ArticleJournal of biomolecular NMR2022
Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.
Article in Journal of biomolecular NMR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra.Analytical chemistry · 2026Article
- Toward ultimate NMR resolution with deep learning.Science advances · 2026Article
- The Evolving Landscape of NMR Structural Elucidation.Molecules (Basel, Switzerland) · 2026Review
- Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.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
- Machine learning ofScientific reports · 2025Article
- TIHI Toolkit: A Peak Finder and Analyzer for Spectroscopic Data.ACS omega · 2024Article
- Beyond traditional magnetic resonance processing with artificial intelligence.Communications chemistry · 2024Article
- Protein NMR assignment by isotope pattern recognition.Science advances · 2024Article
- Primary Structure of Glycans by NMR Spectroscopy.Chemical reviews · 2023Review
- NMR Metabolomics Methods for Investigating Disease.Analytical chemistry · 2023Review
- DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra.Magnetic resonance (Gottingen, Germany) · 2023Article
- NMR-based metabolomics: Where are we now and where are we going?Progress in nuclear magnetic resonance spectroscopyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.
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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.