# RIVER

RIVER is a training-free multi-agent retrieval framework that organizes large language models, biochemical computational tools and external knowledge resources into traceable evidence chains for bidirectional enzyme-to-reaction and reaction-to-enzyme retrieval. In four experimentally supported enzyme–reaction cases drawn from an E. coli metabolomics study, it ranked among 667 candidate proteins with a mean reciprocal rank of 0.833 using Qwen3.6-35B-A3B and 0.750 using GPT-5.6 Luna, above the general-purpose coding-agent combinations and domain methods listed in the comparison table. On public benchmarks it reached Top-1 query success rates of 82.3% on Enzyme-405 and 72.8% on Orphan-335.

RIVER is a training-free multi-agent retrieval framework that organizes large language models, biochemical computational tools and external knowledge resources into traceable evidence chains for bidirectional enzyme-to-reaction and reaction-to-enzyme retrieval. In four experimentally supported enzyme–reaction cases drawn from an E. coli metabolomics study, it ranked among 667 candidate proteins with a mean reciprocal rank of 0.833 using Qwen3.6-35B-A3B and 0.750 using GPT-5.6 Luna, above the general-purpose coding-agent combinations and domain methods listed in the comparison table. On public benchmarks it reached Top-1 query success rates of 82.3% on Enzyme-405 and 72.8% on Orphan-335.