@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://aclanthology.org/2026.findings-acl.1992/",
doi = "10.18653/v1/2026.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."
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<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.</abstract>
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%0 Conference Proceedings
%T LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design
%A Li, Leshu
%A Lu, An
%A Wang, Haiyu
%A Feng, Zhibin
%A Duan, Conghui
%A Bao, Qing
%A Zhao, Zongmin
%A Zhang, Sai Qian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-lipoagent
%X 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.
%R 10.18653/v1/2026.findings-acl.1992
%U https://aclanthology.org/2026.findings-acl.1992/
%U https://doi.org/10.18653/v1/2026.findings-acl.1992
%P 40070-40081
Markdown (Informal)
[LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design](https://aclanthology.org/2026.findings-acl.1992/) (Li et al., Findings 2026)
ACL
- Leshu Li, An Lu, Haiyu Wang, Zhibin Feng, Conghui Duan, Qing Bao, Zongmin Zhao, and Sai Qian Zhang. 2026. LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design. In Findings of the Association for Computational Linguistics: ACL 2026, pages 40070–40081, San Diego, California, United States. Association for Computational Linguistics.