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We propose GraphBioisostere, a bioisostere prediction model that uses graph neural networks. The proposed model leverages a large-scale matched molecular pair dataset constructed from the ChEMBL database and directly learns bioisosterism without target information by considering entire chemical structures. Our evaluation shows that incorporating whole-molecule context improves bioisostere prediction compared to fragment\/substituent-only inputs. Compared with a strong fingerprint-based LightGBM baseline, GraphBioisostere achieves competitive prediction performance, with the best GNN variant approaching the baseline ROC-AUC. Additionally, models pre-trained on target-independent bioisostere prediction improved transfer learning performance for potency change prediction against specific targets, particularly in low-data settings. This suggests that GraphBioisostere acquires useful representations of the relationship between chemical structure and activity. 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