scholarly journals NOAH: NMR Supersequences for Small Molecule Analysis and Structure Elucidation

2017 ◽  
Vol 56 (39) ◽  
pp. 11779-11783 ◽  
Author(s):  
Ēriks Kupče ◽  
Tim D. W. Claridge
Keyword(s):  
Small Molecule ◽  
Structure Elucidation ◽  
Small Molecule Analysis

scholarly journals NOAH: NMR Supersequences for Small Molecule Analysis and Structure Elucidation

2017 ◽  
Vol 129 (39) ◽  
pp. 11941-11945 ◽  
Author(s):  
Ēriks Kupče ◽  
Tim D. W. Claridge
Keyword(s):  
Small Molecule ◽  
Structure Elucidation ◽  
Small Molecule Analysis

scholarly journals The METLIN small molecule dataset for machine learning-based retention time prediction

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier Domingo-Almenara ◽  
Carlos Guijas ◽  
Elizabeth Billings ◽  
J. Rafael Montenegro-Burke ◽  
Winnie Uritboonthai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecule ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Molecular Identity ◽  
Time Prediction ◽  
Wide Range ◽  
Small Molecule Analysis ◽  
Deep Learning Model

AbstractMachine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

Optimization of a New Aerodynamic Cylindrical FAIMS Device for Small Molecule Analysis

2017 ◽  
Vol 28 (3) ◽  
pp. 525-538 ◽  
Author(s):  
Randy W. Purves ◽  
Satendra Prasad ◽  
Michael Belford ◽  
Albert Vandenberg ◽  
Jean-Jacques Dunyach
Keyword(s):  
Small Molecule ◽  
Small Molecule Analysis

N-doped graphene quantum dots as a novel highly-efficient matrix for the analysis of perfluoroalkyl sulfonates and other small molecules by MALDI-TOF MS

2017 ◽  
Vol 9 (13) ◽  
pp. 2014-2020 ◽  
Author(s):  
Ziyu Rao ◽  
Fanglan Geng ◽  
Yiqi Zhou ◽  
Dong Cao ◽  
Yuehui Kang
Keyword(s):  
Quantum Dots ◽  
Small Molecules ◽  
Small Molecule ◽  
Graphene Quantum Dots ◽  
Maldi Tof Ms ◽  
Maldi Matrix ◽  
Maldi Tof ◽  
Doped Graphene ◽  
Small Molecule Analysis ◽  
Tof Ms

N-doped graphene quantum dots (N-GQDs) were sythesized by a facile method and applied as a MALDI matrix for small-molecule analysis.

Current and future deep learning algorithms for MS/MS‐based small molecule structure elucidation

2021 ◽  
Author(s):  
Youzhong Liu ◽  
Thomas De Vijlder ◽  
Wout Bittremieux ◽  
Kris Laukens ◽  
Wouter Heyndrickx
Keyword(s):  
Deep Learning ◽  
Small Molecule ◽  
Structure Elucidation ◽  
Learning Algorithms ◽  
Molecule Structure

scholarly journals Recent Developments Toward Integrated Metabolomics Technologies (UHPLC-MS-SPE-NMR and MicroED) for Higher-Throughput Confident Metabolite Identifications

2021 ◽  
Vol 8 ◽  
Author(s):  
Rajarshi Ghosh ◽  
Guanhong Bu ◽  
Brent L. Nannenga ◽  
Lloyd W. Sumner
Keyword(s):  
Small Molecule ◽  
Structure Elucidation ◽  
Large Scale ◽  
Metabolite Identification ◽  
Spectral Features ◽  
Instrumental Ensemble ◽  
Analytical Instruments ◽  
Methods Development ◽  
Structure Determinations ◽  
Recent Developments

Metabolomics has emerged as a powerful discipline to study complex biological systems from a small molecule perspective. The success of metabolomics hinges upon reliable annotations of spectral features obtained from MS and/or NMR. In spite of tremendous progress with regards to analytical instrumentation and computational tools, < 20% of spectral features are confidently identified in most untargeted metabolomics experiments. This article explores the integration of multiple analytical instruments such as UHPLC-MS/MS-SPE-NMR and the cryo-EM method MicroED to achieve large-scale and confident metabolite identifications in a higher-throughput manner. UHPLC-MS/MS-SPE allows for the simultaneous automated purification of metabolites followed by offline structure elucidation and structure validation by NMR and MicroED. Large-scale study of complex metabolomes such as that of the model plant legume Medicago truncatula can be achieved using an integrated UHPLC-MS/MS-SPE-NMR metabolomics platform. Additionally, recent developments in MicroED to study structures of small organic molecules have enabled faster, easier and precise structure determinations of metabolites. A MicroED small molecule structure elucidation workflow (e.g., crystal screening, sample preparation, data collection and data processing/structure determination) has been described. Ongoing MicroED methods development and its future scope related to structure elucidation of specialized metabolites and metabolomics are highlighted. The incorporation of MicroED with a UHPLC-MS/MS-SPE-NMR instrumental ensemble offers the potential to accelerate and achieve higher rates of metabolite identification.

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