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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.
Motivation:
Molecular captions, patents, and medicinal-chemistry notes describe substructures and their relations in natural language, whereas computational models operate on formal representations such as SMILES. Bridging this semantic-structural gap is important for patent interpretation, structural relationship analysis, and controllable molecular editing, yet current large language models struggle to ground textual references to precise molecular components.
Results:
We propose RelAgent, a cooperative multi-agent framework for molecular relationship grounding. RelAgent decomposes the task into three interpretable stages: entity extraction, substructure localization, and ontology-guided relationship reasoning, and then uses verifier agents to rank structurally plausible candidates. This design supports fine-grained reasoning over molecular substructure and substantially improves performance on the MolGround benchmark. RelAgent achieves 81.4% entity-extraction F1, 56.0% exact-match localization F1, and 54.6% relationship F1 on an open-source LLaMA3.1-8B model, improving the REL F1 from 0.1% to 54.6% and exceeding the vanilla Gemini-3.1-Pro baseline in our experiments. These results indicate that agentic, structure-aware reasoning is a practical direction for interpretable molecular understanding in bioinformatics.
Availability And Implementation:
The source code for RelAgent is available at https://github.com/Anya-RB-Chen/RelAgent.
