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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:

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity
Jennifer Hemmerich, Florentina Troger, Barbara Füzi, et al.
Molecular Informatics (2020) Vol. 39, Iss. 5
Open Access | Times Cited: 52

Showing 1-25 of 52 citing articles:

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications
Jaeseong Jeong, Jinhee Choi
Environmental Science & Technology (2022) Vol. 56, Iss. 12, pp. 7532-7543
Closed Access | Times Cited: 81
An AI‐based Prediction Model for Drug‐drug Interactions in Osteoporosis and Paget's Diseases from SMILES
Truong Nguyen Khanh Hung, Nguyen Quoc Khanh Le, Ngoc Hoang Le, et al.
Molecular Informatics (2022) Vol. 41, Iss. 6
Closed Access | Times Cited: 75
Opportunities and challenges in application of artificial intelligence in pharmacology
Mandeep Kumar, T. P. Nhung Nguyen, Jasleen Kaur, et al.
Pharmacological Reports (2023) Vol. 75, Iss. 1, pp. 3-18
Open Access | Times Cited: 44
Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank
Srijit Seal, Ola Spjuth, Layla Hosseini-Gerami, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 4, pp. 1172-1186
Open Access | Times Cited: 18
FP-ADMET: a compendium of fingerprint-based ADMET prediction models
Vishwesh Venkatraman
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 80
Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection
Srijit Seal, Jordi Carreras‐Puigvert, Maria‐Anna Trapotsi, et al.
Communications Biology (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 65
Oxidative Stress as a Common Key Event in Developmental Neurotoxicity
Yuhei Nishimura, Yasunari Kanda, Hideko Sone, et al.
Oxidative Medicine and Cellular Longevity (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 58
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: 10
Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data
Srijit Seal, Dominic P. Williams, Layla Hosseini-Gerami, et al.
Chemical Research in Toxicology (2024) Vol. 37, Iss. 8, pp. 1290-1305
Open Access | Times Cited: 10
Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature
Maria‐Anna Trapotsi, Elizabeth Mouchet, Guy Williams, et al.
ACS Chemical Biology (2022) Vol. 17, Iss. 7, pp. 1733-1744
Open Access | Times Cited: 35
Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data
Marina García de Lomana, Paula Andrea Marin Zapata, Floriane Montanari
Chemical Research in Toxicology (2023) Vol. 36, Iss. 7, pp. 1107-1120
Open Access | Times Cited: 21
Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks
Edoardo Luca Viganò, Davide Ballabio, Alessandra Roncaglioni
Toxics (2024) Vol. 12, Iss. 1, pp. 87-87
Open Access | Times Cited: 7
Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience
Martijn J. Moné, Giorgia Pallocca, Sylvia E. Escher, et al.
Archives of Toxicology (2020) Vol. 94, Iss. 10, pp. 3581-3592
Open Access | Times Cited: 46
Improved Detection of Drug-Induced Liver Injury by Integrating Predictedin vivoandin vitroData
Srijit Seal, Dominic P. Williams, Layla Hosseini-Gerami, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
Curating Benzothiophene Experimental Absorption and Emission Spectra to Design Fluorescent Organic Polymer Chemical Space: A Machine Learning Quest
Shaimaa Hassan Mallah, Azal Shakir Waheeb, Abrar U. Hassan, et al.
Journal of Fluorescence (2025)
Closed Access
Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms
Guangyin Jia, Ruiji Zhang, Xinyi Zheng, et al.
Toxicology Mechanisms and Methods (2025), pp. 1-27
Closed Access
Rational application of QoIs fungicides to achieve a rice-fungi-fish interaction balance within paddy ecosystems
Hong Li, Xueqing Wang, Beixing Li, et al.
Environmental Pollution (2025), pp. 126169-126169
Closed Access
A reverse engineering descriptor tokenization machine learning analysis to design new triphenylamine chromophores with tunable absorption/emission
Mamduh J. Aljaafreh, Sajjad Hussain Sumrra, Sadaf Noreen
Materials Today Communications (2025), pp. 112632-112632
Closed Access
In silico prediction of mitochondrial toxicity of chemicals using machine learning methods
Piaopiao Zhao, Yayuan Peng, Xuan Xu, et al.
Journal of Applied Toxicology (2021) Vol. 41, Iss. 10, pp. 1518-1526
Closed Access | Times Cited: 26
In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review
Hyun Kil Shin, Ruili Huang, Minjun Chen
Food and Chemical Toxicology (2023) Vol. 179, pp. 113948-113948
Open Access | Times Cited: 10
Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method
Yoshinobu Igarashi, Ryosuke Kojima, Shigeyuki Matsumoto, et al.
The Journal of Toxicological Sciences (2024) Vol. 49, Iss. 3, pp. 117-126
Open Access | Times Cited: 3
Unleashing the potential of cell painting assays for compound activities and hazards prediction
Floriane Odje, David Meijer, Elena von Coburg, et al.
Frontiers in Toxicology (2024) Vol. 6
Open Access | Times Cited: 3
Predicting the new psychoactive substance activity of antitussives and evaluating their ecotoxicity to fish
Wenjun Shi, Xiao-Bing Long, Lei Xin, et al.
The Science of The Total Environment (2024) Vol. 932, pp. 172872-172872
Closed Access | Times Cited: 2
Identification of mitochondrial toxicants by combined in silico and in vitro studies – A structure-based view on the adverse outcome pathway
Florentina Troger, Johannes Delp, Melina Funke, et al.
Computational Toxicology (2020) Vol. 14, pp. 100123-100123
Open Access | Times Cited: 18
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