Drug-drug　interactions　(DDI)　may　lead　to　unexpected　side　effects,　which　is　a　growing　concern　in　both　academia　and　industry.　Many　DDIs　have　been　reported,　but　the　underlying　mechanisms　are　not　well　understood.　Predicting　and　understanding　DDIs　can　help　researchers　to　improve　drug　safety　and　protect　patient　health.　Here,　we　introduce　DDI-GCN,　a　method　that　utilizes　graph　convolutional　networks　(GCN)　to　predict　DDIs　based　on　chemical　structures.　We　demonstrate　that　this　method　achieves　state-of-the-art　prediction　performance　on　the　independent　hold-out　set.　It　can　also　provide　visualization　of　structural　features　associated　with　DDIs,　which　can　help　us　to　study　the　underlying　mechanisms.　To　make　it　easy　and　accessible　to　use,　we　developed　a　web　server　for　DDI-GCN,　which　is　freely　available　at　http://wengzq-lab.cn/ddi/.