Existing research efforts are exploring various ways to improve LLM performance on chemistryspecific tasks. Some groups have developed specialized models, like ChemLLM, which is trained on extensive chemical datasets to ensure it is proficient in a wide array of chemical tasks . This specialization helps ChemLLM perform well in chemical applications. Instruction tuning is another promising approach; models such as MolecularGPT pre-train models with Simplified Molecular Input Line Entry System (SMILES) strings connected to molecular properties to enhance few-shot learning on chemical properties, outperforming traditional models on certain tasks . Additionally, fine-tuned models have demonstrated success in converting unstructured chemical text into structured data for reaction databases, highlighting LLMs' potential to build organized and accessible chemical knowledge bases . Some studies have also assessed the performance of generalpurpose LLMs in chemistry-related programming tasks, such as generating code for chemical data analysis . Alternatively, custom models can be created from the same transformer architecture that powers LLMs but using molecular properties as the training data.