arXiv Machine Learning 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.

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arXiv Machine Learning
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MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

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By Hyuntae Park, Sooyeon Kim, Jiwon Park, SangKeun Lee
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MolEmb is a lightweight framework that adapts multimodal large language models (MLLMs) to serve as general molecular embedding models. By aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, MolEmb produces embeddings conditioned on both a molecular profile and a natural‑language semantic context. The model performs competitively on molecular property prediction and enables cross‑modal molecule‑text retrieval, while the newly introduced MolCAR benchmark demonstrates that context‑aware molecular embedding is largely a data property of the supervision.

By Xinjian Zhao, Xiangru Jian, Yaoyao Xu, Xiaozhuang Song, Wei Pang, Lei Bai, Tianshu Yu