优德细分:自动无噪声地从科学出版物中检索分子结构
Chong Zhou1, Wei Liu2, Xiyue Song1
1School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, People's Republic of China.
Journal of cheminformatics
|November 21, 2023
概括
我们开发了YoDe-Segmentation,这是一个深度学习模型,用于从科学文档中自动提取分子结构. 这种先进的光学化学结构识别 (OCSR) 方法实现了97.62%的提取效率,提高了用于计算分析的数据可访问性.
科学领域:
- 计算化学计算化学
- 化学信息学 化学信息学
- 科学文档分析科学文档分析
背景情况:
- 由于其非机器可读的格式,科学文献中的大量分子结构数据无法用于计算分析.
- 光学化学结构识别 (OCSR) 旨在将化学结构图像转换为机器可读的格式.
- 目前在OCSR中提取分子结构的方法缺乏令人满意的准确性.
研究的目的:
- 介绍YoDe-Segmentation,这是一个新的深度学习模型,用于从科学文档中自动检索分子结构.
- 提高提取分子结构用于计算分析的精度和效率.
- 解决OCSR中现有的基于规则和深度学习方法的局限性.
主要方法:
- YoDe-Segmentation采用一个三阶段的深度学习过程:检测,面具生成和计算.
- 检测阶段识别和分离分子结构.
- 面具地图被生成和完善,然后与孤立的结构相结合,以获得纯分子图像.
主要成果:
- 在严格的测试中,与替代算法相比,YoDe-Segmentation表现出更高的性能.
- 该模型在以化学为中心的期刊的文本上实现了97.62%的平均提取效率.
- 手动验证证实了自动提取过程的稳定性和可靠性.
结论:
- 优德细分显著增强了从科学文献中自动提取分子结构的功能.
- 该模型的高精度和效率使化学数据更容易获得计算研究.
- 这一进步对化学信息学和科学数据挖掘具有广泛的应用.
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