Polypharmacology, which focuses on designing drugs that bind efficiently to multiple targets, has emerged as a new strategic trend in today's drug discovery research. Many successful drugs achieve their effects via multi-target interactions. However, these targets are largely unknown for both marketed drugs and drugs in development. A better knowledge of a drug's mode of action could be of substantial value to future drug development, in particular for side effect prediction and drug repositioning. We propose a network-based computational method for drug target prediction, applicable on a genome-wide scale. Our approach relies on the analysis of gene expression following drug treatment in the context of a functional protein association network. By diffusing differential expression signals to neighboring or correlated nodes in the network, genes are prioritized as potential targets based on the transcriptional response of functionally related genes. Different diffusion strategies were evaluated on 235 publicly available gene expression datasets for treatment with bioactive molecules having a known target. AUC values of up to more than 90% demonstrate the effectiveness of our approach and indicate the predictive power of integrating experimental gene expression data with prior knowledge from protein association networks.