For future works, to improve the prediction accuracy of longer length molecules, as a longer length molecule (token length>10) can have molecular SMILES strings analogous to a sentence, the next-sentence prediction scheme of the BERT transformer could potentially be used to predict these molecules. However, since most incomplete reactions are missing side reactants and products which are smaller molecules, this would not significantly increase the reaction completion rate. Moreover, to minimise the false positive rate in the completed reactions, a calibration step could be added. Atom mapping confidence score would be an important index to understand the atom rearrangement in reactions. However, since there is not a ground truth atom mapping method, this index could only be used as recommendation for false positive detection. A more promising atom mapping tool is required to calibrate the model completed reactions. Eventually, to ultimately achieving reaction structure completion, a "golden dataset" is called for. This requires a more accurate NLP text-mining tool to reduce the noise when mining reactions from literature, and manual curation of at least a few reactions from each reaction subcategory.