MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
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{M...
| Authors | Xinjian Zhao, Xiangru Jian, Yaoyao Xu et al. |
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| Primary category | cs.AI |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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