NMR signal processing, prediction and structure verification with Machine Learning techniques,Magnetic Resonance in Chemistry

当前位置： X-MOL 学术 › Magn. Reson. Chem. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

NMR signal processing, prediction and structure verification with Machine Learning techniques
Magnetic Resonance in Chemistry ( IF 1.9 ) Pub Date : 2020-01-27 , DOI: 10.1002/mrc.4989
Carlos Cobas ₁

Affiliation

Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power. These algorithms are successfully employed for classification, regression, clustering, or dimensionality reduction tasks of large data sets and have been intensively applied in different areas of NMR including metabonomics, clinical diagnosis, or relaxometry. In this article, we concentrate on the various applications of ML/DL in the areas of NMR signal processing and analysis of small molecules, including automatic structure verification and prediction of NMR observables in solution.

中文翻译：

使用机器学习技术进行 NMR 信号处理、预测和结构验证

机器学习 (ML) 方法几十年来一直存在于 NMR 领域，但在过去几年中经历了巨大的增长，特别是由于利用不断增加的数据量的深度学习 (DL) 技术的出现和可用的计算机电源。这些算法已成功用于大数据集的分类、回归、聚类或降维任务，并已在 NMR 的不同领域得到广泛应用，包括代谢组学、临床诊断或弛豫测量。在本文中，我们专注于 ML/DL 在 NMR 信号处理和小分子分析领域的各种应用，包括自动结构验证和溶液中 NMR 观测值的预测。

更新日期：2020-01-27

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11