A framework for automated structure elucidation from routine NMR spectra

Huang, Zhaorui; Chen, Michael S.; Woroch, Cristian P.; Markland, Thomas E.; Kanan, Matthew W.

doi:10.1039/d1sc04105c

Cited by 27 publications

(35 citation statements)

References 48 publications

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“…The model uses different pooling layers with the idea of optimizing feature and compound feature characterization separately. The method can also work with 1 H data (Huang, 2021 ).…”

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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“…The model uses different pooling layers with the idea of optimizing feature and compound feature characterization separately. The method can also work with 1 H data (Huang, 2021 ).…”

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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“…Matthew W. Kanan and his colleagues introduced a machine learning framework based on a convolutional neural network (CNN) to predict the structures of small (<11 nonhydrogen atoms) organic molecules using 1H and/or 13 C NMR spectra. However, their models were only able to elucidate structures with fewer than 11 heavy atoms and neglected the prior knowledge, which greatly limited their real-world applicability . Therefore, more powerful tools and methods are needed to solve the inverse problems of structure elucidation.…”

Section: Introductionmentioning

confidence: 99%

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on ¹³C NMR Spectra and Prior Knowledge

Yao¹,

Yang

Song³

et al. 2023

Anal. Chem.

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Structure elucidation of unknown compounds based on nuclear magnetic resonance (NMR) remains a challenging problem in both synthetic organic and natural product chemistry. Library matching has been an efficient method to assist structure elucidation. However, it is limited by the coverage of libraries. In addition, prior knowledge such as molecular fragments is neglected. To solve the problem, we propose a conditional molecular generation net (CMGNet) to allow input of multiple sources of information. CMGNet not only uses 13C NMR spectrum data as input but molecular formulas and fragments of molecules are also employed as input conditions. Our model applies large-scale pretraining for molecular understanding and fine-tuning on two NMR spectral data sets of different granularity levels to accommodate structure elucidation tasks. CMGNet generates structures based on 13C NMR data, molecular formula, and fragment information, with a recovery rate of 94.17% in the top 10 recommendations. In addition, the generative model performed well in the generation of various classes of compounds and in the structural revision task. CMGNet has a deep understanding of molecular connectivities from 13C NMR, molecular formula, and fragments, paving the way for a new paradigm of deep learning-assisted inverse problem-solving.

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“…Third, the deep learning methods have high expressive power and model capacity because of the depth efficiency, which can take full advantage of big data [55]. Due to these advantages, deep learningbased methods have achieved a state-of-the-art performance in numerous related fields of NMR spectroscopy [56,57], ranging from spectral reconstruction [58][59][60], denoising [61], peak picking [62,63], chemical shift prediction [64][65][66][67][68] and molecular recognition (the SMART method proposed by Zhang et al in 2017) [69] to molecule identification [70][71][72]. It has shown unprecedented capabilities in solving difficult problems in NMR spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

et al. 2022

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Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

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A framework for automated structure elucidation from routine NMR spectra

Abstract: A machine learning model and graph generator were able to accurately predict for the presence of nearly 1000 substructures and the connectivity of small organic molecules from experimental 1D NMR data.

Cited by 27 publications

References 48 publications

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

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

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on ¹³C NMR Spectra and Prior Knowledge

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

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A framework for automated structure elucidation from routine NMR spectra

Abstract: A machine learning model and graph generator were able to accurately predict for the presence of nearly 1000 substructures and the connectivity of small organic molecules from experimental 1D NMR data.

Cited by 27 publications

References 48 publications

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

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

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

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Product

Resources

About

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on ¹³C NMR Spectra and Prior Knowledge