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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 with physicochemical relationships: solubility prediction in organic solvents and water
Samuel Boobier, David R. J. Hose, A. John Blacker, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 214

Showing 1-25 of 214 citing articles:

Model agnostic generation of counterfactual explanations for molecules
Geemi P. Wellawatte, Aditi Seshadri, Andrew Dickson White
Chemical Science (2022) Vol. 13, Iss. 13, pp. 3697-3705
Open Access | Times Cited: 94
Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization
Christos Xiouras, Fabio Cameli, Gustavo Lunardon Quilló, et al.
Chemical Reviews (2022) Vol. 122, Iss. 15, pp. 13006-13042
Closed Access | Times Cited: 72
Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery
Anita Ioana Visan, Irina Neguț
Life (2024) Vol. 14, Iss. 2, pp. 233-233
Open Access | Times Cited: 69
A Perspective on Explanations of Molecular Prediction Models
Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 8, pp. 2149-2160
Open Access | Times Cited: 46
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 16
An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations
Juran Noh, Hieu A. Doan, Heather Job, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 15
Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms
Zhuyifan Ye, Defang Ouyang
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 55
Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures
Florence H. Vermeire, Yunsie Chung, William H. Green
Journal of the American Chemical Society (2022) Vol. 144, Iss. 24, pp. 10785-10797
Open Access | Times Cited: 55
Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction
Gihan Panapitiya, Michael Girard, Aaron Hollas, et al.
ACS Omega (2022) Vol. 7, Iss. 18, pp. 15695-15710
Open Access | Times Cited: 53
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks
Sumin Lee, Myeonghun Lee, Ki-Won Gyak, et al.
ACS Omega (2022) Vol. 7, Iss. 14, pp. 12268-12277
Open Access | Times Cited: 45
Family Tree for Aqueous Organic Redox Couples for Redox Flow Battery Electrolytes: A Conceptual Review
Peter Fischer, Petr Mazúr, Joanna Krakowiak
Molecules (2022) Vol. 27, Iss. 2, pp. 560-560
Open Access | Times Cited: 39
Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review
Filippos Sofos, Christos Stavrogiannis, Kalliopi K. Exarchou-Kouveli, et al.
Fluids (2022) Vol. 7, Iss. 3, pp. 116-116
Open Access | Times Cited: 37
Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials
Gregory J. O. Beran
Chemical Science (2023) Vol. 14, Iss. 46, pp. 13290-13312
Open Access | Times Cited: 34
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Ye Qiu, Zhejun Li, Tong Zhang, et al.
Water Research (2023) Vol. 244, pp. 120503-120503
Closed Access | Times Cited: 31
Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning
N. Shirokii, Y. Din, Ilya Petrov, et al.
Small (2023) Vol. 19, Iss. 19
Closed Access | Times Cited: 28
Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures
Fabian Jirasek, Hans Hasse
Annual Review of Chemical and Biomolecular Engineering (2023) Vol. 14, Iss. 1, pp. 31-51
Open Access | Times Cited: 28
Exploring the potential of deep eutectic solvents in pharmaceuticals: Challenges and opportunities
Priyanka A. Shah, Vishwajit Chavda, Darshna Hirpara, et al.
Journal of Molecular Liquids (2023) Vol. 390, pp. 123171-123171
Closed Access | Times Cited: 26
Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods
Barmak Mostofian, Holli‐Joi Martin, Asghar M. Razavi, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 17, pp. 5408-5432
Open Access | Times Cited: 24
A unified ML framework for solubility prediction across organic solvents
Antony D. Vassileiou, Murray N. Robertson, Bruce G. Wareham, et al.
Digital Discovery (2023) Vol. 2, Iss. 2, pp. 356-367
Open Access | Times Cited: 22
Machine Learning Approach for the Prediction of Eutectic Temperatures for Metal-Free Deep Eutectic Solvents
Anastasia K. Lavrinenko, Ivan Yu. Chernyshov, Evgeny A. Pidko
ACS Sustainable Chemistry & Engineering (2023) Vol. 11, Iss. 42, pp. 15492-15502
Open Access | Times Cited: 21
Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models
Arash Tayyebi, Ali Alshami, Zeinab Rabiei, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 21
Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
Piotr Cysewski, Tomasz Jeliński, Maciej Przybyłek, et al.
Molecules (2024) Vol. 29, Iss. 10, pp. 2296-2296
Open Access | Times Cited: 11
The Future of Material Scientists in an Age of Artificial Intelligence
Ayman Maqsood, Chen Chen, T. Jesper Jacobsson
Advanced Science (2024) Vol. 11, Iss. 19
Open Access | Times Cited: 9
Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control
Meijin Lu, Silin Rao, Hong Yue, et al.
Crystal Growth & Design (2024) Vol. 24, Iss. 12, pp. 5374-5396
Closed Access | Times Cited: 7
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 33, pp. 23103-23120
Closed Access | Times Cited: 7
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