To catalyse the novel reactions, instead of designing new enzymes, which was believed to be uncertain and time-consuming, we focused on enzymatic promiscuity, which expands the specificity of the native enzymes to putative substrates. A deep learning transformer model translating the languages of reaction SMILES and enzyme amino acid sequences of enzymes was trained to learn from the reaction transformations and the protein structures of enzymes, and subsequently suggest on promiscuous enzymes to bind with the substrates in the predicted reactions. The proposed transformer model helped calibrate the feasibility of the predicted reactions and reduce the search scope for promiscuous enzymes in the pool. Eventually, 12,422 novel reactions were predicted, and promiscuous enzymes were suggested to increase the confidence to synthesise these reactions. The populated biological space was also visualised by t-SNE to understand reaction clustering.