In our previous work , we proposed a strategy to use pre-trained models to generate new molecular structures. Similar to pre-training, a given molecule, represented as a SMILES string, is tokenized and randomly masked. The model predictions are then sampled to generate a set of mutations to the original molecule as shown in Figure . Therefore, in combination with the scoring provided by the surrogate model, the language model can be used to iteratively generate new molecules to search chemical space for a given optimization task. Most notably, the MLM can generate molecules that are much larger in the number of atoms relative to the original set of molecules. This is particularly important when considering the rapidly increasing chemical space with molecular size.