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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:

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

Showing 1-25 of 129 citing articles:

Robust deep learning–based protein sequence design using ProteinMPNN
Justas Dauparas, Ivan Anishchenko, Nathaniel R. Bennett, et al.
Science (2022) Vol. 378, Iss. 6615, pp. 49-56
Open Access | Times Cited: 721
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: 514
ProtGPT2 is a deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, Birte Höcker
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 372
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: 297
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
Illuminating protein space with a programmable generative model
John Ingraham, Max Baranov, Zak Costello, et al.
Nature (2023) Vol. 623, Iss. 7989, pp. 1070-1078
Open Access | Times Cited: 163
Hallucinating symmetric protein assemblies
Basile I. M. Wicky, Lukas F. Milles, Alexis Courbet, et al.
Science (2022) Vol. 378, Iss. 6615, pp. 56-61
Open Access | Times Cited: 146
Protein sequence design by conformational landscape optimization
Christoffer Norn, Basile I. M. Wicky, David Juergens, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 11
Open Access | Times Cited: 123
Language models generalize beyond natural proteins
Robert Verkuil, Ori Kabeli, Yilun Du, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 115
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Raphael R. Eguchi, Christian A. Choe, Po‐Ssu Huang
PLoS Computational Biology (2022) Vol. 18, Iss. 6, pp. e1010271-e1010271
Open Access | Times Cited: 86
Computational and artificial intelligence-based methods for antibody development
Ji‐Sun Kim, Matthew McFee, Qiao Fang, et al.
Trends in Pharmacological Sciences (2023) Vol. 44, Iss. 3, pp. 175-189
Open Access | Times Cited: 84
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
De novo protein design—From new structures to programmable functions
Tanja Kortemme
Cell (2024) Vol. 187, Iss. 3, pp. 526-544
Open Access | Times Cited: 67
Machine learning-enabled retrobiosynthesis of molecules
Tianhao Yu, Aashutosh Girish Boob, Michael Volk, et al.
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 137-151
Closed Access | Times Cited: 62
A new age in protein design empowered by deep learning
Hamed Khakzad, Ilia Igashov, Arne Schneuing, et al.
Cell Systems (2023) Vol. 14, Iss. 11, pp. 925-939
Open Access | Times Cited: 40
Diffusion models in bioinformatics and computational biology
Zhiye Guo, Jian Liu, Yanli Wang, et al.
Nature Reviews Bioengineering (2023) Vol. 2, Iss. 2, pp. 136-154
Open Access | Times Cited: 39
Joint Generation of Protein Sequence and Structure with RoseTTAFold Sequence Space Diffusion
Sidney Lyayuga Lisanza, Jake Merle Gershon, S. Tipps, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 36
Opportunities and challenges in design and optimization of protein function
Dina Listov, Casper A. Goverde, Bruno E. Correia, et al.
Nature Reviews Molecular Cell Biology (2024) Vol. 25, Iss. 8, pp. 639-653
Closed Access | Times Cited: 33
Sparks of function by de novo protein design
Alexander E. Chu, Tianyu Lu, Po‐Ssu Huang
Nature Biotechnology (2024) Vol. 42, Iss. 2, pp. 203-215
Closed Access | Times Cited: 27
Rapid and automated design of two-component protein nanomaterials using ProteinMPNN
Robbert J. de Haas, Natalie Brunette, Alex Goodson, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 13
Open Access | Times Cited: 13
Computational design of soluble and functional membrane protein analogues
Casper A. Goverde, Martin Pačesa, Nicolas Goldbach, et al.
Nature (2024) Vol. 631, Iss. 8020, pp. 449-458
Open Access | Times Cited: 13
ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
Alireza Ghafarollahi, Markus J. Buehler
Digital Discovery (2024) Vol. 3, Iss. 7, pp. 1389-1409
Open Access | Times Cited: 12
Deep learning techniques have significantly impacted protein structure prediction and protein design
Robin Pearce, Yang Zhang
Current Opinion in Structural Biology (2021) Vol. 68, pp. 194-207
Open Access | Times Cited: 97
Structure-based protein design with deep learning
Sergey Ovchinnikov, Po‐Ssu Huang
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 136-144
Open Access | Times Cited: 79
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