Evidence map›Paper›PMID 38012793›Full record

ArticleJournal of cheminformatics2023

NMR shift prediction from small data quantities.

Herman Rull, Markus Fischer, Stefan Kuhn

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Computation ofJournal of molecular modeling · 2025
    Article
  5. Article
  6. Can Graph Machines Accurately EstimateMolecules (Basel, Switzerland) · 2024
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Herman RullDepartment of Computer Science, Tartu University, Narva mnt 18, Tartu, 51009, Tartumaa, Estonia.
Markus FischerInstitute for Medical Physics and Biophysics, Leipzig University, Härtelstr. 16-18, 04107, Leipzig, Sachsen, Germany.
Stefan KuhnDepartment of Computer Science, Tartu University, Narva mnt 18, Tartu, 51009, Tartumaa, Estonia. stefan.kuhn@ut.ee.

Funding

De Montfort University VC2020 new staff L SL 2020
6 · The paper itself

Abstract

Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting [Formula: see text] and [Formula: see text] NMR chemical shifts of small molecules in specific solvents.

Indexed as

Chemical shiftDataset sizeMachine learningNMRPrediction

Identifiers

PMID38012793
PMCPMC10683292

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.