In the AI-aided drug discovery (AIDD) field, data insufficiency often limits the ability of deep models. One effective solution is to apply the pretraining and fine-tuning paradigm, benefiting various downstream tasks from large-scale data. Focusing on the discriminative tasks (e.g. molecule classification), most large-scale molecular models are pretrained based on various node-level and graph-level tasks, including predicting masked node/edge attributes, recovering masked topological structures (e.g., motifs), and predicting molecular properties. To deal with generative tasks (e.g., molecule generation), Mimosa pretrained two GNN models for substructure-type prediction and molecular topology prediction. However, the above pretraining strategies are seldom considered by molecular retrosynthesis methods. To fill this blank, we provide a pretraining-assisted multi-task paradigm to suppress the data insufficiency issue in the retrosynthesis task.