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New AI framework RIVER accelerates enzyme-reaction discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RIVER, a novel multi-agent framework designed for enzyme-reaction discovery and annotation. This system integrates large language models with specialized biochemical tools and external knowledge resources to assemble evidence from various sources, including reaction chemistry, protein sequences, and catalytic mechanisms. RIVER aims to improve the accuracy of identifying candidate enzymes for specific reactions and finding reactions catalyzed by given enzymes, outperforming existing methods on public benchmarks and literature-constructed tasks. AI

IMPACT This framework could accelerate biochemical research by improving the efficiency and accuracy of enzyme and reaction identification.

RANK_REASON The cluster contains a research paper detailing a new AI framework for a scientific discovery task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI framework RIVER accelerates enzyme-reaction discovery

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The cluster contains a research paper detailing a new AI framework for a scientific discovery task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yifan Chang, Yunze Li, Feifei Jiang, Juntao Wu, Xin Chen, Han Hu, Yucheng Wu, Wei Tang, Mingqian Ma, Wanli Ouyang, Kaipeng Zhang, Lei Bai, Hongting Tang, Feiran Li, Yuchen Ren ·

    RIVER: Evidence-guided multi-agent retrieval for enzyme--reaction discovery

    arXiv:2611.07757v1 Announce Type: new Abstract: Enzyme function annotation and the identification of candidate enzymes for a given reaction are essential steps in connecting genomic information to biochemical reaction networks. However, existing methods rely on sequence or struct…

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