These extracted reaction mechanisms were usually combined with multistep decision making algorithms to be extended to bio-catalytic retrosyntheis, such as RetroPath, RetroPath2.0, 14 RetroBioCat 7 and Koch et al.'s bioretrosynthesis method. Others used mechanism-free machine learning methods to learn from past reactions, such as GLORYx and Litsa et al.'s method. Jiang et al. learned from enzymatic reaction network connectivity, and predicted enzymatic reactions using link prediction. Several works also predicted backward metabolic reactions for bio-retrosynthesis, e.g., Koch et al. These methods are likely to generate metabolites and metabolic reactions which are unseen in prior literature, but with low confidence; besides, few of them provide information about the enzymes required. Therefore, we propose to learn from the literature-excerpted biological reactions and predict novel reactions to expand the biological reaction dataset. We calibrate the feasibility of the predicted reactions with a natual language processing (NLP) transformer to suggest promiscuous biocatalysts for the reactions. We hypothesise that by diversifying the biological reaction dataset, the potential for practical implementations of synthetic biology to produce molecules of interests would be increased.