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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:

Big-data and machine learning to revamp computational toxicology and its use in risk assessment
Thomas Luechtefeld, Craig Rowlands, Thomas Härtung
Toxicology Research (2018) Vol. 7, Iss. 5, pp. 732-744
Open Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility
Thomas Luechtefeld, Dan H. Marsh, Craig Rowlands, et al.
Toxicological Sciences (2018) Vol. 165, Iss. 1, pp. 198-212
Open Access | Times Cited: 270
Eutectics: formation, properties, and applications
Dongkun Yu, Zhimin Xue, Tiancheng Mu
Chemical Society Reviews (2021) Vol. 50, Iss. 15, pp. 8596-8638
Closed Access | Times Cited: 265
Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine
Ajay Vikram Singh, Mohammad Hasan Dad Ansari, Daniel Rosenkranz, et al.
Advanced Healthcare Materials (2020) Vol. 9, Iss. 17
Open Access | Times Cited: 222
Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity
Heather L. Ciallella, Hao Zhu
Chemical Research in Toxicology (2019) Vol. 32, Iss. 4, pp. 536-547
Open Access | Times Cited: 169
Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
Zeren Jiao, Pingfan Hu, Hongfei Xu, et al.
ACS Chemical Health & Safety (2020) Vol. 27, Iss. 6, pp. 316-334
Open Access | Times Cited: 139
Advances in artificial intelligence for drug delivery and development: A comprehensive review
Amol D. Gholap, Md Jasim Uddin, Md. Faiyazuddin, et al.
Computers in Biology and Medicine (2024) Vol. 178, pp. 108702-108702
Closed Access | Times Cited: 37
Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*
Nicole Kleinstreuer, Thomas Härtung
Archives of Toxicology (2024) Vol. 98, Iss. 3, pp. 735-754
Open Access | Times Cited: 31
Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms
Zhongyu Wang, Jingwen Chen, Huixiao Hong
Environmental Science & Technology (2021) Vol. 55, Iss. 10, pp. 6857-6866
Closed Access | Times Cited: 92
Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology
Costanza Rovida
ALTEX (2020)
Open Access | Times Cited: 80
Safer chemicals using less animals: kick-off of the European ONTOX project
Mathieu Vinken, Emilio Benfenati, François Busquet, et al.
Toxicology (2021) Vol. 458, pp. 152846-152846
Open Access | Times Cited: 62
Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling
Wei-Chun Chou, Zhoumeng Lin
Toxicological Sciences (2022) Vol. 191, Iss. 1, pp. 1-14
Open Access | Times Cited: 62
Probabilistic risk assessment – the keystone for the future of toxicology
Alexandra Maertens
ALTEX (2022) Vol. 39, Iss. 1, pp. 3-29
Open Access | Times Cited: 61
A framework for chemical safety assessment incorporating new approach methodologies within REACH
Nicholas Ball, R. Bars, Philip A. Botham, et al.
Archives of Toxicology (2022) Vol. 96, Iss. 3, pp. 743-766
Open Access | Times Cited: 60
Advances and applications of machine learning and deep learning in environmental ecology and health
Shixuan Cui, Yuchen Gao, Yizhou Huang, et al.
Environmental Pollution (2023) Vol. 335, pp. 122358-122358
Closed Access | Times Cited: 31
ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science
Thomas Härtung
ALTEX (2023), pp. 559-570
Open Access | Times Cited: 28
Clinical Biochemistry of Cancer
Shalini Shalini, Vishal Sharma
(2024), pp. 351-387
Closed Access | Times Cited: 9
Beyond the 3Rs: Expanding the use of human-relevant replacement methods in biomedical research
Kathrin Herrmann
ALTEX (2019), pp. 343-352
Open Access | Times Cited: 75
The exposome – a new approach for risk assessment
Fenna C.M. Sillé, Spyros Karakitsios, André Kleensang, et al.
ALTEX (2020), pp. 3-23
Open Access | Times Cited: 68
Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices
Penny Nymark, Martine Bakker, Susan Dekkers, et al.
Small (2020) Vol. 16, Iss. 6
Closed Access | Times Cited: 55
Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach
Heather L. Ciallella, Daniel P. Russo, Lauren M. Aleksunes, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 15, pp. 10875-10887
Open Access | Times Cited: 41
Replacement of animal testing by integrated approaches to testing and assessment (IATA): a call for in vivitrosi
F. Caloni, I. De Ange̊lis, Thomas Härtung
Archives of Toxicology (2022) Vol. 96, Iss. 7, pp. 1935-1950
Open Access | Times Cited: 37
Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals
Alexandra Maertens, Thomas Luechtefeld, Jean Knight, et al.
ALTEX (2024) Vol. 41, Iss. 1
Open Access | Times Cited: 7
Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches
Heather L. Ciallella, Daniel P. Russo, Lauren M. Aleksunes, et al.
Laboratory Investigation (2020) Vol. 101, Iss. 4, pp. 490-502
Open Access | Times Cited: 39
Curated Data In — Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing
Vinícius M. Alves, Scott S. Auerbach, Nicole Kleinstreuer, et al.
Alternatives to Laboratory Animals (2021) Vol. 49, Iss. 3, pp. 73-82
Open Access | Times Cited: 37
Developments in high-resolution mass spectrometric analyses of new psychoactive substances
Joshua Klingberg, Bethany Keen, Adam Cawley, et al.
Archives of Toxicology (2022) Vol. 96, Iss. 4, pp. 949-967
Open Access | Times Cited: 23
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