Year: 2025
Type: conference-paper
Abstract: Recent LLMs exhibit limited effectiveness on molecular property prediction task due to the semantic gap between molecular representations and natural language, as well as the lack of domain-specific knowledge.To address these challenges, we propose MolRAG, a Retrieval-Augmented Generation framework integrating Chain-of-Thought reasoning for molecular property prediction.MolRAG operates by retrieving structurally analogous molecules as contextual references to guide stepwise knowledge reasoning through chemical structureproperty relationships.This dual mechanism synergizes molecular similarity analysis with structured inference, while generating humaninterpretable rationales grounded in domain knowledge.Experimental results show Mol-RAG outperforms pre-trained LLMs on four datasets, and even matches supervised methods, achieving performance gains of 1.1%-45.7%over direct prediction approaches, demonstrating versatile effectiveness.Our code is available at https://github.com/AcaciaSin/MolRAG.
Authors: Ziting Xian, Jiawei Gu, Lingbo Li, Shangsong Liang
Institutions: Sun Yat-sen University, University of Warwick
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412945209