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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:

Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, et al.
Nature Methods (2019) Vol. 16, Iss. 12, pp. 1315-1322
Open Access | Times Cited: 884

Showing 26-50 of 884 citing articles:

Deep diversification of an AAV capsid protein by machine learning
Drew Bryant, Ali Bashir, Sam Sinai, et al.
Nature Biotechnology (2021) Vol. 39, Iss. 6, pp. 691-696
Closed Access | Times Cited: 224
Enzyme discovery and engineering for sustainable plastic recycling
Baotong Zhu, Dong Wang, Na Wei
Trends in biotechnology (2021) Vol. 40, Iss. 1, pp. 22-37
Open Access | Times Cited: 223
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 221
Transformer protein language models are unsupervised structure learners
Roshan Rao, Joshua Meier, Tom Sercu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 219
Using deep learning to annotate the protein universe
Maxwell L. Bileschi, David Belanger, Drew Bryant, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 6, pp. 932-937
Open Access | Times Cited: 211
Machine learning in protein structure prediction
Mohammed AlQuraishi
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 1-8
Open Access | Times Cited: 206
PredictProtein - Predicting Protein Structure and Function for 29 Years
Michael Bernhofer, Christian Dallago, Tim Karl, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. W1, pp. W535-W540
Open Access | Times Cited: 204
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: 203
Machine learning for metabolic engineering: A review
Christopher E. Lawson, Jose Manuel Martí, Tijana Radivojević, et al.
Metabolic Engineering (2020) Vol. 63, pp. 34-60
Open Access | Times Cited: 198
ProGen: Language Modeling for Protein Generation
Ali Madani, Bryan McCann, Nikhil Naik, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 186
Genome-wide prediction of disease variant effects with a deep protein language model
Nadav Brandes, Grant Goldman, Charlotte H. Wang, et al.
Nature Genetics (2023) Vol. 55, Iss. 9, pp. 1512-1522
Open Access | Times Cited: 181
Deep Learning in Protein Structural Modeling and Design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, et al.
Patterns (2020) Vol. 1, Iss. 9, pp. 100142-100142
Open Access | Times Cited: 179
Recent advances in de novo protein design: Principles, methods, and applications
Xingjie Pan, Tanja Kortemme
Journal of Biological Chemistry (2021) Vol. 296, pp. 100558-100558
Open Access | Times Cited: 171
Antibody structure prediction using interpretable deep learning
Jeffrey A. Ruffolo, Jeremias Sulam, Jeffrey J. Gray
Patterns (2021) Vol. 3, Iss. 2, pp. 100406-100406
Open Access | Times Cited: 156
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: 155
Mega-scale experimental analysis of protein folding stability in biology and design
Kotaro Tsuboyama, Justas Dauparas, Jonathan H. Chen, et al.
Nature (2023) Vol. 620, Iss. 7973, pp. 434-444
Open Access | Times Cited: 153
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: 152
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
Accelerating antibiotic discovery through artificial intelligence
Marcelo C. R. Melo, Jacqueline R. M. A. Maasch, César de la Fuente‐Núñez
Communications Biology (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 142
Machine learning for biochemical engineering: A review
Max Mowbray, Thomas Savage, Chufan Wu, et al.
Biochemical Engineering Journal (2021) Vol. 172, pp. 108054-108054
Open Access | Times Cited: 130
Learning functional properties of proteins with language models
Serbülent Ünsal, Heval Ataş, Muammer Albayrak, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 3, pp. 227-245
Closed Access | Times Cited: 130
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, et al.
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 18-27
Open Access | Times Cited: 127
Advances in machine learning for directed evolution
Bruce J. Wittmann, Kadina E. Johnston, Zachary Wu, et al.
Current Opinion in Structural Biology (2021) Vol. 69, pp. 11-18
Open Access | Times Cited: 125
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Delaram Sadeghi, Afshin Shoeibi, Navid Ghassemi, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105554-105554
Open Access | Times Cited: 125
Controllable protein design with language models
Noelia Ferruz, Birte Höcker
Nature Machine Intelligence (2022) Vol. 4, Iss. 6, pp. 521-532
Open Access | Times Cited: 124

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