The weighted graph construction problem can be solved as a Maximal Network (or fully connected graph, with each node connected to every other node). This approach provides a maximal number of edges N e (N e = N n * (N n -1)), which is a very inefficient approach for calculating a candidate ranking (see Figure ). To build a comprehensive candidate ranking, only a connected graph is necessary, and from a thermodynamic standpoint (with perfect calculations leading to exact ∆∆G bind ) no additional information is obtained by incorporating more edges into such a graph. Still, the Maximal Network approach is very important, acting as a precursor in network generation, with all subsequent network types pruning relevant edges to achieve their desired network layout. The generation of the Maximal Network is a time-critical step in the network planning phase, as all possible edges are generated and scored. Therefore, we implemented a parallelization scheme for this algorithm to speed up the calculations for larger numbers of molecules, which will affect all other network planning algorithms. However, for very large-scale approaches, this parallelization might not be sufficient.