Atom-Level Optical Chemical Structure Recognition with Limited Supervision
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929300172701696 |
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| author | Oldenhof, Martijn De Brouwer, Edward Arany, Adam Moreau, Yves |
| author_facet | Oldenhof, Martijn De Brouwer, Edward Arany, Adam Moreau, Yves |
| contents | Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01743 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Atom-Level Optical Chemical Structure Recognition with Limited Supervision Oldenhof, Martijn De Brouwer, Edward Arany, Adam Moreau, Yves Computer Vision and Pattern Recognition Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction. |
| title | Atom-Level Optical Chemical Structure Recognition with Limited Supervision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.01743 |