In this work we propose a workflow to expand the knowledge of biochemical reactions, specifically based on data mining from KEGG database, and to build a new layer of information on our previous work of hybrid chemical and biological reaction network. This starts from prediction of novel metabolic reactions from a molecular graph-based method to learn from functional transformations of analogue reactions in the reaction dataset and to expand the original biological reaction network. To catalyse the novel predicted reactions, instead of designing new enzymes, which are case-specific, we use an NLP transformer to learn from enzyme language, amino acid sequences, with no segmentation on amino acid chain, and reaction language, reaction SMILES strings, and suggest promiscuous enzymes to bind with the substrates in the predicted reactions, and the suggestions are given with binding possibilities. The populated biological space would be analysed to investigate its feasibility of conducting the proposed bio-chemical transformations.