Protein-protein/peptide　interactions　play　crucial　roles　in　various　biological　processes.　Exploring　their　interactions　attracts　wide　attention.　However,　accurately　predicting　their　binding　sites　remains　a　challenging　task.　Here,　we　develop　an　effective　model　GraphPBSP　based　on　Graph　Attention　Network　with　Convolutional　Neural　Network　and　Multilayer　Perceptron　for　protein-protein/peptide　binding　site　prediction,　which　utilizes　various　feature　types　derived　from　protein　sequence　and　structure　including　interface　residue　pairwise　propensity　developed　by　us　and　sequence　embeddings　obtained　from　a　new　pre-trained　model　ProstT5,　alongside　physicochemical　properties　and　structural　features.　To　our　best　knowledge,　ProstT5　sequence　embeddings　and　residue　pairwise　propensity　are　first　introduced　for　protein-protein/peptide　binding　site　prediction.　Additionally,　we　propose　a　spatial　neighbor-based　feature　statistic　method　for　effectively　considering　key　spatially　neighboring　information　that　significantly　improves　the　model＇s　prediction　ability.　For　model　training,　a　multi-scale　objective　function　is　constructed,　which　enhances　the　learning　capability　across　samples　of　the　same　or　different　classes.　On　multiple　protein-protein/peptide　binding　site　test　sets,　GraphPBSP　outperforms　the　currently　available　stateof-the-art　methods　with　an　excellent　performance.　Additionally,　its　performances　on　protein-DNA/RNA　binding　site　test　sets　also　demonstrate　its　good　generalization　ability.　In　conclusion,　GraphPBSP　is　a　promising　method,　which　can　offer　valuable　information　for　protein　engineering　and　drug　design.