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.

翻译：本文件从NMR数据中直接对化学化合物进行分类,不做结构说明的概念证明方法,有助于缩短寻找良好结构候选人的时间,因为在多数情况下,匹配必须由人类工程师进行,或者至少是一个匹配过程必须由一个人进行有意义的解释,因此,长期以来一直在积极寻求NMR领域的自动化,被确定适合分类的方法是神经神经网络(CNN),其他方法,包括集群和图像登记,在比较分析中被认为不适合这项任务,其结果显示深层次的学习可以为化学信息自动化问题提供解决办法。