Chemistry datasets are crucial for training LLMs to understand and predict various molecular properties. This has enormous implications for drug discovery, material design, and many other applications, as LLMs can help identify promising molecules from vast chemical spaces with high accuracy and speed. With the increasing interest in LLMs for chemistry, there is a growing need for large, diverse, and highquality chemistry datasets that can provide sufficient chemical and structural information to effectively train these models. In , the author trained an efficient transformer encoder model, MoLFormer, on SMILES sequences of 1.1 billion unlabeled molecules and showed that utilizing the learned molecular representation outperforms existing baselines on downstream molecular property prediction tasks. In another, Monteiro et al. investigate ChatGPT's understanding of chemistry by presenting five simple tasks from different subareas and discusses the model's limitations.