If a reaction has atom surplus on the LHS for some atom types and atom surplus on the RHS for others, this means the reaction is missing both -at least one reactant and one product. With given candidate reagents and solvents, the reaction completion starting from adding species into the LHS becomes more certain and has higher priority than the RHS. Therefore, this case follows the same procedure as the reaction with atom surplus on the RHS. However, since atom surplus exists in both sides of the reaction, the possibility of adding only candidate reagents and solvents on the LHS to complete the reactions becomes very low. Therefore, help species are also allowed to be added to the LHS as well, but with a lower priority than the candidate reagents and solvents, since they are prone to create false positive complete reactions. context. For example, to fill the gap "In autumn, the ___ falls from the trees", MLM restricts search to the most possible item falling from trees in autumn, which would be the word "leaves". To train an MLM, a BERT transformer is fed with the same input sequence as the output, and the model optimises weights within its encoder layers in order to process this. To predict a missing word, tokens are randomly masked within the input sequence by replacing the missing word token with a special mask token symbol "<mask>". Given the context of a sentence, the MLM learned from previous semantic examples would infer the possible missing word. 14 Figure shows a proposed model structure for ChemMLM adapted from an MLM model. In Figure (a), reaction SMILES strings are processed into ChemMLM. A reaction SMILES is tokenised into multiple tokens and replaced by index IDs that have one-to-one correspondence with the tokens. The reaction SMILES tokenisation method is discussed in "ChemMLM tokeniser" Section of this paper. A mask function dynamically masks a section of reaction SMILES, where the mask method is discussed in the "Mask method" Section of this paper.