In the context of large-scale multiple hypothesis testing, the hypotheses often possess certain group structures based on additional information such as Gene Ontology in gene expression data and phenotypes in genome-wide association studies. It is hence desirable to incorporate such information when dealing with multiplicity problems to increase statistical power. In this article, we demonstrate the benefit of considering group structure by presenting a p-value weighting procedure which utilizes the relative importance of each group while controlling the false discovery rate under weak conditions. The procedure is easy to implement and shown to be more powerful than the classical Benjamini-Hochberg procedure in both theoretical and simulation studies. By estimating the proportion of true null hypotheses, the data-driven procedure controls the false discovery rate asymptotically. Our analysis on one breast cancer dataset confirms that the procedure performs favorably compared with the classical method.