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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 and Artificial Intelligence in Toxicological Sciences
Zhoumeng Lin, Wei-Chun Chou
Toxicological Sciences (2022) Vol. 189, Iss. 1, pp. 7-19
Open Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment
Ajay Vikram Singh, Girija Bansod, Mihir Mahajan, et al.
ACS Omega (2023) Vol. 8, Iss. 24, pp. 21377-21390
Open Access | Times Cited: 65
An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice
Wei-Chun Chou, Qiran Chen, Long Yuan, et al.
Journal of Controlled Release (2023) Vol. 361, pp. 53-63
Open Access | Times Cited: 40
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: 27
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: 57
TOXRIC: a comprehensive database of toxicological data and benchmarks
Lianlian Wu, Bowei Yan, Junshan Han, et al.
Nucleic Acids Research (2022) Vol. 51, Iss. D1, pp. D1432-D1445
Open Access | Times Cited: 42
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: 27
Artificial intelligence as the new frontier in chemical risk assessment
Thomas Härtung
Frontiers in Artificial Intelligence (2023) Vol. 6
Open Access | Times Cited: 24
A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies
Krishnendu Sinha, Nabanita Ghosh, Parames C. Sil
Chemical Research in Toxicology (2023) Vol. 36, Iss. 8, pp. 1174-1205
Closed Access | Times Cited: 22
Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions
Lingxuan Meng, Beihai Zhou, Haijun Liu, et al.
The Science of The Total Environment (2024) Vol. 946, pp. 174201-174201
Closed Access | Times Cited: 9
Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)
Samuel J. Belfield, Mark T.D. Cronin, Steven J. Enoch, et al.
PLoS ONE (2023) Vol. 18, Iss. 5, pp. e0282924-e0282924
Open Access | Times Cited: 19
Efficiency of pharmaceutical toxicity prediction in computational toxicology
Yoshihiro Uesawa
Toxicological Research (2024) Vol. 40, Iss. 1, pp. 1-9
Closed Access | Times Cited: 7
Systematic approaches to machine learning models for predicting pesticide toxicity
Ganesan Anandhi, M. Iyapparaja
Heliyon (2024) Vol. 10, Iss. 7, pp. e28752-e28752
Open Access | Times Cited: 7
Utility of life stage-specific chemical risk assessments based on New Approach Methodologies (NAMs)
Pavani K. Gonnabathula, Me-Kyoung Choi, Miao Li, et al.
Food and Chemical Toxicology (2024) Vol. 190, pp. 114789-114789
Closed Access | Times Cited: 6
AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability
Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, et al.
Frontiers in Toxicology (2024) Vol. 6
Open Access | Times Cited: 6
Biosynthesis of biomolecules from saffron as an industrial crop and their regulation, with emphasis on the chemistry, extraction methods, identification techniques, and potential applications in human health and food: A critical comprehensive review
Vishal Gupta, Gayatri Jamwal, G. Rai, et al.
Biocatalysis and Agricultural Biotechnology (2024) Vol. 59, pp. 103260-103260
Closed Access | Times Cited: 5
Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers
Xuejiao J. Gao, Krzesimir Ciura, Yuanjie Ma, et al.
Advanced Materials (2024)
Closed Access | Times Cited: 5
Immunomodulatory lectin from Cordia myxa targets PI3K/AKT signalling mediated apoptosis to regress both in-vitro and in-vivo tumour
B M Siddesh, Bayram Kıran, Ankith Sherapura, et al.
International Journal of Biological Macromolecules (2025) Vol. 294, pp. 139433-139433
Closed Access
Strategies for Redesigning Withdrawn Drugs to Enhance Therapeutic Efficacy and Safety: A Review
Chirag Patel, Adeeba Shakeel, Raghvendra Mall, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2025) Vol. 15, Iss. 1
Closed Access
Computational Toxicology and Risk Assessment
Brad Reisfeld, Sherif Farag
Elsevier eBooks (2025)
Closed Access
The role of trust in the use of artificial intelligence for chemical risk assessment
Pim N.H. Wassenaar, Jordi Minnema, Jelle Vriend, et al.
Regulatory Toxicology and Pharmacology (2024) Vol. 148, pp. 105589-105589
Open Access | Times Cited: 4
Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm – a 10-year multicenter retrospective study
Yuan Liu, Songyun Zhao, Wenyi Du, et al.
Frontiers in Medicine (2025) Vol. 11
Open Access
Editorial: Five grand challenges in toxicology
Bengt Fadeel, Jan Alexander, Sara C. Antunes, et al.
Frontiers in Toxicology (2025) Vol. 6
Open Access
Augmented allometric scaling: Predicting drug clearance in farm animals with machine learning using body weight
David Inauen, L.S. Lautz, Jan C.M. Hendriks, et al.
Computational Toxicology (2025), pp. 100341-100341
Closed Access
Integrating artificial intelligence in drug discovery and early drug development: a transformative approach
Alberto Ocaña, Atanasio Pandiella, Cristian Privat, et al.
Biomarker Research (2025) Vol. 13, Iss. 1
Open Access
Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development
Nguyen Duc Viet, Jihae Park, Hojun Lee, et al.
Environmental Pollution (2023) Vol. 341, pp. 123017-123017
Closed Access | Times Cited: 12
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