Fig. 1

Strategy for molecule optimization using a language model. An initial population of molecules is used as input. The language model then generates mutations using predictions for randomly placed masks. Molecules are ranked according to a specified score and top performers are selected for another round of mutations. Two approaches for the language model are investigated, fixed and adaptive. For the fixed approach, the language model is pre-trained on a large molecule dataset and it does not change during the optimization process. For the adaptive approach, the language model is trained on the selected population, which itself changes during the optimization process