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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:

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Marloes Arts, Víctor García Satorras, Chin‐Wei Huang, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 18, pp. 6151-6159
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

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: 38
Generative artificial intelligence for de novo protein design
Adam Winnifrith, Carlos Outeiral, Brian Hie
Current Opinion in Structural Biology (2024) Vol. 86, pp. 102794-102794
Open Access | Times Cited: 16
The Potential of Neural Network Potentials
Timothy T. Duignan
ACS Physical Chemistry Au (2024) Vol. 4, Iss. 3, pp. 232-241
Open Access | Times Cited: 14
Transferable deep generative modeling of intrinsically disordered protein conformations
Giacomo Janson, Michael Feig
PLoS Computational Biology (2024) Vol. 20, Iss. 5, pp. e1012144-e1012144
Open Access | Times Cited: 11
Scaling Graph Neural Networks to Large Proteins
Justin Airas, Bin Zhang
Journal of Chemical Theory and Computation (2025)
Open Access | Times Cited: 1
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 | Times Cited: 1
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
Yuyang Wang, Changwen Xu, Zijie Li, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 15, pp. 5077-5087
Open Access | Times Cited: 22
Synthetic pre-training for neural-network interatomic potentials
John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer
Machine Learning Science and Technology (2023) Vol. 5, Iss. 1, pp. 015003-015003
Open Access | Times Cited: 14
Diffusion Models in De Novo Drug Design
Amira A. Alakhdar, Barnabás Póczos, Newell R. Washburn
Journal of Chemical Information and Modeling (2024)
Open Access | Times Cited: 5
DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces
Michael S. Jones, Kirill Shmilovich, Andrew L. Ferguson
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 21, pp. 7908-7923
Closed Access | Times Cited: 11
Transferable deep generative modeling of intrinsically disordered protein conformations
Giacomo Janson, Michael Feig
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
Accurate Conformation Sampling via Protein Structural Diffusion
Jiahao Fan, Ziyao Li, Eric Alcaide, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
On the emergence of machine-learning methods in bottom-up coarse-graining
Patrick G. Sahrmann, Gregory A. Voth
Current Opinion in Structural Biology (2025) Vol. 90, pp. 102972-102972
Closed Access
A multiscale molecular structural neural network for molecular property prediction
Zhiwei Shi, Miao Ma, Hanyang Ning, et al.
Molecular Diversity (2025)
Closed Access
ChromoGen: Diffusion model predicts single-cell chromatin conformations
Greg Schuette, Zhuohan Lao, Bin Zhang
Science Advances (2025) Vol. 11, Iss. 5
Open Access
Protein Engineering for Industrial Biocatalysis: Principles, Approaches, and Lessons from Engineered PETases
Konstantinos Grigorakis, Christina Ferousi, Evangelos Topakas
Catalysts (2025) Vol. 15, Iss. 2, pp. 147-147
Open Access
Machine learning stochastic dynamics
Ying Tang
Zhongguo kexue. Wulixue Lixue Tianwenxue (2025) Vol. 55, Iss. 10, pp. 100501-100501
Closed Access
Molecular Denoising Using Diffusion Models with Physics-Informed Priors
Ishan Nadkarni, Jhonatam Cordeiro, N. R. Aluru
The Journal of Physical Chemistry Letters (2025), pp. 3078-3085
Closed Access
Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates
Shuming Liu, Cong Wang, Bin Zhang
Biochemistry (2025)
Closed Access
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 2
Open Access
Artificial intelligence for RNA–ligand interaction prediction: advances and prospects
Jing Li, Ying Tan, Ruiqiang Lu, et al.
Drug Discovery Today (2025), pp. 104366-104366
Closed Access
Top-Down Machine Learning of Coarse-Grained Protein Force Fields
Carles Navarro, Maciej Majewski, Gianni De Fabritiis
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 21, pp. 7518-7526
Open Access | Times Cited: 10
Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model
Tim Hsu, Babak Sadigh, Vasily V. Bulatov, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 6, pp. 2335-2348
Open Access | Times Cited: 3
Unbiasing Enhanced Sampling on a High-Dimensional Free Energy Surface with a Deep Generative Model
Yikai Liu, Tushar K. Ghosh, Guang Lin, et al.
The Journal of Physical Chemistry Letters (2024) Vol. 15, Iss. 14, pp. 3938-3945
Open Access | Times Cited: 3
Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology
Marcelo Caparotta, Alberto Pérez
The Journal of Physical Chemistry B (2024) Vol. 128, Iss. 10, pp. 2219-2227
Closed Access | Times Cited: 2
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