ArticleJournal of chemical theory and computation2024
Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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Who cites it
11 citing papers in PubMed.
- Resolving Chemically InequivalentJACS Au · 2026Article
- End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers.Chemical science · 2026Article
- Novel green synthesis of polyfunctionally substituted phthalazines promoted by visible light, DFT studies and molecular docking with antimicrobial and antibiofilm potency.Scientific reports · 2026Article
- The Evolving Landscape of NMR Structural Elucidation.Molecules (Basel, Switzerland) · 2026Review
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- Review
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Computation ofJournal of molecular modeling · 2025Article
- Equivariant learning leveraging geometric invariances in 3D molecular conformers for accurate prediction of quantum chemical properties.Scientific reports · 2025Article
- Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations.ArXiv · 2025Article
- On the Use of Strong Proton Donors as a Tool for Overcoming Line Broadening in NMR: A Comment.Magnetic resonance in chemistry : MRC · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict
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What OpenQuestion holds
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.