论文标题
从NMR光谱直接扣除化学类
Direct deduction of chemical class from NMR spectra
论文作者
论文摘要
本文提出了一种直接从NMR数据分类化合物的概念验证方法，而无需进行结构阐明。这可以帮助减少找到良好结构候选者的时间，因为在大多数情况下必须由人类工程师进行匹配，或者至少必须由一个人进行有意义的解释。因此，在NMR领域长期以来一直在积极寻求自动化。该方法适用于分类的方法是卷积神经网络（CNN）。在比较分析中，尚未发现其他方法，包括聚类和图像注册。结果表明，深度学习可以为化学信息学中的自动化问题提供解决方案。
This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. Therefore, for a long time automation in the area of NMR has been actively sought. The method identified as suitable for the classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to automation problems in cheminformatics.
