MolMiner: You only look once for chemical structure recognition
Youjun Xu, Jinchuan Xiao, Chia-Han Chou, Jianhang Zhang, Jintao Zhu,, Qiwan Hu, Hemin Li, Ningsheng Han, Bingyu Liu, Shuaipeng Zhang, Jinyu Han,, Zhen Zhang, Shuhao Zhang, Weilin Zhang, Luhua Lai, Jianfeng Pei
TL;DR
MolMiner is a deep learning-based software that accurately recognizes and converts 2D printed chemical structures into machine-readable formats, addressing a long-standing challenge in optical chemical structure recognition.
Contribution
This work introduces MolMiner, a novel deep neural network approach for OCSR that outperforms existing rule-based systems on benchmark datasets.
Findings
Achieved state-of-the-art performance on four benchmark datasets.
Successfully applied to real-world scenarios with satisfactory results.
Provides free software for Mac and Windows platforms.
Abstract
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…
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Taxonomy
TopicsComputational Drug Discovery Methods · Machine Learning in Materials Science · Various Chemistry Research Topics