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:

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: 157

Showing 1-25 of 157 citing articles:

Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, et al.
Science (2023) Vol. 379, Iss. 6637, pp. 1123-1130
Open Access | Times Cited: 2121
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: 813
De novo design of protein structure and function with RFdiffusion
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, et al.
Nature (2023) Vol. 620, Iss. 7976, pp. 1089-1100
Open Access | Times Cited: 736
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: 723
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Rohith Krishna, Jue Wang, Woody Ahern, et al.
Science (2024) Vol. 384, Iss. 6693
Open Access | Times Cited: 318
De novo design of luciferases using deep learning
Hsien‐Wei Yeh, Christoffer Norn, Yakov Kipnis, et al.
Nature (2023) Vol. 614, Iss. 7949, pp. 774-780
Open Access | Times Cited: 257
From nature to industry: Harnessing enzymes for biocatalysis
Rebecca Buller, Stefan Lutz, Romas J. Kazlauskas, et al.
Science (2023) Vol. 382, Iss. 6673
Open Access | Times Cited: 207
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: 187
Language models generalize beyond natural proteins
Robert Verkuil, Ori Kabeli, Yilun Du, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 127
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 122
Improving Protein Expression, Stability, and Function with ProteinMPNN
Kiera H. Sumida, Reyes Núñez‐Franco, Indrek Kalvet, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 3, pp. 2054-2061
Open Access | Times Cited: 104
Machine Learning-Guided Protein Engineering
Petr Kouba, Pavel Kohout, Faraneh Haddadi, et al.
ACS Catalysis (2023) Vol. 13, Iss. 21, pp. 13863-13895
Open Access | Times Cited: 90
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: 88
De novo design of high-affinity binders of bioactive helical peptides
Susana Vázquez Torres, Philip J. Y. Leung, Preetham Venkatesh, et al.
Nature (2023) Vol. 626, Iss. 7998, pp. 435-442
Open Access | Times Cited: 85
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: 85
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold
ACS Central Science (2024) Vol. 10, Iss. 2, pp. 226-241
Open Access | Times Cited: 70
Top-down design of protein architectures with reinforcement learning
Isaac D. Lutz, Shunzhi Wang, Christoffer Norn, et al.
Science (2023) Vol. 380, Iss. 6642, pp. 266-273
Closed Access | Times Cited: 69
De novo protein design by inversion of the AlphaFold structure prediction network
Casper A. Goverde, Benedict Wolf, Hamed Khakzad, et al.
Protein Science (2023) Vol. 32, Iss. 6
Open Access | Times Cited: 49
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: 47
Cyclic peptide structure prediction and design using AlphaFold
Stephen Rettie, Katelyn V. Campbell, Asim K. Bera, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 46
Atomic context-conditioned protein sequence design using LigandMPNN
Justas Dauparas, Gyu Rie Lee, Robert Pecoraro, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 46
Design of Heme Enzymes with a Tunable Substrate Binding Pocket Adjacent to an Open Metal Coordination Site
Indrek Kalvet, Mary Ortmayer, Jingming Zhao, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 26, pp. 14307-14315
Open Access | Times Cited: 43
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: 41
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: 31
Computational scoring and experimental evaluation of enzymes generated by neural networks
Sean R. Johnson, Xiaozhi Fu, Sandra Viknander, et al.
Nature Biotechnology (2024)
Open Access | Times Cited: 26
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