@inproceedings{zhan-etal-2026-ligen,
    title = "{L}i{G}en: Active Lipid Generation via a Molecular Language Model",
    author = "Zhan, Ying  and
      Tang, Xiuqi  and
      Zhang, Yan  and
      Tan, Xiao  and
      Shen, Dian  and
      Yu, Zhou  and
      Wang, Beilun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.392/",
    pages = "8674--8686",
    ISBN = "979-8-89176-390-6",
    abstract = "Lipid nanoparticles (LNPs) can deliver cargos to both tumor and immune cells, playing a crucial role in biomedicine. Traditional approaches rely on experimental screening and expert knowledge, which can be costly and time-consuming. Recent methods based on language models have accelerated this process using deep learning. Although these methods can retrieve molecules for fusion or rank candidates from existing libraries, they are still limited by the scope of known formulations. In this work, we propose a method, LiGen, to generate lipid molecules efficiently and actively, facilitating the discovery of high-performing LNP formulations. We first train a lipid-specific molecular language model, LiCore, to learn hidden representations of lipid molecules. We then explore the learned latent space to generate improved candidate formulations. This process is guided by a trained predictor, which evaluates delivery efficiency and provides directional signals. In reconstruction tasks, LiCore achieves nearly perfect reconstruction output with a low invalid ratio on both the LNP-Virtual900k and LNP-Exp12k datasets. The predictor consistently improves ranking-oriented metrics across multiple cell lines, with our method outperforming the best baselines by an average of 4.1{\%}, 10.8{\%}, and 8.1{\%} in Top-50, Top-10, and Top-5 identification accuracy, respectively. Guided by the predictor, LiGen generates novel lipid candidates that achieve a 30.7{\%} improvement over baseline methods on average, with some samples exceeding 50{\%} improvement."
}