ChemMLLM: Chemical Multimodal Large Language Model

Recent years have seen rapid progress in multimodal large language models (MLLMs) in the field of chemistry. However, chemical MLLMs that can handle cross-modal understanding and generation remain underexplored. To fill this gap, we propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation. In this work, we design five types of multimodal tasks across text, molecular SMILES strings and images, and curate the datasets. We benchmark ChemMLLM against a range of general leading MLLMs, Chemical LLMs and specialized models on these tasks. Experimental results show that ChemMLLM achieves superior performance among general-purpose MLLMs and close performance to specialized models across all evaluated tasks. Our work extends the capabilities of chemical multimodal large language models to the realm of image generation, demonstrating the feasibility of unifying multiple cross-modal chemical tasks within a single foundation model and enabling more intuitive, visual human-AI interaction.