arXiv Machine Learning June 5, 2026 By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han

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

Read the original on arXiv Machine Learning →

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Multi-Granular Rationale-Guided Molecular LLM for Property Prediction

Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery. Molecular LLMs represent a molecule through several modalities, notably a 1D SMILES sequence or a 2D molecular graph.

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