In this paper, we provided a suite of methods for SMILESbased language modeling. The presented data processing and neural network modeling techniques are similar to those in natural language processing, and during evaluation, demonstrate the ability to generate a diverse set of novel molecules matching the properties of the reference dataset. In fact, these methods can also be adapted to other tasks such as target classification and transfer learning. We hope that this survey will be a useful guide in developing further SMILES-based molecule generation frameworks.