Title: Improving Protein Interaction Prediction using Pretrained Structure Embedding. Abstract: The prediction of protein-protein interactions (PPIs) is a critical problem because the knowledge of PPIs unravels the cellular behavior and its functionality. So far most previous works on PPI predictions mainly focused on sequence and network information and ignored the structural information of protein physical binding. We design a novel method, called xxx, which can leverage pretrained structure embedding and can be transferred to new ppi predictions. Experimental results on PPi predictions show that our pretrained structure embedding leads to significant improvement in PPI prediction comparing to sequence and network based methods. Furthermore, we show that embeddings pretrained based on ppi from different species can be transferred to improve the prediction for human proteins. 