@inproceedings{baker-etal-2025-molecular,
    title = "Molecular String Representation Preferences in Pretrained {LLM}s: A Comparative Study in Zero- {\&} Few-Shot Molecular Property Prediction",
    author = "Baker, George Arthur  and
      Sanz-Guerrero, Mario  and
      von der Wense, Katharina",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.56/",
    pages = "1071--1085",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) have demonstrated capabilities for natural language formulations of molecular property prediction tasks, but little is known about how performance depends on the representation of input molecules to the model; the status quo approach is to use SMILES strings, although alternative chemical notations convey molecular information differently, each with their own strengths and weaknesses. To learn more about molecular string representation preferences in LLMs, we compare the performance of four recent models{---}GPT-4o, Gemini 1.5 Pro, Llama 3.1 405b, and Mistral Large 2{---}on molecular property prediction tasks from the MoleculeNet benchmark across five different molecular string representations: SMILES, DeepSMILES, SELFIES, InChI, and IUPAC names. We find statistically significant zero- and few-shot preferences for InChI and IUPAC names, potentially due to representation granularity, favorable tokenization, and prevalence in pretraining corpora. This contradicts previous assumptions that molecules should be presented to LLMs as SMILES strings. When these preferences are taken advantage of, few-shot performance rivals or surpasses many previous conventional approaches to property prediction, with the advantage of explainable predictions through chain-of-thought reasoning not held by task-specific models."
}