Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule

Kwon, Youngchun; Lee, Dongseon; Choi, Yang‐Kyu; Kang, Seokho

doi:10.1038/s41598-021-00488-z

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…Several approaches have been made to develop suitable spectral similarity metrics for NMR. The challenge with chemical shifts has typically been solved by smoothing, [31] binning [32] or alignment, [33] or by a combination of these. These techniques have typically been developed for matching an entire spectrum against references.…”

Section: Similarity Metricsmentioning

confidence: 99%

Evaluation of NMR predictors for accuracy and ability to reveal trends in ¹H NMR spectra of fatty acids

Chhaganlal

Underhaug

Mjøs

2023

Magnetic Reson in Chemistry

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Four different nuclear magnetic resonance (NMR) predictors have been evaluated for their ability to predict 600-MHz 1 H spectra of free fatty acids and fatty acid methyl esters of 20 common fatty acids. The predictors were evaluated on two main criteria: (1) their accuracy in direct prediction of the spectra (absolute accuracy) and ( 2) the ability to reveal trends or predict the change that occurs in the spectra as a result of a change in the fatty acid carbon chain, or by esterification of the free fatty acids to methyl esters (relative accuracy). The absolute accuracy in chemical shift prediction for fatty acids was good, compared with previous reports on a broader range of compounds. All four predictors had median prediction errors for chemical shifts of the signals in fatty acid methyl esters well below 0.1 ppm and as low as 0.015 ppm for one of the predictors. However, all predictors also had outliers with errors far above the upper interquartile range. In general, they also fail to reproduce trends of diagnostic value that were observed in the experimental data or properly predict the result of a minor change in molecular structure. All four predictors depend on experimental data from different origins. This may be a limiting factor for the relative accuracy of the predictors.

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Section: Similarity Metricsmentioning

confidence: 99%

Evaluation of NMR predictors for accuracy and ability to reveal trends in ¹H NMR spectra of fatty acids

Chhaganlal

Underhaug

Mjøs

2023

Magnetic Reson in Chemistry

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“…Reference spectra for the molecules are computed using quantum chemistry, and are compared to the signature using the Wasserstein distance (Zhang et al, 2020 ). In another approach, 13 C NMR queries are matched directly to HOSE- and MPNN-predicted chemical shifts of candidate molecules using cosine similarity (Kwon et al, 2021 ). Finally, an ML-driven model was recently trained to recognize hundreds of substructures in 1D 13 C NMR data, which can then be used for automated structure elucidation.…”

Section: Matching: Leveraging Computational Characteristicsmentioning

confidence: 99%

Problems, principles and progress in computational annotation of NMR metabolomics data

Judge

Ebbels

2022

Metabolomics

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Background Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards. Aim of review This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions. Key scientific concepts of review We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

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“…Prior approaches to the automated analysis of NMR spectra employed machine learning models to analyze the spectra. The majority of current models analyze the spectrum of a pure sample of an unknown compound to determine its identity. − These models can be employed only on spectra that contain a single compound, but full workflows capable of analyzing multicomponent spectra are becoming more commonplace. − Although powerful for their trained tasks, these machine-learning models require a database containing spectra of each potential component and cannot identify novel compounds.…”

Section: Introductionmentioning

confidence: 99%

Deconvolution and Analysis of the ¹H NMR Spectra of Crude Reaction Mixtures

Venetos,

Elkin,

Delaney

et al. 2024

J. Chem. Inf. Model.

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Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C−H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

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Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule

Cited by 10 publications

References 16 publications

Evaluation of NMR predictors for accuracy and ability to reveal trends in ¹H NMR spectra of fatty acids

Evaluation of NMR predictors for accuracy and ability to reveal trends in ¹H NMR spectra of fatty acids

Problems, principles and progress in computational annotation of NMR metabolomics data

Deconvolution and Analysis of the ¹H NMR Spectra of Crude Reaction Mixtures

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Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule

Cited by 10 publications

References 16 publications

Evaluation of NMR predictors for accuracy and ability to reveal trends in 1H NMR spectra of fatty acids

Evaluation of NMR predictors for accuracy and ability to reveal trends in 1H NMR spectra of fatty acids

Problems, principles and progress in computational annotation of NMR metabolomics data

Deconvolution and Analysis of the 1H NMR Spectra of Crude Reaction Mixtures

Contact Info

Product

Resources

About

Evaluation of NMR predictors for accuracy and ability to reveal trends in ¹H NMR spectra of fatty acids

Evaluation of NMR predictors for accuracy and ability to reveal trends in ¹H NMR spectra of fatty acids

Deconvolution and Analysis of the ¹H NMR Spectra of Crude Reaction Mixtures