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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.15415 |
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| _version_ | 1866918029507428352 |
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| author | Fan, Siqi Xie, Yuguang Cai, Bowen Xie, Ailin Liu, Gaochao Qiao, Mu Xing, Jie Nie, Zaiqing |
| author_facet | Fan, Siqi Xie, Yuguang Cai, Bowen Xie, Ailin Liu, Gaochao Qiao, Mu Xing, Jie Nie, Zaiqing |
| contents | Understanding the chemical structure from a graphical representation of a molecule is a challenging image caption task that would greatly benefit molecule-centric scientific discovery. Variations in molecular images and caption subtasks pose a significant challenge in both image representation learning and task modeling. Yet, existing methods only focus on a specific caption task that translates a molecular image into its graph structure, i.e., OCSR. In this paper, we propose the Optical Chemical Structure Understanding (OCSU) task, which extends low-level recognition to multilevel understanding and aims to translate chemical structure diagrams into readable strings for both machine and chemist. To facilitate the development of OCSU technology, we explore both OCSR-based and OCSR-free paradigms. We propose DoubleCheck to enhance OCSR performance via attentive feature enhancement for local ambiguous atoms. It can be cascaded with existing SMILES-based molecule understanding methods to achieve OCSU. Meanwhile, Mol-VL is a vision-language model end-to-end optimized for OCSU. We also construct Vis-CheBI20, the first large-scale OCSU dataset. Through comprehensive experiments, we demonstrate the proposed approaches excel at providing chemist-readable caption for chemical structure diagrams, which provide solid baselines for further research. Our code, model, and data are open-sourced at https://github.com/PharMolix/OCSU. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15415 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OCSU: Optical Chemical Structure Understanding for Molecule-centric Scientific Discovery Fan, Siqi Xie, Yuguang Cai, Bowen Xie, Ailin Liu, Gaochao Qiao, Mu Xing, Jie Nie, Zaiqing Computer Vision and Pattern Recognition Understanding the chemical structure from a graphical representation of a molecule is a challenging image caption task that would greatly benefit molecule-centric scientific discovery. Variations in molecular images and caption subtasks pose a significant challenge in both image representation learning and task modeling. Yet, existing methods only focus on a specific caption task that translates a molecular image into its graph structure, i.e., OCSR. In this paper, we propose the Optical Chemical Structure Understanding (OCSU) task, which extends low-level recognition to multilevel understanding and aims to translate chemical structure diagrams into readable strings for both machine and chemist. To facilitate the development of OCSU technology, we explore both OCSR-based and OCSR-free paradigms. We propose DoubleCheck to enhance OCSR performance via attentive feature enhancement for local ambiguous atoms. It can be cascaded with existing SMILES-based molecule understanding methods to achieve OCSU. Meanwhile, Mol-VL is a vision-language model end-to-end optimized for OCSU. We also construct Vis-CheBI20, the first large-scale OCSU dataset. Through comprehensive experiments, we demonstrate the proposed approaches excel at providing chemist-readable caption for chemical structure diagrams, which provide solid baselines for further research. Our code, model, and data are open-sourced at https://github.com/PharMolix/OCSU. |
| title | OCSU: Optical Chemical Structure Understanding for Molecule-centric Scientific Discovery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.15415 |