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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 multi-agent framework, bridges the gap between natural language descriptions and molecular structures for improved bioinformatics. This system enhances molecular relationship grounding and interpretable understanding.
Area of Science:
- Bioinformatics
- Computational Chemistry
- Natural Language Processing
Background:
- Scientific literature and patents describe molecular substructures and relationships in natural language.
- Computational models use formal representations like SMILES, creating a semantic-structural gap.
- Current large language models struggle to link text to precise molecular components.
Purpose of the Study:
- To develop a framework for grounding molecular relationships described in text to their precise structural components.
- To improve patent interpretation, structural relationship analysis, and controllable molecular editing.
- To enhance interpretable molecular understanding in bioinformatics.
Main Methods:
- Proposed RelAgent, a cooperative multi-agent framework for molecular relationship grounding.
- Decomposed the task into entity extraction, substructure localization, and ontology-guided relationship reasoning.
- Utilized verifier agents to rank structurally plausible candidates and enable fine-grained reasoning.
Main Results:
- RelAgent achieved 81.4% entity-extraction F1, 56.0% exact-match localization F1, and 54.6% relationship F1 on a LLaMA3.1-8B model.
- Significantly improved relationship F1 from 0.1% to 54.6% on the MolGround benchmark.
- Exceeded the performance of the vanilla Gemini-3.1-Pro baseline.
Conclusions:
- Agentic, structure-aware reasoning is a practical approach for interpretable molecular understanding.
- RelAgent demonstrates substantial improvements in grounding molecular relationships.
- The framework offers a pathway for more precise computational analysis of chemical information.
