CLMs adapt algorithms developed for sequence processing to learn the 'chemical language', that is, how to generate molecules that are chemically valid (syntax ) and possess desired properties (semantics) . This is achieved by representing molecular structures as string notations, such as the Simplified Molecular Input Line Entry Systems (SMILES , Figure ), among others . These molecular strings are then used for model training and subsequent generation of molecules in textual form. Compared to generative methods based on molecular graphs , CLMs can learn more complex molecular properties better , and generate increasingly larger molecules more efficiently . These aspects have made CLMs become one of the de facto approaches for de novo drug design.