LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design
This work addresses the critical bottleneck of toxicity in lipid nanoparticle design for nucleic acid delivery, offering a practical framework that improves prediction reliability for drug delivery researchers.
LipoAgent introduces a safety-aware multi-agent LLM framework for lipid design that enforces toxicity as a prerequisite for efficiency prediction, achieving a 32% relative improvement in mRNA transfection efficiency prediction over existing models, with wet-lab validation confirming reliable translation of virtual screening rankings to biological outcomes.
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 finetuning 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.