While t-SNE is primarily designed to optimize the embeddings of datapoints by minisming KL divergence between probability distributions, the KL divergence values provides insights into the convergence and quality of the resulting embeddings. It serves as a measure of how well the dimension reduction algorithm t-SNE converges with the two reaction fingerprints. In this case, t-SNE converges better with BERT FP to cluster the metabolic reactions space, where the t-SNE biological space is shown in Figure . That by ECFP, shown in Figure in ESI, resembling a 'ball' with points approximately equidistant from its nearest neighbours, indicates the failure of digitalisation of reactions into ECFP fingerprints. Figure shows most reactions, including curated and predicted reactions are well separated at different regions in the two-dimensional space, except EC1 and EC2 reactions are partially dispersed into other regions. This is likely due to the large coverages of the EC1 and EC2 reactions, which cover oxidation/reduction reactions, and reactions transferring functional groups respectively. These reactions have possible intersections with other reaction types. Moreover, most predicted reactions are located near the regions of their reaction clusters. This assures the feasibilities of predicted reactions.