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bioRxiv · 10.64898/2026.06.10.731360

GALILEO: Embodied AI scientist for autonomous therapeutic discovery in dynamic membrane systems

Jiang, N.·Wei, R.·Xiao, H.·Yin, Z.·Wang, X.·Cui, T.·Shao, K.·Zhou, J.·Fan, J.·Torr, P.·Wu, Y.·Gao, Q.
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Abstract

Generalizable Agentic Laboratory Intelligence for Learning, Experimentation, and Optimization (GALILEO) closes the prediction-to-intervention gap in therapeutic peptide discovery. Unlike prior AI-scientist systems, GALILEO couples an Observation-Thought-Action-Summary (OTAS) reasoning loop with robotic peptide synthesis and multimodal phenotyping. Candidate peptides are retrieved from a clinically informed peptide prior (CPP) and locally edited through auditable operations. GALILEO autonomously prioritized LRRC8C and SLC25A1 branches and used wet-lab feedback to update target beliefs, sequence policies, assay choices, and mechanism hypotheses. Peptides generated under this framework blocked LRRC8C currents, perturbed osmolyte/redox homeostasis, and promoted tumor-dependent T-cell activation, while SLC25A1 peptides disrupted citrate-export metabolism, reduced extracellular acidosis, and enhanced CD8+ T-cell function. These results establish retrieval-and-editing-based physical learning as a route for auditable, experimentally grounded interventions and transferable membrane-blocker rules.

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Jiang, N., Wei, R., Xiao, H., Yin, Z., Wang, X., Cui, T., Shao, K., Zhou, J., Fan, J., Torr, P., Wu, Y., Gao, Q.. 2026-06-12. GALILEO: Embodied AI scientist for autonomous therapeutic discovery in dynamic membrane systems. https://doi.org/10.64898/2026.06.10.731360

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