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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:

Deep Dive into Machine Learning Models for Protein Engineering
Yuting Xu, Deeptak Verma, Robert P. Sheridan, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 6, pp. 2773-2790
Open Access | Times Cited: 182

Showing 1-25 of 182 citing articles:

Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 543
AlphaFold2 and the future of structural biology
Patrick Cramer
Nature Structural & Molecular Biology (2021) Vol. 28, Iss. 9, pp. 704-705
Closed Access | Times Cited: 285
Deep Learning in Virtual Screening: Recent Applications and Developments
Talia B. Kimber, Yonghui Chen, Andrea Volkamer
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 9, pp. 4435-4435
Open Access | Times Cited: 154
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
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, et al.
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 18-27
Open Access | Times Cited: 127
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
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
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: 77
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
Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review
Rocco Meli, Garrett M. Morris, Philip C. Biggin
Frontiers in Bioinformatics (2022) Vol. 2
Open Access | Times Cited: 72
Synthetic Biology: Bottom-Up Assembly of Molecular Systems
Stephan Hirschi, Thomas R. Ward, Wolfgang Meier, et al.
Chemical Reviews (2022) Vol. 122, Iss. 21, pp. 16294-16328
Closed Access | Times Cited: 71
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction
Jin Li, Naiteng Wu, Jian Zhang, et al.
Nano-Micro Letters (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 56
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: 54
Decoding Catalysis by Terpene Synthases
Joshua N. Whitehead, Nicole G. H. Leferink, Linus O. Johannissen, et al.
ACS Catalysis (2023) Vol. 13, Iss. 19, pp. 12774-12802
Open Access | Times Cited: 36
Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations
Daniel J. Diaz, Chengyue Gong, Jeffrey Ouyang-Zhang, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 18
On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins
Kresten Lindorff‐Larsen, Birthe B. Kragelund
Journal of Molecular Biology (2021) Vol. 433, Iss. 20, pp. 167196-167196
Open Access | Times Cited: 71
Systematic engineering of artificial metalloenzymes for new-to-nature reactions
Tobias Vornholt, Fadri Christoffel, Michela M. Pellizzoni, et al.
Science Advances (2021) Vol. 7, Iss. 4
Open Access | Times Cited: 65
Computational methods to predict protein aggregation
Susanna Navarro, Salvador Ventura
Current Opinion in Structural Biology (2022) Vol. 73, pp. 102343-102343
Open Access | Times Cited: 55
The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods
Thereza A. Soares, Ariane Nunes‐Alves, Angelica Mazzolari, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 22, pp. 5317-5320
Closed Access | Times Cited: 55
Extremophiles and extremozymes in lignin bioprocessing
Daochen Zhu, Majjid A. Qaria, Zhu Bin, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 157, pp. 112069-112069
Closed Access | Times Cited: 41
Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design
Braun Markus, Gruber Christian C, Krassnigg Andreas, et al.
ACS Catalysis (2023) Vol. 13, Iss. 21, pp. 14454-14469
Open Access | Times Cited: 29
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: 25
Advancing microbial food safety and hazard analysis through predictive mathematical modeling
Vikram Kumar, Jayesh J. Ahire, Neetu Kumra Taneja
The Microbe (2024) Vol. 2, pp. 100049-100049
Open Access | Times Cited: 10
Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants
Niccolo Alberto Elia Venanzi, Andrea Basciu, Attilio V. Vargiu, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 7, pp. 2681-2694
Open Access | Times Cited: 9
A review of deep learning methods for ligand based drug virtual screening
Hongjie Wu, Junkai Liu, Runhua Zhang, et al.
Fundamental Research (2024) Vol. 4, Iss. 4, pp. 715-737
Open Access | Times Cited: 9
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