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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:

Neural networks to learn protein sequence–function relationships from deep mutational scanning data
Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 48
Open Access | Times Cited: 108

Showing 1-25 of 108 citing articles:

From nature to industry: Harnessing enzymes for biocatalysis
Rebecca Buller, Stefan Lutz, Romas J. Kazlauskas, et al.
Science (2023) Vol. 382, Iss. 6673
Open Access | Times Cited: 168
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
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: 122
Engineered Living Materials For Sustainability
Bolin An, Yan‐Yi Wang, Yuan‐Yuan Huang, et al.
Chemical Reviews (2022) Vol. 123, Iss. 5, pp. 2349-2419
Open Access | Times Cited: 104
AlphaFold, Artificial Intelligence (AI), and Allostery
Ruth Nussinov, Mingzhen Zhang, Yonglan Liu, et al.
The Journal of Physical Chemistry B (2022) Vol. 126, Iss. 34, pp. 6372-6383
Open Access | Times Cited: 100
Single-sequence protein structure prediction using supervised transformer protein language models
Wenkai Wang, Zhenling Peng, Jianyi Yang
Nature Computational Science (2022) Vol. 2, Iss. 12, pp. 804-814
Open Access | Times Cited: 89
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
In silico proof of principle of machine learning-based antibody design at unconstrained scale
Rahmad Akbar, Philippe A. Robert, Cédric R. Weber, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 72
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
Recent progress in the synthesis of advanced biofuel and bioproducts
Brian F. Pfleger, Ralf Takors
Current Opinion in Biotechnology (2023) Vol. 80, pp. 102913-102913
Closed Access | Times Cited: 38
Global epistasis on fitness landscapes
Juan Díaz‐Colunga, Abigail Skwara, Karna Gowda, et al.
Philosophical Transactions of the Royal Society B Biological Sciences (2023) Vol. 378, Iss. 1877
Open Access | Times Cited: 37
Optimal trade-off control in machine learning–based library design, with application to adeno-associated virus (AAV) for gene therapy
Danqing Zhu, David H. Brookes, Akosua Busia, et al.
Science Advances (2024) Vol. 10, Iss. 4
Open Access | Times Cited: 24
Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges
Manoj Kumar Goshisht
ACS Omega (2024) Vol. 9, Iss. 9, pp. 9921-9945
Open Access | Times Cited: 20
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
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: 13
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Ammar Tareen, Mahdi Kooshkbaghi, Anna Posfai, et al.
Genome biology (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 46
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins
Megan Leander, Zhuang Liu, Qiang Cui, et al.
eLife (2022) Vol. 11
Open Access | Times Cited: 35
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
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
Hierarchical graph transformer with contrastive learning for protein function prediction
Zhonghui Gu, Xiao Luo, Jiaxiao Chen, et al.
Bioinformatics (2023) Vol. 39, Iss. 7
Open Access | Times Cited: 21
Biophysics-based protein language models for protein engineering
Sam Gelman, Bryce Johnson, Chase R. Freschlin, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 9
Neural network extrapolation to distant regions of the protein fitness landscape
Chase R. Freschlin, Sarah A. Fahlberg, Pete Heinzelman, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 8
MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis and allostery from deep mutational scanning data
André J. Faure, Ben Lehner
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 7
Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning
Tobias Vornholt, Mojmír Mutný, Gregor W. Schmidt, et al.
ACS Central Science (2024) Vol. 10, Iss. 7, pp. 1357-1370
Open Access | Times Cited: 7
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