ArticleNature communications2021
DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
- Advancing liquid scintillation spectra deconvolution for radiological characterization: comparative techniques for multi-isotope activity assessment.Analytical and bioanalytical chemistry · 2026Article
- Fast Diffusion-Based Metabolomics Profiling by 3D Pure Shift HSQC-DOSY NMR with COLMARvista.Analytical chemistry · 2026Article
- Harmonizing Peak Matching between Multidimensional NMR Spectra.Journal of chemical information and modeling · 2026Article
- MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra.Analytical chemistry · 2026Article
- High-quality pure shift NMR spectra by deep learning using multi-spectral input and joint loss functions.Nature communications · 2026Article
- Metabolite Fraction Libraries for Quantitative NMR Metabolomics.Analytical chemistry · 2026Article
- QuantitativeMolecules (Basel, Switzerland) · 2026Review
- Recent Trends in Metabolomics by NMR Spectroscopy.Angewandte Chemie (International ed. in English) · 2026Review
- DEEP Phaser: A Deep Learning Tandem Vision Transformer for Fully Automated NMR Phase Correction.The journal of physical chemistry letters · 2026Article
- 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
- Harmonizing Peak Matching Between Multidimensional NMR Spectra.bioRxiv : the preprint server for biology · 2026Article
- Fast acquisition of high resolution liquid NMR spectroscopy.Magnetic resonance letters · 2026Review
- ReShuffle-MS: Region-Guided Data Augmentation Improves Artificial Intelligence-Based Resistance Prediction inMicroorganisms · 2026Article
- Top-Down Scoring of Spectral Fitness by Image Analysis for Protein Structure Validation.Journal of chemical information and modeling · 2026Article
- Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.Angewandte Chemie (International ed. in English) · 2026Article
- Utilizing Similarity Measures to Map Chemical Reactivity.Angewandte Chemie (International ed. in English) · 2026Article
- Detection of intramolecular protein dynamics on nanosecond-to-microsecond timescales by nanoparticle-assisted NMR spin relaxation (NASR).Nature protocols · 2026Review
- Simulating the extraction of signal parameters and spectrograms for non-stationary NMR signals.Magnetic resonance (Gottingen, Germany) · 2026Article
- Metabolite Fraction Libraries for Quantitative NMR Metabolomics.bioRxiv : the preprint server for biology · 2025Article
- NMR data processing, visualization, analysis and structure calculation with NMRFx.Communications chemistry · 2025Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.
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