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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:

Informed training set design enables efficient machine learning-assisted directed protein evolution
Bruce J. Wittmann, Yisong Yue, Frances H. Arnold
Cell Systems (2021) Vol. 12, Iss. 11, pp. 1026-1045.e7
Open Access | Times Cited: 158

Showing 1-25 of 158 citing articles:

Efficient evolution of human antibodies from general protein language models
Brian Hie, Varun R. Shanker, Duo Xu, et al.
Nature Biotechnology (2023) Vol. 42, Iss. 2, pp. 275-283
Open Access | Times Cited: 223
Learning protein fitness models from evolutionary and assay-labeled data
Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 7, pp. 1114-1122
Open Access | Times Cited: 161
Combining chemistry and protein engineering for new-to-nature biocatalysis
David C. Miller, Soumitra V. Athavale, Frances H. Arnold
Nature Synthesis (2022) Vol. 1, Iss. 1, pp. 18-23
Open Access | Times Cited: 134
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Yunan Luo, Guangde Jiang, Tianhao Yu, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 127
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: 84
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: 77
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: 69
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: 63
The community-function landscape of microbial consortia
Álvaro Sánchez, Djordje Bajić, Juan Díaz‐Colunga, et al.
Cell Systems (2023) Vol. 14, Iss. 2, pp. 122-134
Closed Access | Times Cited: 60
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: 52
Role of distal sites in enzyme engineering
Jie Gu, Yan Xu, Yao Nie
Biotechnology Advances (2023) Vol. 63, pp. 108094-108094
Closed Access | Times Cited: 46
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Yuchi Qiu, Guo‐Wei Wei
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Open Access | Times Cited: 42
Unsupervised evolution of protein and antibody complexes with a structure-informed language model
Varun R. Shanker, Theodora U. J. Bruun, Brian Hie, et al.
Science (2024) Vol. 385, Iss. 6704, pp. 46-53
Closed Access | Times Cited: 30
Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
Francesca-Zhoufan Li, Ava P. Amini, Yisong Yue, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 21
Multistate and functional protein design using RoseTTAFold sequence space diffusion
Sidney Lisanza, Jacob Merle Gershon, S. Tipps, et al.
Nature Biotechnology (2024)
Open Access | Times Cited: 20
Rapid in silico directed evolution by a protein language model with EVOLVEpro
Kaiyi Jiang, Zhaoqing Yan, Matteo Di Bernardo, et al.
Science (2024)
Closed Access | Times Cited: 18
Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering
Kerr Ding, M. A. Chin, Yunlong Zhao, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 16
Active learning-assisted directed evolution
Jason Yang, Ravi Lal, James C. Bowden, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 4
Accelerated enzyme engineering by machine-learning guided cell-free expression
Grant M. Landwehr, Jonathan W. Bogart, Carol Magalhaes, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 4
Hot spots-making directed evolution easier
Haoran Yu, Shuang Ma, Yiwen Li, et al.
Biotechnology Advances (2022) Vol. 56, pp. 107926-107926
Open Access | Times Cited: 61
Making Enzymes Suitable for Organic Chemistry by Rational Protein Design
Manfred T. Reetz
ChemBioChem (2022) Vol. 23, Iss. 14
Open Access | Times Cited: 50
Heterogeneity of the GFP fitness landscape and data-driven protein design
Louisa González Somermeyer, Aubin Fleiss, Alexander S. Mishin, et al.
eLife (2022) Vol. 11
Open Access | Times Cited: 45
Accuracy and data efficiency in deep learning models of protein expression
Evangelos-Marios Nikolados, Arin Wongprommoon, Oisin Mac Aodha, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 40
evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library
Bruce J. Wittmann, Kadina E. Johnston, Patrick J. Almhjell, et al.
ACS Synthetic Biology (2022) Vol. 11, Iss. 3, pp. 1313-1324
Open Access | Times Cited: 39
Persistent spectral theory-guided protein engineering
Yuchi Qiu, Guo‐Wei Wei
Nature Computational Science (2023) Vol. 3, Iss. 2, pp. 149-163
Open Access | Times Cited: 38
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