Recent studies have shown that LLMs can indeed capture and apply principles of chemistry (as represented in their training corpus) to solve complex problems, going beyond mere pattern recognition. While we acknowledge that LLMs may not possess an understanding of chemistry similar to human experts, existing works suggest that they can potentially use chemical data to generate valid Simplified Molecular Input Line Entry System (SMILES) strings (which encodes molecular structures in text form), propose meaningful molecular modifications, and guide the discovery of compounds with desired properties. Thus, SMILES strings can be leveraged to explore LLMs' understanding of cheminformatics and chemistry design principles. LLMs have the additional benefit of being very flexible with the formatting of input data, meaning that some of the challenges associated with chemistry-specific generative models may be circumvented with LLMs.