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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:

Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case
Bruno G. Galuzzi, Antonio Mirarchi, Edoardo Luca Viganò, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 19, pp. 4748-4759
Open Access | Times Cited: 16

Showing 16 citing articles:

Prediction of Redox Power for Photocatalysts: Synergistic Combination of DFT and Machine Learning
Péter Pál Fehér, Ádám Madarász, András Stirling
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 13, pp. 4125-4135
Open Access | Times Cited: 8
Harnessing iron‑sulfur enzymes for synthetic biology
Helena Shomar, Gregory Bokinsky
Biochimica et Biophysica Acta (BBA) - Molecular Cell Research (2024) Vol. 1871, Iss. 5, pp. 119718-119718
Open Access | Times Cited: 2
Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks
Rostislav Fedorov, Ganna Gryn’ova
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 15, pp. 4796-4814
Closed Access | Times Cited: 7
The energetics and evolution of oxidoreductases in deep time
Kenneth N. McGuinness, Nolan Fehon, Ryan Feehan, et al.
Proteins Structure Function and Bioinformatics (2023) Vol. 92, Iss. 1, pp. 52-59
Open Access | Times Cited: 6
Predicting Redox Potentials by Graph-Based Machine Learning Methods
Linlin Jia, Éric Brémond, Larissa Zaida, et al.
(2024)
Open Access | Times Cited: 1
Predicting redox potentials by graph‐based machine learning methods
Linlin Jia, Éric Brémond, Larissa Zaida, et al.
Journal of Computational Chemistry (2024) Vol. 45, Iss. 28, pp. 2383-2396
Open Access | Times Cited: 1
Insights into the mechanisms of the synergistic fouling mitigation effect of electric and acoustic fields in microfiltration
Yuxiang Shen, Haibo Cheng, Ji Shu, et al.
Separation and Purification Technology (2024) Vol. 341, pp. 126850-126850
Closed Access
Machine Learning Approach to Vertical Energy Gap in Redox Processes
Ronit Sarangi, Suman Maity, Atanu Acharya
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 15, pp. 6747-6755
Open Access
Application of protein engineering to ene-reductase for the synthesis of chiral compounds through asymmetric reaction
Jiacheng Feng, Huiru Ye, Changxin Lu, et al.
Critical Reviews in Biotechnology (2024), pp. 1-18
Closed Access
Retuning the potential of the electrochemical leaf
Marta M. Dolińska, Adam J. Kirwan, Clare F. Megarity
Faraday Discussions (2024) Vol. 252, pp. 188-207
Open Access
Redox Properties of Pyrroloquinoline Quinone in Pyranose Dehydrogenase Measured by Direct Electron Transfer
Kota Takeda, James A. Birrell, Ryo Kusuoka, et al.
ACS Catalysis (2024) Vol. 14, Iss. 16, pp. 12242-12250
Closed Access
Predicting Redox Potentials by Graph-Based Machine Learning Methods
Linlin Jia, Éric Brémond, Larissa Zaida, et al.
(2023)
Open Access | Times Cited: 1
Ground truth explanation dataset for chemical property prediction on molecular graphs
Eugen Hruška, Liang Zhao, Fang Liu
(2022)
Open Access | Times Cited: 1
Ground truth explanation dataset for chemical property prediction on molecular graphs
Eugen Hruška, Liang Zhao, Fang Liu
(2022)
Open Access | Times Cited: 1
The energetics and evolution of oxidoreductases in deep time
Vikas Nanda, Kenneth N. McGuinness, Nolan Fehon, et al.
Authorea (Authorea) (2023)
Open Access
Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation
Giuseppe Silvestri, Federica Arrigoni, Francesca Persico, et al.
Molecules (2023) Vol. 28, Iss. 16, pp. 6016-6016
Open Access
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