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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.16326 |
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| _version_ | 1866913973951004672 |
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| author | Tan, Qian Zhou, Dongzhan Xia, Peng Liu, Wanhao Ouyang, Wanli Bai, Lei Li, Yuqiang Fu, Tianfan |
| author_facet | Tan, Qian Zhou, Dongzhan Xia, Peng Liu, Wanhao Ouyang, Wanli Bai, Lei Li, Yuqiang Fu, Tianfan |
| contents | 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, 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 116.75\% (4.27 vs 1.97 property improvement). The code is publicly available at https://github.com/bbsbz/ChemMLLM.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16326 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChemMLLM: Chemical Multimodal Large Language Model Tan, Qian Zhou, Dongzhan Xia, Peng Liu, Wanhao Ouyang, Wanli Bai, Lei Li, Yuqiang Fu, Tianfan Machine Learning 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, 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 116.75\% (4.27 vs 1.97 property improvement). The code is publicly available at https://github.com/bbsbz/ChemMLLM.git. |
| title | ChemMLLM: Chemical Multimodal Large Language Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.16326 |