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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:

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: 155

Showing 1-25 of 155 citing articles:

Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins
Brian Hie, Kevin Yang, Peter S. Kim
Cell Systems (2022) Vol. 13, Iss. 4, pp. 274-285.e6
Open Access | Times Cited: 84
ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction
Pascal Notin, Aaron W. Kollasch, Daniel P. Ritter, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 77
A general model to predict small molecule substrates of enzymes based on machine and deep learning
Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 75
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
Contrastive learning in protein language space predicts interactions between drugs and protein targets
Rohit Singh, Samuel Sledzieski, Bryan D. Bryson, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 24
Open Access | Times Cited: 71
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: 61
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
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: 40
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: 38
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: 36
Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV
Aubin Ramon, Montader Ali, Misha Atkinson, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 1, pp. 74-91
Open Access | Times Cited: 24
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: 19
Protein language models are biased by unequal sequence sampling across the tree of life
Frances Ding, Jacob Steinhardt
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 18
Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning
Ziyi Zhou, Liang Zhang, Yuanxi Yu, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 16
Genomic language models: opportunities and challenges
Gonzalo Benegas, Chengzhong Ye, Carlos Albors, et al.
Trends in Genetics (2025)
Open Access | Times Cited: 1
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
Using machine learning to predict the effects and consequences of mutations in proteins
Daniel J. Diaz, Anastasiya V. Kulikova, Andrew D. Ellington, et al.
Current Opinion in Structural Biology (2023) Vol. 78, pp. 102518-102518
Open Access | Times Cited: 33
Predicting the antigenic evolution of SARS-COV-2 with deep learning
Wenkai Han, Ningning Chen, Xinzhou Xu, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 30
Cross-protein transfer learning substantially improves disease variant prediction
Milind Jagota, Chengzhong Ye, Carlos Albors, et al.
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 27
Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features
Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, et al.
mAbs (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 25
Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes
Huijin Wei, Xianghua Li
Frontiers in Genetics (2023) Vol. 14
Open Access | Times Cited: 23
Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks
Sean R. Johnson, Xiaozhi Fu, Sandra Viknander, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 22
Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening
Benjamin T. Porebski, Matthew Balmforth, Gareth J. Browne, et al.
Nature Biomedical Engineering (2023) Vol. 8, Iss. 3, pp. 214-232
Open Access | Times Cited: 22
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Mingchen Li, Liqi Kang, Yi Xiong, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 21
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