Although the transformer model gives a specific amino acid sequence for a predicted enzyme, at this stage it is not suggested to directly evolve an enzyme based on the exact sequence. This is due to the model prediction uncertainty, which causes the distance of the predicted amino acid sequence from ground truth. To the best of our knowledge, this could only be possible with the expansion of synthetic biological reaction data to a much larger size, where a more comprehensive transformer could be trained to identify the intrinsic mechanisms between reactions and amino acid sequences by attention units of the transformer. However, by comparing the Levenshtein distance, this novel approach helps to: (1) calibrate the feasibility of predicted reactions, and (2) reduce the search scope for promiscuous enzymes in a pool, and therefore increase the efficiency of exploration of the biochemical reactions space.