Over 50% of drugs fail in stage 3 clinical trails, many due to a poor understanding of the drug's mechanisms of action (MoA) . A better comprehension of drug MoA will significantly improve research and development (R&D).Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA's accuracy. We find that network topology predominantly determines the accuracy of ProTINA's predictions. We further show the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. Then we demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP (https://github.com/ImmuSystems-Lab/TREAP), that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous as it is not computationally demanding, provides easy to interpret results, and is often more accurate at predicting drug targets than current state of the art approaches.