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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 complex prediction with AlphaFold-Multimer
Richard Evans, M. E. O’Neill, Alexander Pritzel, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 2942

Showing 1-25 of 2942 citing articles:

ColabFold: making protein folding accessible to all
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, et al.
Nature Methods (2022) Vol. 19, Iss. 6, pp. 679-682
Open Access | Times Cited: 6674
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Josh Abramson, Jonas Adler, Jack Dunger, et al.
Nature (2024) Vol. 630, Iss. 8016, pp. 493-500
Open Access | Times Cited: 3095
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
Harnessing protein folding neural networks for peptide–protein docking
Tomer Tsaban, Julia K. Varga, Orly Avraham, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 894
Improved prediction of protein-protein interactions using AlphaFold2
Patrick Bryant, Gabriele Pozzati, Arne Elofsson
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 705
Computed structures of core eukaryotic protein complexes
Ian R. Humphreys, Jimin Pei, Minkyung Baek, et al.
Science (2021) Vol. 374, Iss. 6573
Open Access | Times Cited: 454
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
High-resolutionde novostructure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 280
BenchmarkingAlphaFoldfor protein complex modeling reveals accuracy determinants
Rui Yin, Brandon Y. Feng, Amitabh Varshney, et al.
Protein Science (2022) Vol. 31, Iss. 8
Open Access | Times Cited: 258
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: 240
AI-based structure prediction empowers integrative structural analysis of human nuclear pores
Shyamal Mosalaganti, Agnieszka Obarska-Kosińska, Marc Siggel, et al.
Science (2022) Vol. 376, Iss. 6598
Closed Access | Times Cited: 239
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
Mu Gao, Davi Nakajima An, Jerry M. Parks, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 219
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
Feng Ren, Xiao Ding, Min Zheng, et al.
Chemical Science (2023) Vol. 14, Iss. 6, pp. 1443-1452
Open Access | Times Cited: 189
Towards a structurally resolved human protein interaction network
David F. Burke, Patrick Bryant, Inigo Barrio‐Hernandez, et al.
Nature Structural & Molecular Biology (2023) Vol. 30, Iss. 2, pp. 216-225
Open Access | Times Cited: 179
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Jeffrey A. Ruffolo, Lee‐Shin Chu, Sai Pooja Mahajan, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 171
AI revolutions in biology
Anastassis Perrakis, Titia K. Sixma
EMBO Reports (2021) Vol. 22, Iss. 11
Open Access | Times Cited: 164
ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
Brennan Abanades, Wing Ki Wong, Fergus Boyles, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 163
Multi‐state modeling of G‐protein coupled receptors at experimental accuracy
Lim Heo, Michael Feig
Proteins Structure Function and Bioinformatics (2022) Vol. 90, Iss. 11, pp. 1873-1885
Open Access | Times Cited: 160
AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
Thomas C. Terwilliger, Dorothée Liebschner, Tristan I. Croll, et al.
Nature Methods (2023) Vol. 21, Iss. 1, pp. 110-116
Open Access | Times Cited: 153
The impact of AlphaFold2 one year on
David T. Jones, Janet M. Thornton
Nature Methods (2022) Vol. 19, Iss. 1, pp. 15-20
Open Access | Times Cited: 152
Programmable protein delivery with a bacterial contractile injection system
Joseph Kreitz, Mirco Friedrich, Akash Guru, et al.
Nature (2023) Vol. 616, Iss. 7956, pp. 357-364
Open Access | Times Cited: 152
AlphaFold2 models indicate that protein sequence determines both structure and dynamics
Hao‐Bo Guo, Alexander Perminov, Selemon Bekele, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 141
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Jérôme Tubiana, Dina Schneidman‐Duhovny, Haim J. Wolfson
Nature Methods (2022) Vol. 19, Iss. 6, pp. 730-739
Open Access | Times Cited: 140
OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Closed Access | Times Cited: 124
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, et al.
Nature Methods (2024) Vol. 21, Iss. 8, pp. 1514-1524
Open Access | Times Cited: 122
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