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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:

Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenhua Wu, Chang‐Yu Hsieh, et al.
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 399

Showing 1-25 of 399 citing articles:

Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, et al.
Communications Materials (2022) Vol. 3, Iss. 1
Open Access | Times Cited: 334
AI in drug discovery and its clinical relevance
Rizwan Qureshi, Muhammad Irfan, Taimoor Muzaffar Gondal, et al.
Heliyon (2023) Vol. 9, Iss. 7, pp. e17575-e17575
Open Access | Times Cited: 148
Everything is connected: Graph neural networks
Petar Veličković
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102538-102538
Open Access | Times Cited: 140
Hopfield Networks is All You Need
Hubert Ramsauer, Bernhard Schäfl, Johannes M. Lehner, et al.
(2021)
Closed Access | Times Cited: 130
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
Derek van Tilborg, Alisa Alenicheva, Francesca Grisoni
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 23, pp. 5938-5951
Open Access | Times Cited: 116
Artificial intelligence in drug discovery: applications and techniques
Jianyuan Deng, Zhibo Yang, Iwao Ojima, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 109
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
Hanxuan Cai, Huimin Zhang, Duancheng Zhao, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Open Access | Times Cited: 105
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: 95
Quantum machine learning for chemistry and physics
Manas Sajjan, Junxu Li, Raja Selvarajan, et al.
Chemical Society Reviews (2022) Vol. 51, Iss. 15, pp. 6475-6573
Open Access | Times Cited: 91
Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point
Claudio N. Cavasotto, Valeria Scardino
ACS Omega (2022) Vol. 7, Iss. 51, pp. 47536-47546
Open Access | Times Cited: 87
A graph representation of molecular ensembles for polymer property prediction
Matteo Aldeghi, Connor W. Coley
Chemical Science (2022) Vol. 13, Iss. 35, pp. 10486-10498
Open Access | Times Cited: 86
Artificial Intelligence for Drug Discovery: Are We There Yet?
Catrin Hasselgren, Tudor I. Oprea
The Annual Review of Pharmacology and Toxicology (2023) Vol. 64, Iss. 1, pp. 527-550
Open Access | Times Cited: 75
Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Alexander Partin, Thomas Brettin, Yitan Zhu, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 70
A systematic study of key elements underlying molecular property prediction
Jianyuan Deng, Zhibo Yang, Hehe Wang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 66
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 4, pp. 340-350
Open Access | Times Cited: 60
Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants’ Activities and Properties
Shifa Zhong, Xiaohong Guan
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 18193-18202
Closed Access | Times Cited: 55
Meta Learning With Graph Attention Networks for Low-Data Drug Discovery
Qiujie Lv, Guanxing Chen, Ziduo Yang, et al.
IEEE Transactions on Neural Networks and Learning Systems (2023) Vol. 35, Iss. 8, pp. 11218-11230
Closed Access | Times Cited: 50
Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning
Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 48
Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations
Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 47
Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective
Cheng Fang, Ye Wang, Richard Grater, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 11, pp. 3263-3274
Closed Access | Times Cited: 42
Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
Amit Gangwal, M. Azim Ansari, Iqrar Ahmad, et al.
Frontiers in Pharmacology (2024) Vol. 15
Open Access | Times Cited: 27
Advancing material property prediction: using physics-informed machine learning models for viscosity
Alex K. Chew, Matthew Sender, Zachary Kaplan, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 22
Relative molecule self-attention transformer
Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 17
Deep learning in retrosynthesis planning: datasets, models and tools
Jingxin Dong, Mingyi Zhao, Yuansheng Liu, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 78
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Mingyang Wang, Chang‐Yu Hsieh, Jike Wang, et al.
Journal of Medicinal Chemistry (2022) Vol. 65, Iss. 13, pp. 9478-9492
Closed Access | Times Cited: 62
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