双路径全球意识变压器用于光学化学结构识别
Rui Wang1, Yujin Ji2, Youyong Li1,2
1Macao Institute of Materials Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China.
The journal of physical chemistry letters
|December 8, 2025
概括
本研究介绍了双路径全球意识变压器 (DGAT) 以改进光学化学结构识别 (OCSR). DGAT提高了复杂化学结构的序列生成精度,实现了最先进的结果.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 化学信息学 化学信息学
背景情况:
- 光学化学结构识别 (OCSR) 将化学图形重建为机器可读的序列.
- 当前的多式联接融合方法与全球背景和复杂的动机作斗争,导致序列错误.
- 挑战包括准确地识别复杂的结构,如环和长链.
研究的目的:
- 为准确的OCSR序列生成开发一个先进的模型.
- 克服现有方法在捕捉全球背景和处理复杂化学图案方面的局限性.
- 为了提高生成序列的符号精度和化学精度.
主要方法:
- 提出了双路径全球意识转换器 (DGAT) 模型.
- 引入了一个级联的全球功能增强 (CGFE) 模块,以强调全球背景和弥合跨模式差距.
- 实施了稀疏差异全球-本地注意力 (SDGLA) 模块,以动态捕捉全球-本地特征差异.
主要成果:
- 在新建的评估数据集上,DGAT实现了最先进的 (SOTA) 性能.
- 取得的BLEU-4得分为0.840 (+5.3%),ROUGE-L得分为0.908 (+1.9%),平均Tanimoto相似度为0.988 (+1.2%) 与最好的公布模型相比.
- 在生成符号精确和化学精确序列方面表现出卓越的能力.
结论:
- 拟议的DGAT模型显著提高了OCSR能力.
- 在处理复杂的化学结构方面,DGAT有效地解决了以前方法的局限性.
- 该模型的性能证实了其从图形中精确提取化学信息的潜力.
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