AIMC Topic: Magnetic Resonance Spectroscopy

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Advancing PFAS Detection through Machine Learning Prediction of F NMR Spectra.

Environmental science & technology
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants with diverse structures. To further advance the impact assessment and remediation technology for PFAS pollution, new approaches for identifying emerging PFAS are neces...

VirMolAnalyte: An AI-Driven Metabolite Annotation Tool.

Analytical chemistry
Metabolites play a crucial role in sustaining biological activities and are also a significant source of new drug development. Nuclear magnetic resonance (NMR) spectroscopy is one of the most important tools for identifying the structures of the meta...

A Deep Learning Model for Efficient Nontargeted Screening of New Psychoactive Substances with Benchtop Nuclear Magnetic Resonance Devices.

Analytical chemistry
Benchtop nuclear magnetic resonance (NMR) devices enable rapid on-site detection of new psychoactive substances (NPS) at customs or mobile checkpoints, addressing the urgent need for real-time screening in combating illicit drug trafficking. Benchtop...

Deriving three one dimensional NMR spectra from a single experiment through machine learning.

Nature communications
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for analyzing complex mixtures due to its ability to manage matrix complexity, provide detailed molecular insights, and preserve sample integrity. In metabolomics, NMR enables the ident...

Fast and Reliable NMR-Based Fragment Scoring for Drug Discovery.

Journal of the American Chemical Society
Fragment-Based Drug Discovery (FBDD) is a powerful strategy used in the development of new therapeutics. Molecular fragments are screened against a target protein, where interactions are typically characterized by a low affinity. Nuclear Magnetic Res...

Scalable deep learning reconstruction for accelerated multidimensional nuclear magnetic resonance spectroscopy of proteins.

Science advances
High-dimensional nuclear magnetic resonance (NMR) spectroscopy can assist in determining protein structure, but it requires time-consuming acquisition. Deep learning enables ultrafast reconstruction but is limited to spectra of up to three dimensions...

From NMR to AI: Fusing H and C Representations for Enhanced QSPR Modeling.

Journal of chemical information and modeling
The ability to predict log  directly from spectral patterns marks a conceptual shift in cheminformatics. In this work, we demonstrate that H and C NMR spectra, computationally generated from molecular structures and transformed into machine learning-...

Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.

Chemical reviews
Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NM...

Classification and quantification of sesame oil in edible oils and adulterated mixtures using H NMR spectroscopy combined with multivariate, machine learning, and deep learning models.

Food chemistry
Sesame oil is often adulterated with cheaper oils, necessitating accurate authentication and quantification methods. This study investigates the performance of AI-based models using H NMR spectral data for edible oil classification and sesame oil qua...

Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.

Journal of chemical information and modeling
Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at...