Open Access Helper

OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
Samuel Goldman, Janet Li, Connor W. Coley
Analytical Chemistry (2024) Vol. 96, Iss. 8, pp. 3419-3428
Open Access | Times Cited: 14

Showing 14 citing articles:

Tandem mass spectrum prediction for small molecules using graph transformers
Adamo Young, Hannes Röst, Bo Wang
Nature Machine Intelligence (2024) Vol. 6, Iss. 4, pp. 404-416
Closed Access | Times Cited: 26
Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI
Jakob K. Reinhardt, David Craft, Jing‐Ke Weng
Trends in Biochemical Sciences (2025)
Closed Access | Times Cited: 1
FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events
Yannek Nowatzky, Francesco F. Russo, Jan Lisec, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications
Alexa Canchola, Lang Tran, Wonsik Woo, et al.
Environment International (2025), pp. 109404-109404
Open Access
Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation
Xinran Chen, Zijing Zhang, Xin Hong, et al.
Nature Synthesis (2025)
Open Access
Comprehensive and Explainable Fragmentation: A Machine Learning Approach for Fast and Accurate Mass Spectrum Prediction
Xianyang Zhang, Xue‐Qing Gong
The Journal of Physical Chemistry A (2025)
Closed Access
MS2Mol: A transformer model for illuminating dark chemical space from mass spectra
Thomas Butler, Abraham Frandsen, Rose Lightheart, et al.
(2023)
Open Access | Times Cited: 6
MS2Mol: A transformer model for illuminating dark chemical space from mass spectra
Thomas Butler, Abraham Frandsen, Rose Lightheart, et al.
(2023)
Open Access | Times Cited: 5
MS2Mol: A transformer model for illuminating dark chemical space from mass spectra
Thomas Butler, Abraham Frandsen, Rose Lightheart, et al.
(2023)
Open Access | Times Cited: 4
Advancing the Prediction of MS/MS Spectra Using Machine Learning
Julia Nguyen, Richard Overstreet, Ethan King, et al.
Journal of the American Society for Mass Spectrometry (2024) Vol. 35, Iss. 10, pp. 2256-2266
Closed Access | Times Cited: 1
MS2Mol: A transformer model for illuminating dark chemical space from mass spectra
Thomas Butler, Abraham Frandsen, Rose Lightheart, et al.
(2023)
Open Access | Times Cited: 3
A Decade of Computational Mass Spectrometry from Reference Spectra to Deep Learning
Michael A. Stravs
CHIMIA International Journal for Chemistry (2024) Vol. 78, Iss. 7-8, pp. 525-530
Open Access
Machine learning methods for compound annotation in non‐targeted mass spectrometry—A brief overview of fingerprinting, in silico fragmentation and de novo methods
Francesco F. Russo, Yannek Nowatzky, Carsten Jaeger, et al.
Rapid Communications in Mass Spectrometry (2024) Vol. 38, Iss. 20
Open Access
Knowledge-based In Silico Fragmentation and Annotation of Mass Spectra for Natural Products with MassKG
Bingjie Zhu, Zhenhao Li, Zhong Jin, et al.
Computational and Structural Biotechnology Journal (2024)
Open Access
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