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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 to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List
Weixiao Cheng, Carla A. Ng
Environmental Science & Technology (2019) Vol. 53, Iss. 23, pp. 13970-13980
Open Access | Times Cited: 101

Showing 26-50 of 101 citing articles:

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)
Hyuna Kwon, Zulfikhar A. Ali, Bryan M. Wong
Environmental Science & Technology Letters (2022) Vol. 10, Iss. 11, pp. 1017-1022
Open Access | Times Cited: 36
Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation
Huiming Cao, Peng Jian-hua, Zhen Zhou, et al.
Environmental Science & Technology (2022) Vol. 57, Iss. 46, pp. 17762-17773
Closed Access | Times Cited: 30
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: 29
Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors
Ettayapuram Ramaprasad Azhagiya Singam, Kathleen A. Durkin, Michele A. La Merrill, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 10, pp. 4487-4499
Open Access | Times Cited: 7
Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park
Deji Jing, Kexuan Yang, Zhanhong Shi, et al.
Heliyon (2024) Vol. 10, Iss. 8, pp. e29077-e29077
Open Access | Times Cited: 7
Quantitative cross-species comparison of serum albumin binding of per- and polyfluoroalkyl substances from five structural classes
Hannah M. Starnes, Thomas W. Jackson, Kylie D. Rock, et al.
Toxicological Sciences (2024) Vol. 199, Iss. 1, pp. 132-149
Open Access | Times Cited: 6
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
Molecular Screening and Toxicity Estimation of 260,000 Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) through Machine Learning
Thanh T. Lai, David Kuntz, Angela K. Wilson
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 19, pp. 4569-4578
Closed Access | Times Cited: 25
Comprehensive Exposure Studies of Per- and Polyfluoroalkyl Substances in the General Population: Target, Nontarget Screening, and Toxicity Prediction
Laihui Li, Nanyang Yu, Xuebin Wang, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 20, pp. 14617-14626
Closed Access | Times Cited: 25
Warming Affects Bioconcentration and Bioaccumulation of Per- and Polyfluoroalkyl Substances by Pelagic and Benthic Organisms in a Water–Sediment System
Haotian Wang, Diexuan Hu, Wu Wen, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 9, pp. 3612-3622
Closed Access | Times Cited: 13
Prediction of 35 Target Per- and Polyfluoroalkyl Substances (PFASs) in California Groundwater Using Multilabel Semisupervised Machine Learning
Jialin Dong, Gabriel Tsai, Christopher I. Olivares
ACS ES&T Water (2023) Vol. 4, Iss. 3, pp. 969-981
Open Access | Times Cited: 13
Unveiling Priority Emerging PFAS in Taihu Lake Using Integrated Nontarget Screening, Target Analysis, and Risk Characterization
Yao Fu, Yuyan Ji, Yawen Tian, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 42, pp. 18980-18991
Closed Access | Times Cited: 5
Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment
Md Hasan-Ur Rahman, Rabbi Sikder, Tanvir Ahamed Tonmoy, et al.
Journal of Water Process Engineering (2025) Vol. 70, pp. 106941-106941
Closed Access
Advancing PFAS Remediation through Physics-Based Modeling of 2D Materials: Recent Progress, Challenges, and Opportunities
Monzure-Khoda Kazi, Sunith Varghese, Nahid Sarker, et al.
Industrial & Engineering Chemistry Research (2025)
Closed Access
Screening Estimates of Bioaccumulation Factors for 4950 Per- and Polyfluoroalkyl Substances in Aquatic Species
Qi Wang, Bixuan Wang, Ting Hou, et al.
Journal of Hazardous Materials (2025) Vol. 489, pp. 137672-137672
Closed Access
Predicting Mito-Target Interactions for Per-and Poly-Fluoroalkyl Compounds: Mapping Mitochondrial Toxicity on Zebrafish Voltage-Dependent Anion Channel 2
Michael González‐Durruthy, Amit Kumar Halder, Ana S. Moura, et al.
Aquatic Toxicology (2025) Vol. 281, pp. 107302-107302
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
Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation
Zhipeng Yin, Min Zhang, Runzeng Liu, et al.
Water Research (2025), pp. 123500-123500
Closed Access
Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning
Ettayapuram Ramaprasad Azhagiya Singam, Phum Tachachartvanich, Denis Fourches, et al.
Environmental Research (2020) Vol. 190, pp. 109920-109920
Open Access | Times Cited: 35
Interpretation of Reductive PFAS Defluorination with Quantum Chemical Parameters
Zhiwen Cheng, Qincheng Chen, Zekun Liu, et al.
Environmental Science & Technology Letters (2021) Vol. 8, Iss. 8, pp. 645-650
Closed Access | Times Cited: 31
Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity
Jeremy Feinstein, Ganesh Sivaraman, Kurt Picel, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 12, pp. 5793-5803
Open Access | Times Cited: 27
Mechanistic insights into the removal of PFOA by 2D MXene/CNT membrane with the influence of Ca2+ and humic acid
Hang Xu, Jun Ma, Mingmei Ding, et al.
Desalination (2022) Vol. 529, pp. 115643-115643
Closed Access | Times Cited: 22
Global classification models for predicting acute toxicity of chemicals towards Daphnia magna
Xinliang Yu
Environmental Research (2023) Vol. 238, pp. 117239-117239
Closed Access | Times Cited: 12
Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach
Kazuhiro Maeda, Masashi Hirano, Taka Hayashi, et al.
Environmental Science & Technology (2023) Vol. 58, Iss. 1, pp. 488-497
Closed Access | Times Cited: 11
An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening
An Su, Yingying Cheng, Chengwei Zhang, et al.
The Science of The Total Environment (2024) Vol. 921, pp. 171229-171229
Closed Access | Times Cited: 4

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