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Cheminformatics

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Patrick Jiang, Yue Guo, Jiawei Han

Featured June 30, 2026

Original Paper

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Simply

By giving large language models extra chemistry facts like similar molecules and literature, MolE-RAG helps them better guess how chemicals will behave, even without special training.

In depth
The paper introduces MolE-RAG, a training-free framework that significantly enhances large language models (LLMs) for molecular property prediction. It addresses the semantic gap between molecular representations (like SMILES) and natural language by augmenting LLM prompts with three complementary inference-time context sources: retrieved chemistry literature, molecule-specific context (e.g., functional groups, physicochemical descriptors), and structurally similar molecules. This approach allows LLMs to leverage external chemical knowledge without requiring additional fine-tuning.

Key Takeaways

  • 1
    The authors propose MolE-RAG, a novel training-free framework that augments LLM-based molecular property prediction with diverse inference-time chemical context.
  • 2
    The framework integrates three complementary context sources: textual retrieval from chemistry literature, molecule-specific context (identifiers, functional groups, RDKit descriptors), and structural retrieval of similar labeled molecules.
  • 3
    MolE-RAG substantially improves prediction performance for general-purpose LLMs, particularly smaller models, making them competitive with or superior to proprietary baselines and specialized graph neural networks on various molecular property tasks.

Conceptual Flow

HIGH LEVEL
1
Methodology: Augmenting LLMs with Chemical Context

The system takes a molecule, finds related information from three different sources, and gives it to a smart computer program to make a better guess about the molecule.

Molecule Input
Find Extra Info
Text Snippets
Molecule Details
Similar Molecules
Smart Program
2
Results: Boosting Prediction Accuracy

Adding these extra chemistry facts helps the smart computer program make much more accurate predictions about molecules, especially for smaller programs.

Smart Program Alone
Smart Program + Extra Info
Compare Guesses
Lower Accuracy
Higher Accuracy