ArticleJournal of biomolecular NMR2021
FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.
Article in Journal of biomolecular NMR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 76 citations in OpenAlex.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
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- Two-dimensional NMR from a single pulse: Reconstructing heteronuclear 2D spectra via off-resonance decoupling and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Toward ultimate NMR resolution with deep learning.Science advances · 2026Article
- Deep Learning Assisted Proton Pure Shift NMR Spectroscopy.Journal of the American Chemical Society · 2026Article
- Fast acquisition of high resolution liquid NMR spectroscopy.Magnetic resonance letters · 2026Review
- Simulating the extraction of signal parameters and spectrograms for non-stationary NMR signals.Magnetic resonance (Gottingen, Germany) · 2026Article
- Decoherence Principles and Algorithms for One-Dimensional Nonuniform Sampling Schedules for Multidimensional NMR.Analytical chemistry · 2025Article
- NMR in the Age of Modern Biomedical Research and Drug Discovery.Journal of molecular biology · 2025Review
- Deep Learning for Hyperpolarized NMR of Intrinsically Disordered Proteins without Resolution Loss: Access to Short-Lived Intermediates.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Scalable deep learning reconstruction for accelerated multidimensional nuclear magnetic resonance spectroscopy of proteins.Science advances · 2025Article
- Super-resolution triple-resonance NMR spectroscopy for the sequential assignment of proteins.Science advances · 2025Article
- ShimNet: A Neural Network for Postacquisition Improvement of NMR Spectra Distorted by Magnetic-Field Inhomogeneity.The journal of physical chemistry. B · 2025Article
- Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment.Nature communications · 2025Article
- A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.Science advances · 2024Article
- Beyond traditional magnetic resonance processing with artificial intelligence.Communications chemistry · 2024Article
- Protein NMR assignment by isotope pattern recognition.Science advances · 2024Article
- Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks.Nature communications · 2024Article
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2 authors at 1 institution in 1 country.
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Abstract
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.