structures such as hypergraphs. Testing newly designed measures would require a broader training set of CRNs to assess their validity, underlining the need for developing such a database. We anticipate that our approach forms a foundation that may lead to training algorithms that can go beyond the synthesis of complex molecules and enable data-driven planning for the synthesis of complex out-of-equilibrium reaction networks with ever greater complexity. Fig. : Degree, clustering coefficient and betweenness centrality. An example for how network measures are determined for node i, where all edges have weights equal to 1. The degree (ki) measures the sum of the weights of the connections of each node. The clustering coefficient (cci) measures the likelihood that two neighbours of a node forming a wedge form a feedback triangle (i). A wedge, Wi, is a set of two edges h  i and i  j with h ≠ j at node i. A feedback triangle (i), therefore, is h  i  j  h. The betweenness centrality measures how frequent a node lies on the path with the least number of steps between other nodes. It is computed by counting the number of shortest paths that pass through node i. .