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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-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse
Tao Bo, Yaohui Lin, Jinglong Han, et al.
Journal of Hazardous Materials (2023) Vol. 452, pp. 131344-131344
Closed Access | Times Cited: 19

Showing 19 citing articles:

From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization
Yutong Wang, Peng Wang, Tengjiao Fan, et al.
Journal of Hazardous Materials (2024) Vol. 476, pp. 134945-134945
Closed Access | Times Cited: 17
Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment
Vaisali Chandrasekar, Saad Mohammad, Omar M. Aboumarzouk, et al.
Journal of Hazardous Materials (2025) Vol. 487, pp. 137071-137071
Closed Access | Times Cited: 6
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Changsen Bai, Lianlian Wu, Ruijiang Li, et al.
Advanced Science (2025)
Open Access | Times Cited: 1
ToxMPNN: A deep learning model for small molecule toxicity prediction
Yini Zhou, Chao Ning, Yijun Tan, et al.
Journal of Applied Toxicology (2024) Vol. 44, Iss. 7, pp. 953-964
Closed Access | Times Cited: 6
Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds
Ying He, Guohong Liu, Song Hu, et al.
Journal of Hazardous Materials (2023) Vol. 458, pp. 131942-131942
Closed Access | Times Cited: 14
Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters
Yuhang Chen, Mengyuan Li, Weichun Gao, et al.
Journal of Environmental Sciences (2023) Vol. 146, pp. 251-263
Closed Access | Times Cited: 14
A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity
Konda Mani Saravanan, Jiang-Fan Wan, Liujiang Dai, et al.
Methods (2024) Vol. 226, pp. 164-175
Closed Access | Times Cited: 5
Multi-task multi-view and iterative error-correcting random forest for acute toxicity prediction
Jie Gao, Lianlian Wu, Guangyi Lin, et al.
Expert Systems with Applications (2025), pp. 126972-126972
Closed Access
Prediction of acute toxicity of organic contaminants to fish: model development and a novel approach to identify reactive substructures
Shangyu Li, Mingming Zhang, Peizhe Sun
Journal of Hazardous Materials (2025) Vol. 491, pp. 137917-137917
Closed Access
Fabrication of D-α-tocopheryl polyethylene glycol 1000 succinates and human serum albumin conjugated chitosan nanoparticles of bosutinib for colon targeting application; in vitro-in vivo investigation
Laxmi Manthalkar, Sankha Bhattacharya, Ketan Hatware, et al.
International Journal of Biological Macromolecules (2023) Vol. 253, pp. 127531-127531
Closed Access | Times Cited: 7
Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment
Jiajia Wei, Lei Tian, Fan Nie, et al.
Heliyon (2024) Vol. 10, Iss. 5, pp. e26808-e26808
Open Access | Times Cited: 2
In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning
Sebastian Schieferdecker, Florian Rottach, Esther Vock
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3114-3122
Closed Access | Times Cited: 2
Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives
Rehab Ali Ibrahim, Mohamed Aly Saad Aly, Yasmine S. Moemen, et al.
Chemosphere (2024) Vol. 359, pp. 142362-142362
Closed Access | Times Cited: 2
Using the Super-learner to Predict the Chemical Acute Toxicity on Rats
Yuzhe Li, Bixuan Wang, Fujun Ma, et al.
Journal of Hazardous Materials (2024) Vol. 480, pp. 136311-136311
Closed Access | Times Cited: 2
Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy
Areum Sohn, Jongham Park, Woojin Kim, et al.
Toxics (2024) Vol. 12, Iss. 11, pp. 822-822
Open Access | Times Cited: 1
Noncovalent Tagging for Identifying Unknown Contaminants of Specific Bioactivity in Environmental Water
Hongyu Wen, Minhui Shen, Zhisheng Lai, et al.
Analytical Chemistry (2023) Vol. 95, Iss. 43, pp. 15851-15855
Closed Access | Times Cited: 2
Theoretical Prediction Model for the Acute Toxicity of Aromatic Hydrocarbons and Pesticides Compounds to Selenastrum capricornutum
Yue Xu, Mi‐Kyung Song, Yuan Chen, et al.
Russian Journal of General Chemistry (2024) Vol. 94, Iss. 4, pp. 914-921
Closed Access
Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds
Xiudi Lv, Mei He, Jiajia Wei, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 34, pp. 47220-47236
Closed Access
A Multiscale-Information-Embedded Universal Toxicity Prediction Framework
Song He, Lianlian Wu, Fanmeng Wang, et al.
Research Square (Research Square) (2024)
Closed Access
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