MolMiner: You only look once for chemical structure recognition
| Title: | MolMiner: You only look once for chemical structure recognition |
|---|---|
| Authors: | Xu, Youjun, Xiao, Jinchuan, Chou, Chia-Han, Zhang, Jianhang, Zhu, Jintao, Hu, Qiwan, Li, Hemin, Han, Ningsheng, Liu, Bingyu, Zhang, Shuaipeng, Han, Jinyu, Zhang, Zhen, Zhang, Shuhao, Zhang, Weilin, Lai, Luhua, Pei, Jianfeng |
| Publication Year: | 2022 |
| Collection: | Computer Science Quantitative Biology |
| Subject Terms: | Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods |
| More Details: | Molecular structures are always depicted as 2D printed form in scientific documents like journal papers and patents. However, these 2D depictions are not machine-readable. Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades follow a rule-based approach where the key step of vectorization of the depiction is based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software MolMiner, which is primarily built up using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with distance-based construction algorithm. We carefully evaluate our software on four benchmark datasets with the state-of-the-art performance. Various real application scenarios are also tested, yielding satisfactory outcomes. The free download links of Mac and Windows versions are available: Mac: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/mac/PharmaMind-mac-latest-setup.dmg and Windows: https://molminer-cdn.iipharma.cn/pharma-mind/artifact/latest/win/PharmaMind-win-latest-setup.exe Comment: 19 pages, 4 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2205.11016 |
| Accession Number: | edsarx.2205.11016 |
| Database: | arXiv |
| Description not available. |