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RelAgent: a multi-agent solution for molecular relationship grounding
Rubing Chen1,2, Jiaxin Wu1,2, Chen Jason Zhang1,2
1Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR.
Bioinformatics (Oxford, England)
|July 23, 2026
Summary
RelAgent, a novel multi-agent framework, enhances molecular relationship grounding by bridging natural language descriptions with formal molecular structures. This approach significantly improves the accuracy of identifying molecular components and their relationships.
Area of Science:
- Bioinformatics
- Computational Chemistry
- Natural Language Processing
Background:
- Molecular information is often described in natural language (e.g., patents, medicinal chemistry notes) but processed computationally using formal representations like SMILES.
- Bridging the semantic-structural gap is crucial for patent interpretation, molecular relationship analysis, and controlled molecular editing.
- Existing large language models face challenges in accurately linking textual mentions to specific molecular entities.
Purpose of the Study:
- To develop a framework for grounding molecular relationships described in text to precise molecular structures.
- To improve the interpretability and accuracy of computational models in understanding molecular data.
- To address the limitations of current large language models in molecular entity recognition.
Main Methods:
- Propose RelAgent, a cooperative multi-agent framework designed for molecular relationship grounding.
- Decompose the task into three stages: entity extraction, substructure localization, and ontology-guided relationship reasoning.
- Employ verifier agents to rank structurally plausible candidates and ensure fine-grained reasoning over molecular substructures.
Main Results:
- RelAgent achieves significant improvements on the MolGround benchmark.
- Demonstrates an 81.4% entity-extraction F1, 56.0% exact-match localization F1, and 54.6% relationship F1 using an LLaMA3.1-8B model.
- Substantially improves REL F1 from 0.1% to 54.6%, outperforming the vanilla Gemini-3.1-Pro baseline.
Conclusions:
- Agentic, structure-aware reasoning represents a practical and effective approach for interpretable molecular understanding in bioinformatics.
- RelAgent offers a robust solution for accurately grounding molecular relationships, advancing computational approaches in chemistry and biology.
- The framework's interpretability and performance highlight its potential for various applications in molecular data analysis.
