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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:

Advances in machine learning for directed evolution
Bruce J. Wittmann, Kadina E. Johnston, Zachary Wu, et al.
Current Opinion in Structural Biology (2021) Vol. 69, pp. 11-18
Open Access | Times Cited: 125

Showing 1-25 of 125 citing articles:

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: 152
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
A versatile active learning workflow for optimization of genetic and metabolic networks
Amir Pandi, Christoph Diehl, Ali Yazdizadeh Kharrazi, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 70
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-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
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
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
Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development
Kwangho Nam, Yihan Shao, Dan Thomas Major, et al.
ACS Omega (2024)
Open Access | Times Cited: 15
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides
Montserrat Goles, Anamaria Sanchez–Daza, Gabriel Cabas-Mora, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 15
Adaptive machine learning for protein engineering
Brian Hie, Kevin Yang
Current Opinion in Structural Biology (2021) Vol. 72, pp. 145-152
Open Access | Times Cited: 84
SYNBIP: synthetic binding proteins for research, diagnosis and therapy
Xiaona Wang, Fengcheng Li, Wenqi Qiu, et al.
Nucleic Acids Research (2021) Vol. 50, Iss. D1, pp. D560-D570
Open Access | Times Cited: 64
Machine learning solutions for predicting protein–protein interactions
Rita Casadio, Pier Luigi Martelli, Castrense Savojardo
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 6
Open Access | Times Cited: 45
AAV capsid design: A Goldilocks challenge
Sergei Zolotukhin, Luk H. Vandenberghe
Trends in Molecular Medicine (2022) Vol. 28, Iss. 3, pp. 183-193
Closed 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
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
ProT-VAE: Protein Transformer Variational AutoEncoder for Functional Protein Design
Emre Sevgen, Joshua Moller, Adrian Lange, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 24
Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction
Hanna D. Clements, Autumn R. Flynn, Bryce T. Nicholls, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 32, pp. 17656-17664
Open Access | Times Cited: 22
Protein Engineering with Lightweight Graph Denoising Neural Networks
Bingxin Zhou, Lirong Zheng, Banghao Wu, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 9, pp. 3650-3661
Open Access | Times Cited: 8
A combinatorially complete epistatic fitness landscape in an enzyme active site
Kadina E. Johnston, Patrick J. Almhjell, Ella J. Watkins‐Dulaney, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 32
Open Access | Times Cited: 8
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
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