LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design
LipoAgent is a new framework designed to enhance the safety and efficiency of lipid nanoparticles for nucleic acid delivery. By integrating multi-agent coordination and toxicity considerations, it aims to improve the design process for lipids. The framework has shown a significant improvement in mRNA transfection efficiency predictions compared to existing models.
- ▪LipoAgent combines domain-specific fine-tuning with a conditional prediction objective to prioritize safety in lipid design.
- ▪The framework achieved an average 32% relative improvement in mRNA transfection efficiency prediction across multiple foundation models.
- ▪Wet-lab validation confirms that the virtual screening rankings from LipoAgent translate reliably to biological transfection outcomes.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25250 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
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Computer Science > Artificial Intelligence arXiv:2605.25250 (cs) [Submitted on 24 May 2026] Title:LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design Authors:Leshu Li, An Lu, Haiyu Wang, Zhibin Feng, Conghui Duan, Qing Bao, Zongmin Zhao, Sai Qian Zhang View a PDF of the paper titled LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design, by Leshu Li and 7 other authors View PDF HTML (experimental) 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.
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