Loecker, J.; Pujara, N.; Bryant, W.; Puniya, B. L.; Packrisamy, P.; Hamed, A.; Helikar, T.

Constraint-based metabolic modeling is a powerful way to study the mechanistic basis of cellular states and disease, but its effective use demands substantial computational expertise and careful coordination of multi-step analyses. We developed MechAInistic to lower this barrier and enable researchers to ask complex biological questions in natural language. Harnessing large language models, MechAInistic is a multi-agent system organized around an Architect-Reviewer pattern that transforms a natural-language question into an executable, model-grounded workflow and generates a structured report. The system supports a variety of tasks, including pathway comparison, perturbation analysis, drug-target exploration, and literature-grounded interpretation across paired metabolic model states. We tested MechAInistic on two drug-repurposing use cases. For Naive B cells from Rheumatoid Arthritis (RA) paired with healthy controls, the system quantified the metabolic rewiring driving disease, prioritized candidate reactions using topological hub filtering and robustness analysis, and surfaced Devimistat as a potential repurposing candidate acting through 2-oxoglutarate dehydrogenase in the TCA cycle. In a paired CD4+ Th17 cell study from Multiple Sclerosis (MS) and healthy controls, the same workflow identified NADP-dependent isocitrate dehydrogenase as the optimal single target and proposed ivosidenib as an FDA-approved repurposing candidate. Together, these results show that MechAInistic interfaces directly with mechanistic modeling and turns large language model reasoning into reproducible biological discovery. MechAInistic is accessible at https://mechainistic.dtih.org.