LUMI-lab: A foundation model-driven autonomous platform enabling discovery of ionizable lipid designs for mRNA delivery
Summary
Integrating AI with robotics offers a promising approach to molecular discovery and optimization, enabling efficient exploration of vast chemical spaces. However, its application in emerging fields is often constrained by sparse historical data. Here, we introduce LUMI-lab, a self-driving platform that integrates a transformer-based foundation model with an active-learning experiment workflow to address the challenges of data scarcity. To demonstrate its potential, LUMI-lab autonomously sy
Content
# LUMI-lab: A foundation model-driven autonomous platform enabling discovery of ionizable lipid designs for mRNA delivery
*Published: 2026 Mar 19*
Integrating AI with robotics offers a promising approach to molecular discovery
and optimization, enabling efficient exploration of vast chemical spaces.
However, its application in emerging fields is often constrained by sparse
historical data. Here, we introduce LUMI-lab, a self-driving platform that
integrates a transformer-based foundation model with an active-learning
experiment workflow to address the challenges of data scarcity. To demonstrate
its potential, LUMI-lab autonomously synthesized and screened over 1,700 lipid
nanoparticles (LNPs), identifying ionizable lipids with enhanced mRNA
transfection potency in human bronchial cells. It discovered brominated lipid
tails as a feature that improves mRNA delivery. Intratracheal administration of
LNPs formulated with LUMI-6, the top-performing lipid, to mice achieved 20.3%
gene editing efficacy in lung epithelial cells. These findings demonstrate
LUMI-lab as a powerful, data-efficient platform for autonomous discovery and
optimization of molecules, highlighting the potential of AI-driven robotic
systems to advance next-generation RNA delivery technologies.
DOI: 10.1016/j.cell.2026.01.012