In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a deep neural network architecture inspired by WaveNet, for performing analyses on time domain NMR data. We first demonstrate the effectiveness of this architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra. It is shown that a single network is able to reconstruct a diverse range of 2D NUS spectra that have been obtained with arbitrary sampling schedules, with a range of sweep widths, and a variety of other acquisition parameters. The performance of the trained FID-Net in this case exceeds or matches existing methods currently used for the reconstruction of NUS NMR spectra. Secondly, we present a network based on the FID-Net architecture that can efficiently virtually decouple ¹³C_α-¹³C_β couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated. The ability for these DNNs to work effectively in a wide range of scenarios, without retraining, paves the way for their widespread usage in analysing NMR data.

近年来，深度神经网络 (DNN) 在分析和解释 NMR 数据方面的变革潜力已得到明确认可。然而，迄今为止，DNN 在 NMR 中的大多数应用要么难以超越现有方法，要么范围仅限于与网络训练数据非常相似的狭窄数据范围。这些限制阻碍了 DNN 在 NMR 中的广泛应用。为了解决这个问题，我们引入了 FID-Net，这是一种受 WaveNet 启发的深度神经网络架构，用于对时域 NMR 数据进行分析。我们首先证明了这种架构在重建非均匀采样 (NUS) 生物分子 NMR 光谱方面的有效性。结果表明，单个网络能够重建通过任意采样计划获得的各种 2D NUS 光谱，具有一系列扫描宽度和各种其他采集参数。在这种情况下，经过训练的 FID-Net 的性能超过或匹配目前用于重建 NUS NMR 光谱的现有方法。其次，我们提出了一个基于 FID-Net 架构的网络，可以有效地虚拟解耦^{单次分析中的 HNCA 蛋白质 NMR 光谱中的13} C _α - ¹³ C _β偶联，同时使甘氨酸残基未调制。这些 DNN 无需重新训练即可在各种场景中有效工作的能力为其在分析 NMR 数据中的广泛使用铺平了道路。