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A pilot study for fragment identification using 2D NMR and deep learning
Magnetic Resonance in Chemistry ( IF 1.9 ) Pub Date : 2021-09-04 , DOI: 10.1002/mrc.5212
Stefan Kuhn 1, 2 , Eda Tumer , Simon Colreavy-Donnelly 1 , Ricardo Moreira Borges 3
Affiliation  

This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.

中文翻译:

使用 2D NMR 和深度学习进行碎片识别的初步研究

本文介绍了一种方法的概念证明,该方法使用定制的基于图像的卷积神经网络应用程序来识别混合物的 2D NMR 光谱中的子结构。这是通过单独和组合使用 HSQC 和 HMBC 光谱完成的。该应用程序可以使用简单的网络可靠地检测纯化合物中的子结构。结果表明,仅对纯化合物进行训练时,它可以用于混合物。HMBC 数据以及 HMBC 和 HSQC 的组合在该初步研究中显示出比单独使用 HSQC 更好的结果。
更新日期:2021-09-04
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