Department of Nanoengineering, University of California, San Diego, La Jolla, California, 92093, United States of America.
Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, 92093, United States of America.
Sci Rep. 2017 Oct 27;7(1):14243. doi: 10.1038/s41598-017-13923-x.
Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products.
已经探索了各种比较 2D NMR 光谱的算法，以评估它们对天然产物去重以及确定分子结构的能力。然而，光谱伪影、溶剂效应以及官能团（s）对化学位移的相互作用结合在一起，阻碍了它们的有效性。在这里，我们利用非均匀采样（NUS）2D NMR 技术和深度卷积神经网络（CNNs）创建了一个工具 SMART，它可以辅助天然产物的发现工作。首先，我们对异核单量子相干（HSQC）NMR 脉冲序列进行了改编，使其适用于最先进的核磁共振（NMR）仪器，并对数据重建方法进行了优化；其次，我们在一个包含超过 2054 个 HSQC 光谱的数据库上训练了一个带有对比损失的深度 CNN。为了展示 SMART 的实用性，我们在嵌入聚类空间中自动定位了几个新分离的化合物及其已知类似物，从而简化了新天然产物的发现流程。
