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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:

Low-N protein engineering with data-efficient deep learning
Surojit Biswas, Grigory Khimulya, Ethan C. Alley, et al.
Nature Methods (2021) Vol. 18, Iss. 4, pp. 389-396
Open Access | Times Cited: 295

Showing 1-25 of 295 citing articles:

SignalP 6.0 predicts all five types of signal peptides using protein language models
Felix Teufel, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 7, pp. 1023-1025
Open Access | Times Cited: 1546
Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 582
Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R. Greene, et al.
Nature Biotechnology (2023) Vol. 41, Iss. 8, pp. 1099-1106
Open Access | Times Cited: 507
De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, et al.
Nature (2021) Vol. 600, Iss. 7889, pp. 547-552
Open Access | Times Cited: 416
Directed Evolution: Methodologies and Applications
Yajie Wang, Pu Xue, Mingfeng Cao, et al.
Chemical Reviews (2021) Vol. 121, Iss. 20, pp. 12384-12444
Closed Access | Times Cited: 394
Scaffolding protein functional sites using deep learning
Jue Wang, Sidney Lyayuga Lisanza, David Juergens, et al.
Science (2022) Vol. 377, Iss. 6604, pp. 387-394
Open Access | Times Cited: 295
Efficient evolution of human antibodies from general protein language models
Brian Hie, Varun R. Shanker, Duo Xu, et al.
Nature Biotechnology (2023) Vol. 42, Iss. 2, pp. 275-283
Open Access | Times Cited: 201
Learning protein fitness models from evolutionary and assay-labeled data
Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 7, pp. 1114-1122
Open Access | Times Cited: 154
Informed training set design enables efficient machine learning-assisted directed protein evolution
Bruce J. Wittmann, Yisong Yue, Frances H. Arnold
Cell Systems (2021) Vol. 12, Iss. 11, pp. 1026-1045.e7
Open Access | Times Cited: 151
Generating functional protein variants with variational autoencoders
Alex Hawkins‐Hooker, Florence Depardieu, Sebastien Baur, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 2, pp. e1008736-e1008736
Open Access | Times Cited: 149
Protein sequence design with a learned potential
Namrata Anand, Raphael R. Eguchi, I.I. Mathews, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 129
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Yunan Luo, Guangde Jiang, Tianhao Yu, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 122
Neural networks to learn protein sequence–function relationships from deep mutational scanning data
Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 48
Open Access | Times Cited: 108
Learning meaningful representations of protein sequences
Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 106
Engineering cytokine therapeutics
Jeroen Deckers, Tom Anbergen, Ayla M. Hokke, et al.
Nature Reviews Bioengineering (2023) Vol. 1, Iss. 4, pp. 286-303
Open Access | Times Cited: 103
Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity
Erika Erickson, Japheth E. Gado, Luisana Avilán, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 101
ProteInfer, deep neural networks for protein functional inference
Theo Sanderson, Maxwell L. Bileschi, David Belanger, et al.
eLife (2023) Vol. 12
Open Access | Times Cited: 97
Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space
Emily K. Makowski, Patrick C. Kinnunen, Jie Huang, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 91
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Rahmad Akbar, Habib Bashour, Puneet Rawat, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 75
Machine learning modeling of family wide enzyme-substrate specificity screens
Samuel Goldman, Ria Das, Kevin Yang, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 2, pp. e1009853-e1009853
Open Access | Times Cited: 73
Machine learning to navigate fitness landscapes for protein engineering
Chase R. Freschlin, Sarah A. Fahlberg, Philip A. Romero
Current Opinion in Biotechnology (2022) Vol. 75, pp. 102713-102713
Open Access | Times Cited: 73
From sequence to function through structure: Deep learning for protein design
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 21, pp. 238-250
Open Access | Times Cited: 72
In silico proof of principle of machine learning-based antibody design at unconstrained scale
Rahmad Akbar, Philippe A. Robert, Cédric R. Weber, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 70
Simulating 500 million years of evolution with a language model
Thomas Hayes, Roshan Rao, Halil Akin, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access | Times Cited: 69
Machine learning for functional protein design
Pascal Notin, Nathan Rollins, Yarin Gal, et al.
Nature Biotechnology (2024) Vol. 42, Iss. 2, pp. 216-228
Closed Access | Times Cited: 66
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