arXiv AI July 7, 2026 By Wenda Wang, Jinjia Feng, Zhewei Wei

Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation

arXiv:2607. 03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 3

MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding

arXiv:2607. 01982v1 Announce Type: cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery.

By Wenda Wang, Yihan Tong, Yuwei Hu, Zhewei Wei

llms multimodal safety

arXiv AI

Jun 3

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.

By Arun Raja, Garrett M. Morris, Kian Ming A. Chai

llms nlp benchmarks

arXiv Machine Learning

Jul 3

Probing Chemical Language Models: Effects of Pre-training and Fine-tuning

arXiv:2607. 02140v1 Announce Type: new Abstract: Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substructures they encode.

By Anna Karnysheva, Dietrich Klakow, Ji-Ung Lee

llms fine-tuning

arXiv Machine Learning

Aug 26

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

arXiv:2510.07289v2 Announce Type: replace Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...

By Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang

safety

arXiv Machine Learning

Jun 11

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

arXiv:2606. 11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds.

By Emily Nguyen, Yongchan Hong, Harsh Toshniwal, Yan Liu, Andreas Luttens

llms rag multimodal benchmarks safety

arXiv AI

Jul 16

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools

arXiv:2607. 13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence representations under-specify key graph-topological cues.

By Konstantinos Bougiatiotis, Dimitrios Kelesis, Georgios Paliouras

llms agents

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Related stories

MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding

Rethinking Molecular Text Representations for LLMs: An Empirical Study

Probing Chemical Language Models: Effects of Pre-training and Fine-tuning

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools