ChemMLLM: Chemical Multimodal Large Language Model

ACL ARR 2025 May Submission2356 Authors

Abstract: Multimodal large language models (MLLMs) have made impressive progress in many applications in recent years. However, chemical MLLMs that can handle cross-modal understanding and generation remain underexplored. To fill this gap, in this paper, we propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation. Also, we design five multimodal tasks across text, molecular SMILES strings, and image, and curate the datasets. We benchmark ChemMLLM against a range of general leading MLLMs and Chemical LLMs on these tasks. Experimental results show that ChemMLLM achieves superior performance across all evaluated tasks. For example, in molecule image optimization task, ChemMLLM outperforms the best baseline (GPT-4o) by 118.9\% (4.27 vs 1.95 property improvement). The code is publicly available at https://anonymous.4open.science/r/ChemMLLM-0D98/.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: multimodality, cross-modal application, vision question answering

Contribution Types: Model analysis & interpretability, Data resources

Languages Studied: English

Submission Number: 2356