QC-GN<sup>2</sup>oMS<sup>2</sup>: a Graph Neural Net for High Resolution Mass Spectra Prediction-Reference-Cited by-同舟云学术

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QC-GN²oMS²: a Graph Neural Net for High Resolution Mass Spectra Prediction

Published:2024-07-16 Issue:15 Volume:64 Page:5806-5816
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Overstreet Richard¹^ORCID,King Ethan²,Clopton Grady³,Nguyen Julia²^ORCID,Ciesielski Danielle⁴^ORCID

Affiliation:

1. Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

2. Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

3. Department of Chemistry, Tennessee State University, Nashville, Tennessee 37209, United States

4. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

Funder

Pacific Northwest National Laboratory

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00446

Reference51 articles.

1. Determination of capsaicin, dihydrocapsaicin, and nonivamide in self-defense weapons by liquid chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry

2. LC–high resolution MS in environmental analysis: from target screening to the identification of unknowns

3. The benefits of high resolution mass spectrometry in environmental analysis

4. Strategies to Characterize Polar Organic Contamination in Wastewater: Exploring the Capability of High Resolution Mass Spectrometry

5. Screening new psychoactive substances in urban wastewater using high resolution mass spectrometry

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