DP5 without DFT: Uncertainty-calibrated graph neural net accelerates structure confirmation via NMR

Ruslan Kotlyarov; Alexander Howarth; Jonathan Goodman

doi:10.1039/d5sc06988b

January 1, 2026Chemical ScienceOpen Access

DP5 without DFT: Uncertainty-calibrated graph neural net accelerates structure confirmation via NMR

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Key Points

The study aims to improve the process of assigning candidate structures to NMR spectra by utilizing a novel method without DFT.
Evaluated the DP4 and DP5 methods for structure assignment.
Applied a graph neural network model to enhance uncertainty calibration.
Analyzed the efficacy of the model in confirming molecular structures based on NMR data.
The uncertainty-calibrated model outperformed traditional methods in structure confirmation speed.
Significant reduction in time required for accurate NMR spectrum assignments was observed.
Potential for broader applications in molecular identification was identified.

Implication

Analysis evaluates NMR spectra for structure assignment in molecular analysis, indicating time efficiency gains.

Abstract

The evaluation and assignment of candidate structures to NMR spectra can be facilitated by the DP4 method, which assumes that one of the candidate structures is correct, and the DP5...

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Cite This Study

Kotlyarov et al. (2026) studied this question.

synapsesocial.com/papers/69abc1955af8044f7a4ea6c1 https://doi.org/10.1039/d5sc06988b

View Full Paper

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DP5 without DFT: Uncertainty-calibrated graph neural net accelerates structure confirmation via NMR

Key Points

Implication

Abstract

Discussion

Cite This Study

DP5 without DFT: Uncertainty-calibrated graph neural net accelerates structure confirmation via NMR

Key Points

Implication

Abstract

Discussion

Cite This Study

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