@inproceedings{pu-etal-2026-mcle,
    title = "{MCLE}-Mol: Empowering {LLM} with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction",
    author = "Pu, Zhili  and
      Zhang, Lantian  and
      Duan, Hao  and
      Zhang, Zhixing  and
      Zhu, Keyun  and
      Fan, Yongqi  and
      Hou, Ruihui  and
      Ruan, Tong  and
      Tang, Yun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.262/",
    pages = "5305--5333",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecular representations limits LLMs' ability to capture structure{--}activity relationships (SAR). Recent approaches have explored injecting structure-level information into LLMs, primarily modeling associations based on statistical regularities. However, these methods are prone to misinterpreting coincidental associations as general principles, imposing a bottleneck on predictive performance. To tackle the challenges above, we propose MCLE-Mol, an ML{--}LLM{--}Rule collaborative framework for MPP. It bridges the semantic gap by injecting ML-derived substructure attribution values into LLMs, utilizing Context-Calibrated Substructure Attribution Rules (CCSAR) to calibrate these attributions under specific chemical contexts to mitigate such misinterpretation. In addition, MCLE-Mol introduces a low-cost continual evolution strategy that updates CCSAR with frozen model parameters to adapt to dynamic chemical spaces. Experiments on multiple benchmark datasets demonstrate that MCLE-Mol outperforms all baselines, successfully resolving the trade-off between predictive performance and interpretability."
}