LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design

ACL ARR 2026 January Submission7958 Authors

Keywords: Multi-Agent Systems, Drug Discovery, Safety-Aware Learning, Human-in-the-Loop

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.

Paper Type: Long

Research Area: Clinical and Biomedical Applications

Research Area Keywords: Clinical and Biomedical Applications, AI / LLM Agents

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 7958