Multi-Modal Large Language Model-Guided Discovery of Gut Microbiota-Targeting Plant Polysaccharides for Precision Intervention in Metabolic Dysfunction-Associated Steatotic Liver Disease

Abstract

The rising global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) demands innovative interventions that target its gut–liver pathophysiological axis. Plant polysaccharides have emerged as potent modulators of the gut microbiota, yet the vast chemical space and the complex multi-omics interactions render traditional discovery pipelines inefficient. This paper presents a system-level vision for multi-modal large language model (LLM)-guided discovery of gut microbiota-targeting plant polysaccharides as precision therapies for MASLD. We articulate a comprehensive architecture that integrates heterogeneous data streams—genomic, metabolomic, glycomic, clinical, and imaging data—into a unified multi-modal reasoning framework anchored by a generalist LLM. The system leverages cross-modal representation learning, knowledge graphs linking botanical sources to host–microbe metabolic networks, and in silico screening cascades to prioritize candidate polysaccharides. Beyond the computational engine, we examine the structural trade-offs between centralized and federated data governance models, the biases embedded in multi-modal training corpora that risk perpetuating inequitable interventions, and the sustainability costs of large-scale LLM training and inference. We discuss deployment pathways that span from cloud-based high-performance computing to edge inference within clinical and research settings, and we evaluate the robustness of such systems under data distribution shifts and adversarial perturbations. Policy dimensions are given sustained attention, particularly with respect to intellectual property of natural product sequences, regulatory approval of AI-discovered compounds, and international frameworks for equitable benefit sharing. The paper contends that responsible, system-scale design of multi-modal LLM architectures can not only accelerate the discovery of microbiota-directed polysaccharides but also recalibrate how AI is embedded in translational metabolic medicine, foregrounding fairness, environmental accountability, and resilient infrastructure. By grounding these arguments in concrete biomedical challenges, we offer a blueprint for the next generation of AI-augmented natural product discovery systems.