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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.03777 |
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| _version_ | 1866916725833859072 |
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| author | Research, LG AI Chun, Sehyun Kim, Jiye Jo, Ahra Jo, Yeonsik Oh, Seungyul Lee, Seungjun Ryoo, Kwangrok Lee, Jongmin Kim, Seung Hwan Kang, Byung Jun Lee, Soonyoung Park, Jun Ha Moon, Chanwoo Ham, Jiwon Lee, Haein Han, Heejae Byun, Jaeseung Do, Soojong Ha, Minju Kim, Dongyun Bae, Kyunghoon Lim, Woohyung Lee, Edward Hwayoung Park, Yongmin Yu, Jeongsang Jo, Gerrard Jeongwon Hong, Yeonjung Yoo, Kyungjae Han, Sehui Lee, Jaewan Park, Changyoung Jeon, Kijeong Yi, Sihyuk |
| author_facet | Research, LG AI Chun, Sehyun Kim, Jiye Jo, Ahra Jo, Yeonsik Oh, Seungyul Lee, Seungjun Ryoo, Kwangrok Lee, Jongmin Kim, Seung Hwan Kang, Byung Jun Lee, Soonyoung Park, Jun Ha Moon, Chanwoo Ham, Jiwon Lee, Haein Han, Heejae Byun, Jaeseung Do, Soojong Ha, Minju Kim, Dongyun Bae, Kyunghoon Lim, Woohyung Lee, Edward Hwayoung Park, Yongmin Yu, Jeongsang Jo, Gerrard Jeongwon Hong, Yeonjung Yoo, Kyungjae Han, Sehui Lee, Jaewan Park, Changyoung Jeon, Kijeong Yi, Sihyuk |
| contents | The extraction of molecular structures and reaction data from scientific documents is challenging due to their varied, unstructured chemical formats and complex document layouts. To address this, we introduce MolMole, a vision-based deep learning framework that unifies molecule detection, reaction diagram parsing, and optical chemical structure recognition (OCSR) into a single pipeline for automating the extraction of chemical data directly from page-level documents. Recognizing the lack of a standard page-level benchmark and evaluation metric, we also present a testset of 550 pages annotated with molecule bounding boxes, reaction labels, and MOLfiles, along with a novel evaluation metric. Experimental results demonstrate that MolMole outperforms existing toolkits on both our benchmark and public datasets. The benchmark testset will be publicly available, and the MolMole toolkit will be accessible soon through an interactive demo on the LG AI Research website. For commercial inquiries, please contact us at \href{mailto:contact_ddu@lgresearch.ai}{contact\_ddu@lgresearch.ai}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MolMole: Molecule Mining from Scientific Literature Research, LG AI Chun, Sehyun Kim, Jiye Jo, Ahra Jo, Yeonsik Oh, Seungyul Lee, Seungjun Ryoo, Kwangrok Lee, Jongmin Kim, Seung Hwan Kang, Byung Jun Lee, Soonyoung Park, Jun Ha Moon, Chanwoo Ham, Jiwon Lee, Haein Han, Heejae Byun, Jaeseung Do, Soojong Ha, Minju Kim, Dongyun Bae, Kyunghoon Lim, Woohyung Lee, Edward Hwayoung Park, Yongmin Yu, Jeongsang Jo, Gerrard Jeongwon Hong, Yeonjung Yoo, Kyungjae Han, Sehui Lee, Jaewan Park, Changyoung Jeon, Kijeong Yi, Sihyuk Machine Learning The extraction of molecular structures and reaction data from scientific documents is challenging due to their varied, unstructured chemical formats and complex document layouts. To address this, we introduce MolMole, a vision-based deep learning framework that unifies molecule detection, reaction diagram parsing, and optical chemical structure recognition (OCSR) into a single pipeline for automating the extraction of chemical data directly from page-level documents. Recognizing the lack of a standard page-level benchmark and evaluation metric, we also present a testset of 550 pages annotated with molecule bounding boxes, reaction labels, and MOLfiles, along with a novel evaluation metric. Experimental results demonstrate that MolMole outperforms existing toolkits on both our benchmark and public datasets. The benchmark testset will be publicly available, and the MolMole toolkit will be accessible soon through an interactive demo on the LG AI Research website. For commercial inquiries, please contact us at \href{mailto:contact_ddu@lgresearch.ai}{contact\_ddu@lgresearch.ai}. |
| title | MolMole: Molecule Mining from Scientific Literature |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.03777 |