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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:

Expanding functional protein sequence spaces using generative adversarial networks
Donatas Repecka, Vykintas Jauniškis, Laurynas Karpus, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 4, pp. 324-333
Open Access | Times Cited: 261

Showing 1-25 of 261 citing articles:

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: 588
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
De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, et al.
Nature (2021) Vol. 600, Iss. 7889, pp. 547-552
Open Access | Times Cited: 420
Low-N protein engineering with data-efficient deep learning
Surojit Biswas, Grigory Khimulya, Ethan C. Alley, et al.
Nature Methods (2021) Vol. 18, Iss. 4, pp. 389-396
Open Access | Times Cited: 297
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: 295
ProGen2: Exploring the boundaries of protein language models
Erik Nijkamp, Jeffrey A. Ruffolo, Eli N. Weinstein, et al.
Cell Systems (2023) Vol. 14, Iss. 11, pp. 968-978.e3
Open Access | Times Cited: 162
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
Deep generative molecular design reshapes drug discovery
Xiangxiang Zeng, Fei Wang, Yuan Luo, et al.
Cell Reports Medicine (2022) Vol. 3, Iss. 12, pp. 100794-100794
Open Access | Times Cited: 120
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 7, pp. 3273-3284
Open Access | Times Cited: 111
Designing Microbial Cell Factories for the Production of Chemicals
Jae Sung Cho, Gi Bae Kim, Hyunmin Eun, et al.
JACS Au (2022) Vol. 2, Iss. 8, pp. 1781-1799
Open Access | Times Cited: 107
Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space
Emily K. Makowski, Patrick C. Kinnunen, Jie Huang, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 91
Protein–protein interaction prediction with deep learning: A comprehensive review
Farzan Soleymani, Eric Paquet, Herna L. Viktor, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 5316-5341
Open Access | Times Cited: 81
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Rahmad Akbar, Habib Bashour, Puneet Rawat, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 76
Machine learning to navigate fitness landscapes for protein engineering
Chase R. Freschlin, Sarah A. Fahlberg, Philip A. Romero
Current Opinion in Biotechnology (2022) Vol. 75, pp. 102713-102713
Open Access | Times Cited: 73
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
Ankh ☥: Optimized Protein Language Model Unlocks General-Purpose Modelling
Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 68
Discovering highly potent antimicrobial peptides with deep generative model HydrAMP
Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 67
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: 66
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: 65
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 Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing
Ankan Dash, Junyi Ye, Guiling Wang
IEEE Access (2023) Vol. 12, pp. 18330-18357
Open Access | Times Cited: 51
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: 51
Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments
Maximilian Gantz, Stefanie Neun, Elliot J. Medcalf, et al.
Chemical Reviews (2023) Vol. 123, Iss. 9, pp. 5571-5611
Open Access | Times Cited: 46
A primer to directed evolution: current methodologies and future directions
Lara Sellés Vidal, Mark Isalan, John Heap, et al.
RSC Chemical Biology (2023) Vol. 4, Iss. 4, pp. 271-291
Open Access | Times Cited: 44
Strategies for non-viral vectors targeting organs beyond the liver
Jeonghwan Kim, Yulia Eygeris, Renee C. Ryals, et al.
Nature Nanotechnology (2023) Vol. 19, Iss. 4, pp. 428-447
Closed Access | Times Cited: 42
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