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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:

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

Showing 1-25 of 122 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
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
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
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
Bo Chen, Xingyi Cheng, Li Pan, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 52
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
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
Improving protein succinylation sites prediction using embeddings from protein language model
Suresh Pokharel, Pawel Pratyush, Michael Heinzinger, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 44
Computational enzyme redesign: large jumps in function
Yinglu Cui, Jinyuan Sun, Bian Wu
Trends in Chemistry (2022) Vol. 4, Iss. 5, pp. 409-419
Closed Access | Times Cited: 36
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
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
Self-play reinforcement learning guides protein engineering
Yi Wang, Hui Tang, Lichao Huang, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 8, pp. 845-860
Closed Access | Times Cited: 31
Theoretical and Data-Driven Approaches for Biomolecular Condensates
Kadi L. Saar, Daoyuan Qian, Lydia L. Good, et al.
Chemical Reviews (2023) Vol. 123, Iss. 14, pp. 8988-9009
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
Calibrated geometric deep learning improves kinase–drug binding predictions
Yunan Luo, Yang Liu, Jian Peng
Nature Machine Intelligence (2023) Vol. 5, Iss. 12, pp. 1390-1401
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
Network of epistatic interactions in an enzyme active site revealed by large-scale deep mutational scanning
Allison Judge, Banumathi Sankaran, Liya Hu, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 12
Open Access | Times Cited: 8
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
Developing BioNavi for Hybrid Retrosynthesis Planning
Tao Zeng, Zhehao Jin, Shuangjia Zheng, et al.
JACS Au (2024) Vol. 4, Iss. 7, pp. 2492-2502
Open Access | Times Cited: 7
An end-to-end framework for the prediction of protein structure and fitness from single sequence
Yinghui Chen, Yunxin Xu, Ди Лю, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy
Nan Zheng, Yongchao Cai, Zehua Zhang, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering
Marcel Wittmund, Frédéric Cadet, Mehdi D. Davari
ACS Catalysis (2022) Vol. 12, Iss. 22, pp. 14243-14263
Closed Access | Times Cited: 33
Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering
Jesse Horne, Diwakar Shukla
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 19, pp. 6235-6245
Open Access | Times Cited: 31
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