Automated molecular structure segmentation from documents using ChemSAM
Bowen Tang,
Zhangming Niu,
Xiaofeng Wang
et al.
Abstract:Chemical structure segmentation constitutes a pivotal task in cheminformatics, involving the extraction and abstraction of structural information of chemical compounds from text-based sources, including patents and scientific articles. This study introduces a deep learning approach to chemical structure segmentation, employing a Vision Transformer (ViT) to discern the structural patterns of chemical compounds from their graphical representations. The Chemistry-Segment Anything Model (ChemSAM) achieves state-of… Show more
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