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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:

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: 252

Showing 1-25 of 252 citing articles:

AlphaFold2 and its applications in the fields of biology and medicine
Zhenyu Yang, Xiaoxi Zeng, Yi Zhao, et al.
Signal Transduction and Targeted Therapy (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 241
Before and after AlphaFold2: An overview of protein structure prediction
Letícia M. F. Bertoline, Angélica N. Lima, José Eduardo Krieger, et al.
Frontiers in Bioinformatics (2023) Vol. 3
Open Access | Times Cited: 147
Structure-based prediction of T cell receptor:peptide-MHC interactions
Philip Bradley
eLife (2023) Vol. 12
Open Access | Times Cited: 89
AlphaFold2 protein structure prediction: Implications for drug discovery
Neera Borkakoti, Janet M. Thornton
Current Opinion in Structural Biology (2023) Vol. 78, pp. 102526-102526
Open Access | Times Cited: 58
AlphaFold, allosteric, and orthosteric drug discovery: Ways forward
Ruth Nussinov, Mingzhen Zhang, Yonglan Liu, et al.
Drug Discovery Today (2023) Vol. 28, Iss. 6, pp. 103551-103551
Open Access | Times Cited: 53
Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy
Rui Yin, Brian G. Pierce
Protein Science (2023) Vol. 33, Iss. 1
Open Access | Times Cited: 51
Critical assessment of methods of protein structure prediction (CASP)—Round XV
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, et al.
Proteins Structure Function and Bioinformatics (2023) Vol. 91, Iss. 12, pp. 1539-1549
Open Access | Times Cited: 49
AlphaFold2 structures guide prospective ligand discovery
Jiankun Lyu, Nicholas J. Kapolka, Ryan H. Gumpper, et al.
Science (2024) Vol. 384, Iss. 6702
Closed Access | Times Cited: 49
TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning
Rui Yin, Helder Veras Ribeiro Filho, Valerie C. L. Lin, et al.
Nucleic Acids Research (2023) Vol. 51, Iss. W1, pp. W569-W576
Open Access | Times Cited: 45
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Hélène Bret, Jinmei Gao, Diego Javier Zea, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 45
CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2
Ben Shor, Dina Schneidman‐Duhovny
Nature Methods (2024) Vol. 21, Iss. 3, pp. 477-487
Open Access | Times Cited: 38
Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments
Greta Grassmann, Mattia Miotto, Fausta Desantis, et al.
Chemical Reviews (2024) Vol. 124, Iss. 7, pp. 3932-3977
Open Access | Times Cited: 32
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Vinayak Agarwal, Andrew C. McShan
Nature Chemical Biology (2024) Vol. 20, Iss. 8, pp. 950-959
Closed Access | Times Cited: 25
Modelling protein complexes with crosslinking mass spectrometry and deep learning
Kolja Stahl, Robert Warneke, Lorenz Demann, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 25
Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions
E. Schmid, Johannes C. Walter
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 22
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery
Wiktoria Wilman, Sonia Wróbel, Weronika Bielska, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Open Access | Times Cited: 59
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
Minhyeok Lee
Molecules (2023) Vol. 28, Iss. 13, pp. 5169-5169
Open Access | Times Cited: 37
Protein–protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction
Huan Rui, Kate S. Ashton, Jaeki Min, et al.
RSC Chemical Biology (2023) Vol. 4, Iss. 3, pp. 192-215
Open Access | Times Cited: 36
CD19 CAR antigen engagement mechanisms and affinity tuning
Changhao He, Jorge Mansilla‐Soto, Nandish Khanra, et al.
Science Immunology (2023) Vol. 8, Iss. 81
Open Access | Times Cited: 34
Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning
Xingang Peng, Yipin Lei, Peiyuan Feng, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 4, pp. 395-407
Closed Access | Times Cited: 33
Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens
Federica Guarra, Giorgio Colombo
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 16, pp. 5315-5333
Open Access | Times Cited: 32
Recent advances in predicting and modeling protein–protein interactions
Jesse Durham, Jing Zhang, Ian R. Humphreys, et al.
Trends in Biochemical Sciences (2023) Vol. 48, Iss. 6, pp. 527-538
Closed Access | Times Cited: 30
Targeting small GTPases: emerging grasps on previously untamable targets, pioneered by KRAS
Guowei Yin, Jing Huang, Johnny Petela, et al.
Signal Transduction and Targeted Therapy (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 27
A combinatorial approach to uncover an additional Integrator subunit
Sarah Offley, Moritz M. Pfleiderer, Avery Zucco, et al.
Cell Reports (2023) Vol. 42, Iss. 3, pp. 112244-112244
Open Access | Times Cited: 25
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