← Back to all news
arXiv AI By Xinjian Zhao, Xiangru Jian, Yaoyao Xu, Xiaozhuang Song, Wei Pang, Lei Bai, Tianshu Yu

MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models

Read the original on arXiv AI →

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.

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.

Related stories

arXiv Machine Learning
Sep 29

ChemMLLM: Chemical Multimodal Large Language Model

ChemMLLM is a unified chemical multimodal large language model designed for molecule understanding and generation across text, SMILES strings, and images. The authors curated five multimodal tasks and benchmarked ChemMLLM against leading general MLLMs, chemical LLMs, and specialized models, finding it outperforms general-purpose MLLMs and matches specialized models on all tasks. The study demonstrates that a single foundation model can handle diverse cross‑modal chemical tasks, including image generation, enabling more intuitive visual human‑AI interaction.

By Qian Tan, Di Zhang, Ben Gao, Peng Xia, Wanhao Liu, Shufei Zhang, Wanli Ouyang, Lei Bai, Yuqiang Li, Tianfan Fu
llmsdiffusionmultimodalbenchmarks
More like this →
arXiv Machine Learning
Jun 5

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

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.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han
llmsragfine-tuning
More like this →