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bioRxiv · 10.64898/2026.04.14.718256

PathwaySeeker: Evidence-Grounded AI Reasoning over Organism-Specific Metabolic Networks

Oliveira Monteiro, L. M.·Chowdhury, N. B.·Oostrom, M.·McDermott, J. E.·Stratton, K. G.·Choudhury, S.·Bardhan, J. P.
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Abstract

Metabolic activity is not an intrinsic property of an organism, but an emergent state shaped by environmental and experimental context. Despite recent advances in large language models (LLMs) and multi-omics profiling, current computational frameworks struggle to represent and reason over metabolism in a condition-specific manner. General-purpose AI systems operate on static, public biochemical knowledge, while multi-omics datasets capture dynamic measurements without a structured framework for mechanistic interpretation. As a result, metabolic networks remains analysis remains disconnected from the experimental states that define biological function. Here, we introduce PathwaySeeker, an evidence-grounded AI system for organism-specific metabolic network reasoning. PathwaySeeker reconstructs sample-specific metabolic graphs from integrated proteomic and metabolomic data, fine-tunes an LLM on the resulting graph structure, and verifies each reasoning step against the experimental graph through iterative hypothesis search, an approach we term Oracle-in-the-Loop inference. Every output claim carries explicit evidence provenance, distinguishing experimentally confirmed relationships from biochemically plausible hypotheses requiring validation. We demonstrate the system using multi-omics data from the non-model white-rot fungus Trametes versicolor, where PathwaySeeker recovers branched phenylpropanoid pathways and transparently stratifies confirmed reactions from testable extensions. Post-hoc thermodynamic analysis condition-specific metabolite dynamics support the biological feasibility of the reconstructed routes. By embedding experimental evidence provenance directly into language model-guided metabolic network reasoning, PathwaySeeker enables systematic differentiation between experimentally grounded knowledge and structured hypothesis, bridging frontier AI capabilities with organism-specific experimental evidence. Availability and ImplementationPathwaySeeker is available at https://github.com/pnnl/PathwaySeeker

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Oliveira Monteiro, L. M., Chowdhury, N. B., Oostrom, M., McDermott, J. E., Stratton, K. G., Choudhury, S., Bardhan, J. P.. 2026-04-17. PathwaySeeker: Evidence-Grounded AI Reasoning over Organism-Specific Metabolic Networks. https://doi.org/10.64898/2026.04.14.718256

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