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

2026-08-24

AI Summary

研究通过MolEmb框架利用多模态大语言模型生成条件分子嵌入，解决了分子表示缺乏自然语言接口的问题，支持属性预测和跨模态检索。

Abstract

Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property prediction, virtual screening, and retrieval. Most molecular encoders are specialist models built around a single molecular view, producing unconditional vectors with no language interface for varying the representation. We ask whether multimodal large language models (MLLMs), which natively process images, text, and symbolic inputs, can instead serve as \emph{general molecular embedding models} that produce embeddings conditioned on both a molecular profile and a natural-language semantic context. We introduce \textbf{MolEmb}, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective. The resulting embedding model is competitive on molecular property prediction and supports cross-modal molecule--text retrieval in the same space. We further introduce \textbf{MolCAR}, a diagnostic benchmark for context-aware retrieval, and find that context-aware molecular embedding is primarily a data property of the supervision. These results suggest that MLLMs are not merely chemistry assistants or generators, but a viable and extensible route to general molecular embedding models.

Problem

• multimodal large language models
• molecular embedding
• context-aware

Innovation

• Multimodal Large Language Models
• Molecular Embeddings
• Context-Aware Retrieval
• Bidirectional Contrastive Objective