ReviewChemical reviews2025
Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.
Review in Chemical reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- The Effect of Homogeneous and Inhomogeneous Electric Fields on NMR Properties of Imidazole and Pyrrole Under.Magnetic resonance in chemistry : MRC · 2026Article
- Quantitative Prediction of Exchangeable Proton Chemical Shifts.Nature communications · 2026Article
- QuantitativeMolecules (Basel, Switzerland) · 2026Review
- Advances in Eco-Friendly Extraction of Fruit Bioactive Compounds: Technologies, Challenges and Future Directions.Analytical science advances · 2026Review
- Unraveling the coordination isomerism by ligand hyperfine NMR shifts.Chemical science · 2026Article
- From Covalent Systems to Bulk Phases: Addressing Structural Complexity with Computational NMR.Accounts of chemical research · 2026Article
- Reaction Products Revised and Mechanisms Revisited with Machine Learning-Augmented Computational NMR.The Journal of organic chemistry · 2026Article
- The Evolving Landscape of NMR Structural Elucidation.Molecules (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NMR, emphasizing their role in enhancing accuracy, efficiency, and scalability. QM methods provide precise predictions of NMR parameters, enabling detailed structural characterization of diverse systems. ML techniques, leveraging extensive data sets and advanced algorithms, complement QM by efficiently automating spectral assignments, predicting chemical shifts, and analyzing complex data. Together, these approaches have transformed NMR workflows, addressing challenges in metabolomics, protein structure determination, and drug discovery. This review highlights recent progress, emerging tools, and future directions in computational NMR, underscoring its critical role in modern structural science.
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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.