Evidence map›Paper›PMID 39593119›Full record

ArticleJournal of cheminformatics2024

GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts.

Haochen Chen, Tao Liang, Kai Tan, Anan Wu, Xin Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

5 authors.

Haochen ChenFujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, People's Republic of China.
Tao LiangFujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, People's Republic of China.
Kai TanFujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, People's Republic of China.
Anan WuFujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, People's Republic of China. ananwu@xmu.edu.cn.
Xin LuFujian Provincial Key Laboratory for Theoretical and Computational Chemistry, Departmental of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, People's Republic of China. xinlu@xmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, inspired by the graph transformer, we presented an improved protocol, termed GT-NMR, which integrates 2D molecular graph representation with Transformer architecture, for accurate yet efficient prediction of NMR chemical shifts. The effectiveness of the GT-NMR was thoroughly examined with the standard nmrshiftdb2 dataset, 37 natural products and structural elucidation of 11 pairs of natural products. Systematical analysis affirms that GT-NMR outperforms traditional graph-based methods in all aspects, achieving state-of-the-art performance, with the mean absolute error of 0.158 and 1.189 ppm in predicting

Indexed as

Graph neural networkGraph transformerMachine learningMolecular complexityNMR chemical shiftsTransformer

Identifiers

PMID39593119
PMCPMC11590296

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Registered trials

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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.