Peer Reviewed
Nuclear Magnetic Resonance and Artificial Intelligence

This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited.

nuclear magnetic resonance NMR artificial intelligence spectrum prediction metabolomics
NMR spectroscopy is indispensable for the identification and structural elucidation of small compounds (e.g., metabolites). The resonance frequencies of each spin (mainly 13C and 1H), also known as chemical shifts, together with their multiplicities, provide a unique fingerprint for different chemical environments within a molecule. By analyzing these shifts and peak profiles, analysts can deduce the connectivity, arrangement, and electronic environments of atoms in a molecule, making NMR an essential tool for determining the precise structure of organic and inorganic compounds. NMR spectroscopy also plays a crucial role in studying molecular dynamics and interactions. The technique can probe how molecules behave in different environments, offering insights into conformational changes, reaction mechanisms, and molecular interactions. This makes NMR invaluable not only for structural chemistry but also for understanding complex biological systems and metabolic pathways. In metabolomics, NMR is used to identify and quantify metabolites in biological samples, providing a comprehensive overview of metabolic processes [1]. By analyzing the NMR spectra of biofluids, tissues, or cells, researchers can gain insights into the metabolic state of an organism, detect biomarkers for diseases, and study the effects of drugs and other interventions [2].
Integrating artificial intelligence (AI) into NMR spectroscopy has been revolutionizing the field, enhancing the accuracy, efficiency, and scope of analyses. This review outlines this progress. Most techniques mentioned here can be classified as machine learning (ML), so we mostly use the terms AI and ML interchangeably. Deep learning (DL) is a subset of newer ML techniques. We do not delve into details of the definition of artificial intelligence, but comprise everything here which is helping to replace human expertise and input. The aim of this is to transform data handling and interpretation, enabling more complex and large-scale studies automatically. This review focuses on advancements in small molecule chemistry and the potential for future developments. Apart from the references given in the text, we point the reader to two recent special issues on the topic [3][4] and the reviews [5][6][7][8] for more materials and references. As part of this overview paper, we do not claim to cover the extensive literature exhaustively.

References

  1. Borges, R.M.; Ferreira, G.A.; Campos, M.M.; Teixeira, A.M.; Costa, F.D.N.; das Chagas, F.O.; Colonna, M. NMR as a tool for compound identification in mixtures. Phytochem. Anal. 2023, 34, 385–392.
  2. Wishart, D.S.; Cheng, L.L.; Copie, V.; Edison, A.S.; Eghbalnia, H.R.; Hoch, J.C.; Gouveia, G.J.; Pathmasiri, W.; Powers, R.; Schock, T.B.; et al. NMR and Metabolomics-A Roadmap for the Future. Metabolites 2022, 12, 678.
  3. Journal of Magnetic Resonance. Special Issue: Artificial Intelligence in NMR, EPR, and MRI; Elsevier: Amsterdam, The Netherlands, 2022; Available online: https://www.sciencedirect.com/special-issue/106L0B084H8 (accessed on 17 October 2024).
  4. Magnetic Resonance in Chemistry. Special Issue: Applications of Machine Learning and Artificial Intelligence in NMR; Wiley: Hoboken, NJ, USA, 2022; Volume 60.
  5. Lu, X.Y.; Wu, H.P.; Ma, H.; Li, H.; Li, J.; Liu, Y.T.; Pan, Z.Y.; Xie, Y.; Wang, L.; Ren, B.; et al. Deep Learning-Assisted Spectrum–Structure Correlation: State-of-the-Art and Perspectives. Anal. Chem. 2024, 96, 7959–7975.
  6. Shukla, V.K.; Heller, G.T.; Hansen, D.F. Biomolecular NMR spectroscopy in the era of artificial intelligence. Structure 2023, 31, 1360–1374.
  7. Karamanos, T.K.; Matthews, S. Biomolecular NMR in the AI-assisted structural biology era: Old tricks and new opportunities. Biochim. Biophys. Acta (BBA)-Proteins Proteom. 2024, 1872, 140949.
  8. Cortés, I.; Cuadrado, C.; Hernández Daranas, A.; Sarotti, A.M. Machine learning in computational NMR-aided structural elucidation. Front. Nat. Prod. 2023, 2, 1122426.
More
Related Content
The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making.
Keywords: machine learning in social sciences; nonlinear relationships; model interpretability; predictive analytics; imbalanced data handling
This entry provides a comprehensive overview of methods used in image matching. It starts by introducing area-based matching, outlining well-established techniques for determining correspondences. Then, it presents the concept of feature-based image matching, covering feature point detection and description issues, including both handcrafted and learning-based operators. Brief presentations of frequently used detectors and descriptors are included, followed by a presentation of descriptor matching and outlier rejection techniques. Finally, the entry provides a brief overview of relational matching.
Keywords: photogrammetry; computer vision; image matching; feature-based matching; area-based matching; relational matching; handcrafted operators; learning-based operators; outlier rejection
Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models are difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. The researchers of this study assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Find more at: https://scholar.google.com/citations?user=AwaT7tcAAAAJ&hl=en&oi=ao
Keywords: machine learning; medical prediction; bootstrap simulation; shapely additive explanations; SHAP
The Bioelectric Medicine Market is revolutionizing healthcare by integrating advanced technologies with medical science to treat chronic diseases and improve patient outcomes. This emerging market is experiencing robust growth due to the increasing prevalence of neurological and chronic disorders, coupled with advancements in medical technology. According to Kings Research, the global bioelectric medicine market is poised to expand significantly in the coming years, driven by innovations and a growing focus on non-invasive treatments.
Keywords: Bioelectric Medicine Market Size And Share Report, 2030
The synergy between Newcomb-Benford and Bayes' laws provides a universal framework for comprehending information, probability, conformality, and computational intelligence.
Keywords: Newcomb-Benford Law; harmt (harmonic unit of information); likelihood; Canonical PMF; Global-local duality; Bayesian Law; Secretary problem; Cross-ratio; Coding source; Conformability
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , ,
View Times: 408
Online Date: 18 Oct 2024
Video Production Service