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-the-art results on publicly available benchmark datasets and real-world tasks, underscoring its effectiveness in accurately segmenting chemical structures from text-based sources. Moreover, this deep learning-based approach obviates the need for handcrafted features and demonstrates robustness against variations in image quality and style. During the detection phase, a ViT-based encoder-decoder model is used to identify and locate chemical structure depictions on the input page. This model generates masks to ascertain whether each pixel belongs to a chemical structure, thereby offering a pixel-level classification and indicating the presence or absence of chemical structures at each position. Subsequently, the generated masks are clustered based on their connectivity, and each mask cluster is updated to encapsulate a single structure in the post-processing workflow. This two-step process facilitates the effective automatic extraction of chemical structure depictions from documents. By utilizing the deep learning approach described herein, it is demonstrated that effective performance on low-resolution and densely arranged molecular structural layouts in journal articles and patents is achievable.

中文翻译：

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使用 ChemSAM 从文档中自动进行分子结构分割

化学结构分割是化学信息学的一项关键任务，涉及从基于文本的来源（包括专利和科学文章）中提取和抽象化合物的结构信息。本研究引入了化学结构分割的深度学习方法，采用视觉变换器 (ViT) 从化合物的图形表示中辨别化合物的结构模式。Chemistry-Segment Anything Model (ChemSAM) 在公开的基准数据集和现实世界任务上取得了最先进的结果，强调了其从基于文本的来源准确分割化学结构的有效性。此外，这种基于深度学习的方法消除了对手工特征的需求，并表现出针对图像质量和风格变化的鲁棒性。在检测阶段，使用基于 ViT 的编码器-解码器模型来识别和定位输入页面上的化学结构描述。该模型生成掩模来确定每个像素是否属于化学结构，从而提供像素级分类并指示每个位置是否存在化学结构。随后，生成的掩模根据其连接性进行聚类，并且更新每个掩模簇以在后处理工作流程中封装单个结构。这个两步过程有助于从文档中有效地自动提取化学结构描述。通过利用本文描述的深度学习方法，事实证明，可以在期刊文章和专利中的低分辨率和密集排列的分子结构布局上实现有效的性能。