Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909533870227456 |
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| author | Chen, Yufan Leung, Ching Ting Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu |
| author_facet | Chen, Yufan Leung, Ching Ting Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu |
| contents | Artificial intelligence (AI) has demonstrated significant promise in advancing organic chemistry research; however, its effectiveness depends on the availability of high-quality chemical reaction data. Currently, most published chemical reactions are not available in machine-readable form, limiting the broader application of AI in this field. The extraction of published chemical reactions into structured databases still relies heavily on manual curation, and robust automatic parsing of chemical reaction images into machine-readable data remains a significant challenge. To address this, we introduce the Reaction Image Multimodal large language model (RxnIM), the first multimodal large language model specifically designed to parse chemical reaction images into machine-readable reaction data. RxnIM not only extracts key chemical components from reaction images but also interprets the textual content that describes reaction conditions. Together with specially designed large-scale dataset generation method to support model training, our approach achieves excellent performance, with an average F1 score of 88% on various benchmarks, surpassing literature methods by 5%. This represents a crucial step toward the automatic construction of large databases of machine-readable reaction data parsed from images in the chemistry literature, providing essential data resources for AI research in chemistry. The source code, model checkpoints, and datasets developed in this work are released under permissive licenses. An instance of the RxnIM web application can be accessed at https://huggingface.co/spaces/CYF200127/RxnIM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model Chen, Yufan Leung, Ching Ting Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu Computer Vision and Pattern Recognition Machine Learning Artificial intelligence (AI) has demonstrated significant promise in advancing organic chemistry research; however, its effectiveness depends on the availability of high-quality chemical reaction data. Currently, most published chemical reactions are not available in machine-readable form, limiting the broader application of AI in this field. The extraction of published chemical reactions into structured databases still relies heavily on manual curation, and robust automatic parsing of chemical reaction images into machine-readable data remains a significant challenge. To address this, we introduce the Reaction Image Multimodal large language model (RxnIM), the first multimodal large language model specifically designed to parse chemical reaction images into machine-readable reaction data. RxnIM not only extracts key chemical components from reaction images but also interprets the textual content that describes reaction conditions. Together with specially designed large-scale dataset generation method to support model training, our approach achieves excellent performance, with an average F1 score of 88% on various benchmarks, surpassing literature methods by 5%. This represents a crucial step toward the automatic construction of large databases of machine-readable reaction data parsed from images in the chemistry literature, providing essential data resources for AI research in chemistry. The source code, model checkpoints, and datasets developed in this work are released under permissive licenses. An instance of the RxnIM web application can be accessed at https://huggingface.co/spaces/CYF200127/RxnIM. |
| title | Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.08156 |