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

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.