MolRAG: Unlocking the Power of Large Language Models for Molecular Property Prediction

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 .