The choice of a particular generative model depends on several aspects of the given task and materials. Considering data, the most common representation of a molecule is simplified molecular-input line-entry system (SMILES) notation , which comprises ASCII characters structured by a special language rule. This specification immediately suggests the possibility of building SMILES-based language models, in which a SMILES character can be treated as a single token, a sequence of one or more tokens as a (meaningful) text or phrase, and the whole text data set as a language corpus. Inspired by such natural language models as n-grams and recurrent neural networks , we are free to simulate our own language framework capable of capturing SMILES fragments and generating novel sequences.