---
id: 20260828-T0-08
title: "MolEmb：多模态大模型在分子嵌入任务上表现出色"
title_en: "MolEmb: Multimodal LLMs Prove Effective as Molecular Embedding Models"
url: https://ai.daily.yangsir.net/daily/20260828-T0-08
issue_date: 2026-08-28
publish_date: 2026-08-27T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2608.23646
---

# MolEmb：多模态大模型在分子嵌入任务上表现出色

arXiv 新论文提出 MolEmb，发现多模态大语言模型可以直接用作强大的分子嵌入模型。它突破了传统分子编码器需要专门训练的局限，为药物发现、性质预测和虚拟筛选等任务提供了新的基础设施方案。

## English Version

**MolEmb: Multimodal LLMs Prove Effective as Molecular Embedding Models**

A new arXiv paper introduces MolEmb, demonstrating that multimodal large language models can serve as powerful molecular embedding models. This approach bypasses traditional specialized training, offering a new foundation for drug discovery, property prediction, and virtual screening.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2608.23646)

**详情页**：https://ai.daily.yangsir.net/daily/20260828-T0-08

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