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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:

FireProtDB: database of manually curated protein stability data
Jan Štourač, Juraj Dúbrava, Miloš Musil, et al.
Nucleic Acids Research (2020) Vol. 49, Iss. D1, pp. D319-D324
Open Access | Times Cited: 90

Showing 1-25 of 90 citing articles:

Enzymes, In Vivo Biocatalysis, and Metabolic Engineering for Enabling a Circular Economy and Sustainability
Pattarawan Intasian, Kridsadakorn Prakinee, Aisaraphon Phintha, et al.
Chemical Reviews (2021) Vol. 121, Iss. 17, pp. 10367-10451
Open Access | Times Cited: 151
DDMut: predicting effects of mutations on protein stability using deep learning
Yunzhuo Zhou, Qisheng Pan, Douglas E. V. Pires, et al.
Nucleic Acids Research (2023) Vol. 51, Iss. W1, pp. W122-W128
Open Access | Times Cited: 94
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
Thermal stability enhancement: Fundamental concepts of protein engineering strategies to manipulate the flexible structure
Mahdie Rahban, Samaneh Zolghadri, Najmeh Salehi, et al.
International Journal of Biological Macromolecules (2022) Vol. 214, pp. 642-654
Closed Access | Times Cited: 69
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
Transfer learning to leverage larger datasets for improved prediction of protein stability changes
Henry Dieckhaus, Michael Brocidiacono, Nicholas Z. Randolph, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 6
Open Access | Times Cited: 34
Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme
Simon d’Oelsnitz, Daniel J. Diaz, Wantae Kim, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 23
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
Artificial intelligence challenges for predicting the impact of mutations on protein stability
Fabrizio Pucci, Martin Schwersensky, Marianne Rooman
Current Opinion in Structural Biology (2021) Vol. 72, pp. 161-168
Open Access | Times Cited: 71
PDBe-KB: collaboratively defining the biological context of structural data
Mihály Váradi, Stephen Anyango, David Armstrong, et al.
Nucleic Acids Research (2021) Vol. 50, Iss. D1, pp. D534-D542
Open Access | Times Cited: 67
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
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
Building Enzymes through Design and Evolution
Euan J. Hossack, Florence J. Hardy, Anthony P. Green
ACS Catalysis (2023) Vol. 13, Iss. 19, pp. 12436-12444
Open Access | Times Cited: 29
A review of enzyme design in catalytic stability by artificial intelligence
Yongfan Ming, Wenkang Wang, Rui Yin, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 23
FireProt 2.0: web-based platform for the fully automated design of thermostable proteins
Miloš Musil, Andrej Jezik, Jana Horackova, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 22
ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks
Gen Li, Sijie Yao, Long Fan
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 2, pp. 340-347
Open Access | Times Cited: 11
Guiding questions to avoid data leakage in biological machine learning applications
Judith Bernett, David B. Blumenthal, Dominik G. Grimm, et al.
Nature Methods (2024) Vol. 21, Iss. 8, pp. 1444-1453
Closed Access | Times Cited: 10
Learning the shape of protein microenvironments with a holographic convolutional neural network
Michael N. Pun, A. Ivanov, Quinn Bellamy, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 6
Open Access | Times Cited: 6
The influence of reduced amino acid alphabets on prediction orthologous protein thermostability
Yuxin Jiang, Xiaoyu Yuan, Sumei Zheng, et al.
Research Square (Research Square) (2025)
Closed Access
The 2021 Nucleic Acids Research database issue and the online molecular biology database collection
Daniel J. Rigden, Xosé M. Fernández
Nucleic Acids Research (2020) Vol. 49, Iss. D1, pp. D1-D9
Open Access | Times Cited: 50
Tools for computational design and high-throughput screening of therapeutic enzymes
Michal Vasina, Jan Velecký, Joan Planas-Iglesias, et al.
Advanced Drug Delivery Reviews (2022) Vol. 183, pp. 114143-114143
Closed Access | Times Cited: 33
Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics
Weijie Zhang, Hao Wang, Nan Feng, et al.
Antibody Therapeutics (2022) Vol. 6, Iss. 1, pp. 13-29
Open Access | Times Cited: 26
Predicting protein stability changes upon mutation using a simple orientational potential
Iván Martín Hernández, Yves Dehouck, Ugo Bastolla, et al.
Bioinformatics (2023) Vol. 39, Iss. 1
Open Access | Times Cited: 16
On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering
Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel, et al.
Faraday Discussions (2024) Vol. 252, pp. 89-114
Open Access | Times Cited: 4
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