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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-Based Models with High Accuracy and Broad Applicability Domains for Screening PMT/vPvM Substances
Qiming Zhao, Yang Yu, Yuchen Gao, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 24, pp. 17880-17889
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

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: 32
MatGPT: A Vane of Materials Informatics from Past, Present, to Future
Zhilong Wang, An Chen, Kehao Tao, et al.
Advanced Materials (2023) Vol. 36, Iss. 6
Closed Access | Times Cited: 30
Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances
Qiming Zhao, Yuting Zheng, Yu Qiu, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 14, pp. 6149-6157
Closed Access | Times Cited: 10
Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review
Zhuang Liu, Yonghai Gan, Jun Luo, et al.
Water (2025) Vol. 17, Iss. 1, pp. 85-85
Open Access | Times Cited: 1
Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics
Wenjia Liu, Zhongyu Wang, Jingwen Chen, et al.
Chemical Research in Toxicology (2023) Vol. 36, Iss. 6, pp. 947-958
Closed Access | Times Cited: 18
Occurrence and Ecological Risk of Alkylamine Triazines in Chinese Estuarine Sediments: An Emerging Class of Persistent, Mobile, and Toxic Substances
Pengyang Li, Wenyuan Su, Laijin Zhong, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 15, pp. 6814-6824
Closed Access | Times Cited: 7
Integrated Transfer Learning and Multitask Learning Strategies to Construct Graph Neural Network Models for Predicting Bioaccumulation Parameters of Chemicals
Zijun Xiao, Minghua Zhu, Jingwen Chen, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 35, pp. 15650-15660
Closed Access | Times Cited: 6
Physics-informed neural network-based serial hybrid model capturing the hidden kinetics for sulfur-driven autotrophic denitrification process
Xu Zou, Hongxiao Guo, Chu-Kuan Jiang, et al.
Water Research (2023) Vol. 243, pp. 120331-120331
Closed Access | Times Cited: 15
Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure
Han Min, Biao Jin, Jun Liang, et al.
Water Research (2023) Vol. 244, pp. 120470-120470
Closed Access | Times Cited: 14
Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points
Haobo Wang, Wenjia Liu, Jingwen Chen, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 44, pp. 16906-16917
Closed Access | Times Cited: 14
Variable KOC and Poor-Quality Data Sources Cause High Discrepancy in Current Mobility Assessment of Organic Substances
Fu Liu, Fan Fan, Qingmiao Yu, et al.
ACS ES&T Water (2025)
Closed Access
Entropy Similarity-Driven Transformation Reaction Molecular Networking Reveals Transformation Pathways and Potential Risks of Emerging Contaminants in Wastewater: The Example of Sartans
Yuli Qian, Yunhao Ke, Liye Wang, et al.
Environmental Science & Technology (2025)
Closed Access
New trend on chemical structure representation learning in toxicology: In reviews of machine learning model methodology
Jiabin Zhang, Lei Zhao, Wei Wang, et al.
Critical Reviews in Environmental Science and Technology (2025), pp. 1-26
Closed Access
Artificial intelligence: A key fulcrum for addressing complex environmental health issues
Lei Huang, Qiannan Duan, Yuxin Liu, et al.
Environment International (2025), pp. 109389-109389
Open Access
Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARĪ³) IC50 inhibition by emerging pollutants using light gradient boosting machine
Awomuti Adeboye, Zhen Yu, Adesina Odunayo Blessing, et al.
SAR and QSAR in environmental research (2025), pp. 1-23
Closed Access
The Active Soil Layer of Thawing Permafrost Is an Emergent Source for Organic Substances of Concern to Water Resources
Min Han, Biao Jin, Hans Peter H. Arp
Environmental Science & Technology Letters (2025)
Open Access
Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water
Han Min, Jun Liang, Biao Jin, et al.
iScience (2024) Vol. 27, Iss. 2, pp. 109012-109012
Open Access | Times Cited: 3
Structure-activity relationship study on the phytotoxicity of polyether lubricants: Experimental investigation and theoretical prediction based on machine learning models
Ying Wang, Yuhua Song, Hanwen Wang, et al.
Journal of Molecular Liquids (2025), pp. 127527-127527
Closed Access
Exploration of Chemical Space Covered by Nontarget Screening Based on the Prediction of Chemical Substances Amenable to LC-HRMS Analysis
Xiang Huang, Wangjing Zhai, Wenyuan Su, et al.
Environmental Science & Technology Letters (2025)
Closed Access
Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches
Xiaoqing Wang, Fei Li, Yuefa Teng, et al.
The Science of The Total Environment (2023) Vol. 871, pp. 162103-162103
Closed Access | Times Cited: 7
Development and Application of Machine Learning Models for Prediction of Soil Available Cadmium Based on Soil Properties and Climate Features
Zhihui Yang, Hui Xia, Ziyun Guo, et al.
Environmental Pollution (2024) Vol. 355, pp. 124148-124148
Closed Access | Times Cited: 2
Machine learning-assisted identification of environmental pollutants by liquid chromatography coupled with high-resolution mass spectrometry
Haotian Wang, Laijin Zhong, Wenyuan Su, et al.
TrAC Trends in Analytical Chemistry (2024), pp. 117988-117988
Closed Access | Times Cited: 2
PBT assessment of chemicals detected in effluent of wastewater treatment plants by suspected screening analysis
Gang Wu, Feng Zhu, Xuxiang Zhang, et al.
Environmental Research (2023) Vol. 237, pp. 116892-116892
Closed Access | Times Cited: 4
Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split
Chao Chen, Zhengliang Huang, Xuyan Zou, et al.
The Science of The Total Environment (2024) Vol. 917, pp. 170435-170435
Closed Access | Times Cited: 1
Determining high priority disinfection byproducts based on experimental aquatic toxicity data and predictive models: Virtual screening and in vivo study
Nan Zhou, Shuxin Sui, H. Liu, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 175489-175489
Closed Access | Times Cited: 1
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