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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:

Physical pooling functions in graph neural networks for molecular property prediction
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, et al.
Computers & Chemical Engineering (2023) Vol. 172, pp. 108202-108202
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 1, pp. 9-17
Open Access | Times Cited: 163
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
David Buterez, Jon Paul Janet, Steven J. Kiddle, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 22
Generative AI and process systems engineering: The next frontier
Benjamin Decardi‐Nelson, Abdulelah S. Alshehri, Akshay Ajagekar, et al.
Computers & Chemical Engineering (2024) Vol. 187, pp. 108723-108723
Open Access | Times Cited: 20
Neural multi-task learning in drug design
Stephan Allenspach, Jan A. Hiss, Gisbert Schneider
Nature Machine Intelligence (2024) Vol. 6, Iss. 2, pp. 124-137
Closed Access | Times Cited: 12
Introduction to Predicting Properties of Organic Materials
Didier Mathieu
Challenges and advances in computational chemistry and physics (2025), pp. 27-63
Closed Access | Times Cited: 1
Towards understanding structure–property relations in materials with interpretable deep learning
Tien-Sinh Vu, Minh-Quyet Ha, Duong‐Nguyen Nguyen, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 17
Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph
Xiaohua Lu, Liangxu Xie, Lei Xu, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 1666-1679
Open Access | Times Cited: 8
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
Kobi Felton, Lukas Raßpe-Lange, Jan G. Rittig, et al.
Chemical Engineering Journal (2024) Vol. 492, pp. 151999-151999
Open Access | Times Cited: 8
Thermodynamics-consistent graph neural networks
Jan G. Rittig, Alexander Mitsos
Chemical Science (2024)
Open Access | Times Cited: 7
High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties
Santiago Vargas, Winston Gee, Anastassia N. Alexandrova
Digital Discovery (2024) Vol. 3, Iss. 5, pp. 987-998
Open Access | Times Cited: 6
Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks
Kumail Alhamoud, Yasir Ghunaim, Abdulelah S. Alshehri, et al.
Computers & Chemical Engineering (2024) Vol. 183, pp. 108622-108622
Closed Access | Times Cited: 5
Physics-inspired machine learning of localized intensive properties
Ke Chen, Christian Künkel, Bingqing Cheng, et al.
Chemical Science (2023) Vol. 14, Iss. 18, pp. 4913-4922
Open Access | Times Cited: 12
HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions
Rishabh D. Guha, Santiago Vargas, Evan Walter Clark Spotte‐Smith, et al.
Journal of Chemical Information and Modeling (2025)
Open Access
Introduction to Machine Learning for Predictive Modeling of Organic Materials
Didier Mathieu, Clément Wespiser
Challenges and advances in computational chemistry and physics (2025), pp. 43-60
Closed Access
Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop
Youngchun Kwon, Hyunjeong Jeon, Joon Hyuk Choi, et al.
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
Pooling solvent mixtures for solvation free energy predictions
Roel J. Leenhouts, Nathan Morgan, Emad Al Ibrahim, et al.
Chemical Engineering Journal (2025), pp. 162232-162232
Open Access
Review on Graph Neural Networks for Process Soft Sensor Development, Fault Diagnosis, and Process Monitoring
Mingwei Jia, Yuan Yao, Yi Liu
Industrial & Engineering Chemistry Research (2025)
Closed Access
Uncertainty-Aware Deep Reinforcement Learning Approach for Computational Molecular Design
Abdulelah S. Alshehri, Bryan Tantisujjatham, Maher M. Alrashed
Industrial & Engineering Chemistry Research (2025)
Closed Access
Multi-fidelity graph neural networks for predicting toluene/water partition coefficients
Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, et al.
(2024)
Open Access | Times Cited: 3
Modelling local and general quantum mechanical properties with attention-based pooling
David Buterez, Jon Paul Janet, Steven J. Kiddle, et al.
Communications Chemistry (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 9
Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
(2023)
Open Access | Times Cited: 9
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 13, pp. 5695-5707
Open Access | Times Cited: 2
Artificial intelligence for novel fuel design
S. Mani Sarathy, Basem A. Eraqi
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105630-105630
Closed Access | Times Cited: 2
Graph neural networks with molecular segmentation for property prediction and structure–property relationship discovery
Zhudan Chen, Dazi Li, Minghui Liu, et al.
Computers & Chemical Engineering (2023) Vol. 179, pp. 108403-108403
Closed Access | Times Cited: 5
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
David Buterez, Jon Paul Janet, Steven J. Kiddle, et al.
(2024)
Open Access | Times Cited: 1
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