ArticleMagnetic resonance (Gottingen, Germany)2023
DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra.
Article in Magnetic resonance (Gottingen, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
- Metabodeconplus-An R Package for Automated Deconvolution and Alignment of 1D NMR Metabolomics Data.Metabolites · 2026Article
- Metabolite Fraction Libraries for Quantitative NMR Metabolomics.Analytical chemistry · 2026Article
- DEEP Phaser: A Deep Learning Tandem Vision Transformer for Fully Automated NMR Phase Correction.The journal of physical chemistry letters · 2026Article
- 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
- ShimNet: A Neural Network for Postacquisition Improvement of NMR Spectra Distorted by Magnetic-Field Inhomogeneity.The journal of physical chemistry. B · 2025Article
- COLMAR1d2d: Synergistic Combination of 1D with 2D NMR for Enhanced High-Throughput Identification and Quantification of Metabolites in Complex Mixtures.Analytical chemistry · 2025Article
- COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields.Analytical chemistry · 2024Article
- Article
- Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings.Magnetic resonance (Gottingen, Germany) · 2024Article
- NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows.Metabolomics : Official journal of the Metabolomic Society · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.
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Registered trials
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.