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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:

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation
Jiali Guo, Ming Sun, Xueyan Zhao, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 4, pp. 1143-1156
Closed Access | Times Cited: 13

Showing 13 citing articles:

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
Speeding Up the Cocrystallization Process: Machine Learning-Combined Methods for the Prediction of Multicomponent Systems
Rebecca Birolo, Federica Bravetti, Eugenio Alladio, et al.
Crystal Growth & Design (2023) Vol. 23, Iss. 11, pp. 7898-7911
Open Access | Times Cited: 13
Comment on ‘Physics-based representations for machine learning properties of chemical reactions’
Kevin Spiekermann, Thijs Stuyver, Lagnajit Pattanaik, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 4, pp. 048001-048001
Open Access | Times Cited: 10
Explainable Graph Neural Networks with Data Augmentation for Predicting pKa of C–H Acids
Hongle An, Xuyang Liu, Wensheng Cai, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 7, pp. 2383-2392
Closed Access | Times Cited: 5
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
(2024)
Open Access | Times Cited: 1
Cocrystal Prediction Tool (CCPT): A Web Server for Deep Learning-Assisted Cocrystal Screening and Density Evaluation
Jiali Guo, Songran Yang, Chenghui Wang, et al.
Crystal Growth & Design (2024)
Closed Access | Times Cited: 1
Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy
Yanling Wu, Kun Li, Menglong Li, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 22, pp. 7011-7031
Closed Access | Times Cited: 3
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
(2024)
Open Access
Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface
Yuting Wang, Yanling Jiang, Yu Zhou, et al.
AAPS PharmSciTech (2024) Vol. 25, Iss. 5
Closed Access
Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks
Kevin Spiekermann, Xiaorui Dong, Angiras Menon, et al.
The Journal of Physical Chemistry A (2024)
Closed Access
Enhancing chemistry-intuitive feature learning to improve prediction performance of optical properties
Ming Sun, Caixia Fu, Haoming Su, et al.
Chemical Science (2024)
Open Access
Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches
Beáta Lemli, Szilárd Pál, Ala’ Salem, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 22, pp. 12045-12045
Open Access
CAT-MPNN: A dual-mode network to predict the density of Deep Eutectic Solvents forming an open-access database
Sheng Ma, J. Li, Jingtao Wang
Chemical Engineering Science (2024), pp. 121097-121097
Closed Access
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