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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 equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 5, pp. 558-567
Open Access | Times Cited: 23

Showing 23 citing articles:

Structure prediction of alternative protein conformations
Patrick Bryant, Frank Noé
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Generative Model for Constructing Reaction Path from Initial to Final States
Akihide Hayashi, So Takamoto, Ju Li, et al.
Journal of Chemical Theory and Computation (2025)
Closed Access
Frontiers in integrative structural modeling of macromolecular assemblies
Kartik Majila, Shreyas Arvindekar, M. Jindal, et al.
QRB Discovery (2025) Vol. 6
Open Access
Challenges and compromises: Predicting unbound antibody structures with deep learning
Alexander Greenshields‐Watson, Odysseas Vavourakis, Fabian C. Spoendlin, et al.
Current Opinion in Structural Biology (2025) Vol. 90, pp. 102983-102983
Open Access
Scaling Graph Neural Networks to Large Proteins
Justin Airas, Bin Zhang
Journal of Chemical Theory and Computation (2025)
Open Access
Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence–based methods
Akashnathan Aranganathan, Xinyu Gu, Dedi Wang, et al.
Current Opinion in Structural Biology (2025) Vol. 91, pp. 103000-103000
Open Access
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary
eLife (2024) Vol. 13
Open Access | Times Cited: 4
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2
Xingyue Guan, Qian-Yuan Tang, Weitong Ren, et al.
Proceedings of the National Academy of Sciences (2024) Vol. 121, Iss. 35
Closed Access | Times Cited: 3
Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR
Yuanpeng J. Huang, G.T. Montelione
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Deep learning guided design of dynamic proteins
Amy B Guo, Deniz Akpinaroglu, Mark J. S. Kelly, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Artificial Intelligence Must Be Made More Scientific
Peter V. Coveney, Roger Highfield
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 15, pp. 5739-5741
Open Access | Times Cited: 2
From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph‐Based Deep Learning
Yaosen Min, Wei Ye, Peizhuo Wang, et al.
Advanced Science (2024) Vol. 11, Iss. 40
Open Access | Times Cited: 2
Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules
Trevor A. Norton, Debswapna Bhattacharya
Journal of Molecular Biology (2024), pp. 168818-168818
Open Access | Times Cited: 1
Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy
Yao Hu, Hao Yang, Mingwei Li, et al.
Advanced Science (2024) Vol. 11, Iss. 44
Open Access | Times Cited: 1
AlphaFold2's training set powers its predictions of fold-switched conformations
Joseph W. Schafer, Lauren L. Porter
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Yaosen Min, Wei Ye, Peizhuo Wang, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 4
Sampling thermodynamic ensembles of molecular systems with generative neural networks: Will integrating physics-based models close the generalization gap?
Grant M. Rotskoff
Current Opinion in Solid State and Materials Science (2024) Vol. 30, pp. 101158-101158
Closed Access
Predicting the alternative conformation of a known protein structure based on the distance map of AlphaFold2
Jiaxuan Li, Zefeng Zhu, Chen Song
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Deep learning of protein energy landscape and conformational dynamics from experimental structures in PDB
Yike Tang, Mendi Yu, Ganggang Bai, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
Seeing Double: Molecular dynamics simulations reveal the stability of certain alternate protein conformations in crystal structures
Aviv A. Rosenberg, Sanketh Vedula, Alex Bronstein, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary
eLife (2024) Vol. 13
Open Access
Generating Multistate Conformations of P-type ATPases with a Conditional Diffusion Model
Jingtian Xu, Yong Wang
Journal of Chemical Information and Modeling (2024)
Closed Access
Editorial: Machine Learning for Structural Biology
Gabriele Corso, Gina El Nesr, Hannah K. Wayment-Steele
PRX Life (2024) Vol. 2, Iss. 4
Closed Access
Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning
Timothy J O'Donnell, Chakravarthi Kanduri, Giulio Isacchini, et al.
Cell Systems (2024) Vol. 15, Iss. 12, pp. 1168-1189
Open Access
Generating Multi-state Conformations of P-type ATPases with a Diffusion Model
Jingtian Xu, Yong Wang
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
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