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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:

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks
Bian Li, Yucheng Yang, John A. Capra, et al.
PLoS Computational Biology (2020) Vol. 16, Iss. 11, pp. e1008291-e1008291
Open Access | Times Cited: 109

Showing 1-25 of 109 citing articles:

Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 245
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset
Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 2
Open Access | Times Cited: 109
Rapid protein stability prediction using deep learning representations
Lasse M. Blaabjerg, Maher M. Kassem, Lydia L. Good, et al.
eLife (2023) Vol. 12
Open Access | Times Cited: 103
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: 44
Development and use of machine learning algorithms in vaccine target selection
Barbara Bravi
npj Vaccines (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 34
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: 25
Structure-based self-supervised learning enables ultrafast protein stability prediction upon mutation
Jinyuan Sun, Tong Zhu, Yinglu Cui, et al.
The Innovation (2025) Vol. 6, Iss. 1, pp. 100750-100750
Open Access | Times Cited: 2
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: 72
An antisymmetric neural network to predict free energy changes in protein variants
Silvia Benevenuta, Corrado Pancotti, Piero Fariselli, et al.
Journal of Physics D Applied Physics (2021) Vol. 54, Iss. 24, pp. 245403-245403
Closed Access | Times Cited: 58
Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins
Simon Dürr, Andrea Levy, Ursula Röthlisberger
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 39
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: 38
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: 15
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: 11
Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy
Yunxin Xu, Ди Лю, Haipeng Gong
Nature Computational Science (2024) Vol. 4, Iss. 11, pp. 840-850
Closed Access | Times Cited: 9
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures
Qisheng Pan, Thanh Nguyen, David B. Ascher, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Open Access | Times Cited: 37
BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification
Shuyu Wang, Hongzhou Tang, Yuliang Zhao, et al.
Protein Science (2022) Vol. 31, Iss. 11
Open Access | Times Cited: 32
ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks
Shuyu Wang, Hongzhou Tang, Peng Shan, et al.
Computational Biology and Chemistry (2023) Vol. 107, pp. 107952-107952
Open Access | Times Cited: 22
Automated optimisation of solubility and conformational stability of antibodies and proteins
Angelo Rosace, Anja Bennett, Marc Oeller, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 21
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: 19
PROSTATA: a framework for protein stability assessment using transformers
Dmitriy Umerenkov, Fedor Nikolaev, Tatiana Shashkova, et al.
Bioinformatics (2023) Vol. 39, Iss. 11
Open Access | Times Cited: 19
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures
Tong Wang, Guangming Xiang, Siwei He, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 7
ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes
Xiangwen Wang, Derek J. Quinn, Thomas S. Moody, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3123-3139
Open Access | Times Cited: 6
A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations
Corrado Pancotti, Silvia Benevenuta, Valeria Repetto, et al.
Genes (2021) Vol. 12, Iss. 6, pp. 911-911
Open Access | Times Cited: 36
The rapid emergence of multiple sublineages of Omicron (B.1.1.529) variant: Dynamic profiling via molecular phylogenetics and mutational landscape studies
Chiranjib Chakraborty, Manojit Bhattacharya, Ashish Ranjan Sharma, et al.
Journal of Infection and Public Health (2022) Vol. 15, Iss. 11, pp. 1234-1258
Open Access | Times Cited: 27
Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses
Yang Yu, Zhe Wang, Lingling Wang, et al.
Journal of Cheminformatics (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 26
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