@inproceedings{li-etal-2026-lipoagent,
    title = "{L}ipo{A}gent: Coordinating Fine-Tuned {LLM} Agents for Safer Lipid Design",
    author = "Li, Leshu  and
      Lu, An  and
      Wang, Haiyu  and
      Feng, Zhibin  and
      Duan, Conghui  and
      Bao, Qing  and
      Zhao, Zongmin  and
      Zhang, Sai Qian",
    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-workshops/2026.findings-acl.1992/",
    pages = "40070--40081",
    ISBN = "979-8-89176-395-1",
    abstract = "Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and biologically safe remains a major bottleneck. In practical screening, toxicity is a decision-level constraint: if a lipid is toxic, its efficiency prediction is clinically irrelevant. We propose LipoAgent , a safety-aware multi-agent LLM framework for lipid discovery. LipoAgent combines domain-specific fine-tuning with a conditional prediction objective that enforces toxicity as a prerequisite for efficiency prediction, and further improves reliability via multi-agent verification with lightweight human oversight when disagreement persists. Across multiple foundation models, LipoAgent achieves an average 32{\%} relative improvement in mRNA transfection efficiency prediction compared with other reported models for lipid design. Wet-lab validation confirms that virtual screening rankings reliably translate to biological transfection outcomes. The code is publicly available at https://github.com/SAI-Lab-NYU/LipoAgent.git."
}