Random Research highlight: Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. We developed a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node: this way we just factor out the degree from the PageRank. We show that some of the best targets known have high scores in the new measure, so the method could be used to identify new target proteins in networks. PLoS ONE 8(1): e54204. doi:10.1371/journal.pone.0054204,2013.