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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:

Machine learning coarse-grained potentials of protein thermodynamics
Maciej Majewski, Adrià Pérez, Philipp Thölke, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 56

Showing 26-50 of 56 citing articles:

Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects
Vijay K. Nuthakki, Rakesh Barik, Sharanabassappa B. Gangashetty, et al.
Advances in pharmacology (2025)
Closed Access
Hierarchical Deep Potential with Structure Constraints for Efficient Coarse-Grained Modeling
Qi Huang, Yedi Li, Lei Zhu, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates
Shuming Liu, Cong Wang, Bin Zhang
Biochemistry (2025)
Closed Access
A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes
Korbinian Liebl, Gregory A. Voth
Journal of Chemical Theory and Computation (2025)
Closed Access
Everything You Want to Know About Coarse‐Graining and Never Dared to Ask: Macromolecules as a Key Example
Marina Guenza
Wiley Interdisciplinary Reviews Computational Molecular Science (2025) Vol. 15, Iss. 2
Open 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
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: 3
Fitting Force Field Parameters to NMR Relaxation Data
Felix Kümmerer, Simone Orioli, Kresten Lindorff‐Larsen
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 12, pp. 3741-3751
Open Access | Times Cited: 8
Recent advances in protein conformation sampling by combining machine learning with molecular simulation
Yiming 一鸣 Tang 唐, Zhongyuan 中元 Yang 杨, Yifei 逸飞 Yao 姚, et al.
Chinese Physics B (2024) Vol. 33, Iss. 3, pp. 030701-030701
Closed Access | Times Cited: 2
Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning
Talant Ruzmetov, Ta I Hung, Saisri Padmaja Jonnalagedda, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence
Elena Frasnetti, A Magni, Matteo Castelli, et al.
Current Opinion in Structural Biology (2024) Vol. 87, pp. 102835-102835
Open Access | Times Cited: 2
Graph neural network coarse-grain force field for the molecular crystal RDX
Brian H. Lee, James P. Larentzos, John K. Brennan, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 2
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Antonio Mirarchi, Raúl P. Peláez, Guillem Simeon, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 22, pp. 9871-9878
Open Access | Times Cited: 2
Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids
Patrick G. Sahrmann, Gregory A. Voth
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 22, pp. 10235-10246
Closed Access | Times Cited: 2
Coarse-Grained Modeling Using Neural Networks Trained on Structural Data
Mikhail Ivanov, Maksim Posysoev, Alexander P. Lyubartsev
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 19, pp. 6704-6717
Open Access | Times Cited: 6
Multi-eGO: Model Improvements toward the Study of Complex Self-Assembly Processes
Fran Bačić Toplek, Emanuele Scalone, Bruno Stegani, et al.
Journal of Chemical Theory and Computation (2023) Vol. 20, Iss. 1, pp. 459-468
Open Access | Times Cited: 5
Neural potentials of proteins extrapolate beyond training data
Geemi P. Wellawatte, Glen M. Hocky, Andrew Dickson White
The Journal of Chemical Physics (2023) Vol. 159, Iss. 8
Open Access | Times Cited: 4
Neural potentials of proteins extrapolate beyond training data
Geemi P. Wellawatte, Glen M. Hocky, Andrew Dickson White
(2023)
Closed Access | Times Cited: 2
Neural potentials of proteins extrapolate beyond training data
Geemi P. Wellawatte, Glen M. Hocky, Andrew Dickson White
(2023)
Open Access | Times Cited: 2
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Cecilia Clementi, Nicholas E. Charron, Félix Musil, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 2
Resolving the dynamic properties of entangled linear polymers in non-equilibrium coarse grain simulation with a priori scaling factors
Yihan Nie, Zhuoqun Zheng, Chengkai Li, et al.
Nanoscale (2024) Vol. 16, Iss. 13, pp. 6548-6560
Closed Access
Recent Advances in Modeling Membrane β-Barrel Proteins Using Molecular Dynamics Simulations: From Their Lipid Environments to Their Assemblies
Anna L. Duncan, Ya Gao, Evert Haanappel, et al.
Methods in molecular biology (2024), pp. 311-330
Closed Access
Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations
L. Walter, Patrick K. Quoika, Martin Zacharias
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3465-3476
Open Access
Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning
Chia‐en A. Chang, Talant Ruzmetov, Ta I Hung, et al.
Research Square (Research Square) (2024)
Open Access
Creating Diverse Molecular Weaving Patterns from the Same Molecular Threads Based on Pathway Complexity
Tianyu Shan, Ding Xiao, Zhijin Ju, et al.
(2024)
Closed Access

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