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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:

Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS
Chao Feng, Qian Xu, Xinlei Qiu, et al.
Chemosphere (2020) Vol. 271, pp. 129447-129447
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification
Jun Xue, Bingyi Wang, Hongchao Ji, et al.
Bioinformatics (2024) Vol. 40, Iss. 3
Open Access | Times Cited: 17
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: 9
Beyond target chemicals: updating the NORMAN prioritisation scheme to support the EU chemicals strategy with semi-quantitative suspect/non-target screening data
Valeria Dulio, ‪Nikiforos Alygizakis, Kelsey Ng, et al.
Environmental Sciences Europe (2024) Vol. 36, Iss. 1
Open Access | Times Cited: 9
Prediction of Retention Indices in LC-HRMS for Enhanced Structural Identification of Organic Micropollutants in Water: Selectivity-Based Filtration
Ardiana Kajtazi, Marin Kajtazi, Maike Felipe Santos Barbetta, et al.
Analytical Chemistry (2025) Vol. 97, Iss. 1, pp. 65-74
Closed Access | Times Cited: 1
Retention Time Prediction with Message-Passing Neural Networks
Sergey Osipenko, Е. Н. Николаев, Yury Kostyukevich
Separations (2022) Vol. 9, Iss. 10, pp. 291-291
Open Access | Times Cited: 31
Using machine learning to predict the efficiency of biochar in pesticide remediation
Amrita Nighojkar, Shilpa Pandey, Minoo Naebe, et al.
npj Sustainable Agriculture (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 20
New Revolution for Quality Control of TCM in Industry 4.0: Focus on Artificial Intelligence and Bioinformatics
Yaolei Li, Jing Fan, Xian‐Long Cheng, et al.
TrAC Trends in Analytical Chemistry (2024), pp. 118023-118023
Open Access | Times Cited: 7
Developments in high-resolution mass spectrometric analyses of new psychoactive substances
Joshua Klingberg, Bethany Keen, Adam Cawley, et al.
Archives of Toxicology (2022) Vol. 96, Iss. 4, pp. 949-967
Open Access | Times Cited: 23
Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction
Dehao Song, Ting Tang, Rui Wang, et al.
Environmental Pollution (2024) Vol. 347, pp. 123763-123763
Closed Access | Times Cited: 6
Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening
Henrik Hupatz, Ida Rahu, Wei‐Chieh Wang, et al.
Analytical and Bioanalytical Chemistry (2024)
Open Access | Times Cited: 4
Enhanced database creation with in silico workflows for suspect screening of unknown tebuconazole transformation products in environmental samples by UHPLC-HRMS
Kevin Rocco, C. Margoum, Loïc Richard, et al.
Journal of Hazardous Materials (2022) Vol. 440, pp. 129706-129706
Open Access | Times Cited: 18
Profiling of pesticides and pesticide transformation products in Chinese herbal teas
Chao Feng, Xu Qian, Xinlei Qiu, et al.
Food Chemistry (2022) Vol. 383, pp. 132431-132431
Closed Access | Times Cited: 17
Optimal machine learning algorithm for prediction model for retention times of plant toxins
Masaru Taniguchi, Shoichiro Noguchi, Hideyuki Kawashima, et al.
Food Control (2025), pp. 111251-111251
Closed Access
The Role and Choice of Molecular Descriptors for Predicting Retention Times in HPLC: A Comprehensive Review
Elena Bandini, Ardiana Kajtazi, Roman Szücs, et al.
TrAC Trends in Analytical Chemistry (2025), pp. 118207-118207
Closed Access
ZHPO-LightXBoost An Integrated Prediction Model Based on Small Samples for Pesticide Residues in Crops
X. Sha, Yong-Zhe Zhu, X. Sha, et al.
Environmental Modelling & Software (2025), pp. 106440-106440
Closed Access
Insights into Surface and Chemical Interactions in Electrochemical Probing of Diazinon: A Comprehensive Review on Current Trends, Challenges, and Perspectives
Buchi Reddy Gari Hema Sai, G. Shanthi Priya, Mohammed Suhaib Al Huq, et al.
Trends in Environmental Analytical Chemistry (2025), pp. e00269-e00269
Closed Access
Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning
Yunwu Fan, Yu Deng, Yi Yang, et al.
Environmental Science Advances (2023) Vol. 3, Iss. 2, pp. 198-207
Open Access | Times Cited: 8
Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms
Amrita Nighojkar, Jyoti Nagpal, Winston O. Soboyejo, et al.
Environmental Monitoring and Assessment (2023) Vol. 195, Iss. 8
Closed Access | Times Cited: 7
Deep graph convolutional network for small-molecule retention time prediction
Qiyue Kang, Pengfei Fang, Shuai Zhang, et al.
Journal of Chromatography A (2023) Vol. 1711, pp. 464439-464439
Closed Access | Times Cited: 6
Global Xenobiotic Profiling of Rat Plasma Using Untargeted Metabolomics and Background Subtraction-Based Approaches: Method Evaluation and Comparison
Xiaojuan Jiang, Simian Chen, Mingshe Zhu, et al.
Current Drug Metabolism (2023) Vol. 24, Iss. 3, pp. 200-210
Closed Access | Times Cited: 5
The surveillance and prediction of food contamination using intelligent systems: a bibliometric analysis
Kgomotso Lebelo, Muthoni Masinde, Ntsoaki Joyce Malebo, et al.
British Food Journal (2021) Vol. 124, Iss. 4, pp. 1149-1169
Closed Access | Times Cited: 11
Changes in Microbial Diversity, Soil Function, and Plant Biomass of Cotton Rhizosphere Soil Under the Influence of Chlorpyrifos
Xiaobing Wang, Jian Wang, Yaping Wang, et al.
Current Microbiology (2022) Vol. 79, Iss. 11
Closed Access | Times Cited: 8
In Silico Supported Nontarget Analysis of Contaminants of Emerging Concern: Increasing Confidence in Unknown Identification in Wastewater and Surface Waters
Luisa F. Angeles, Lahiruni M. Halwatura, Jonathan P. Antle, et al.
ACS ES&T Water (2021) Vol. 1, Iss. 8, pp. 1765-1775
Closed Access | Times Cited: 9
RT-Tranformer: Retention Time Prediction for Metabolite Annotation to Assist in Metabolite Identification
Jun Xue, Bingyi Wang, Weihua Li
(2023)
Open Access | Times Cited: 2
Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics
Miao Tian, Zhonglong Lin, Xu Wang, et al.
Molecules (2021) Vol. 26, Iss. 9, pp. 2715-2715
Open Access | Times Cited: 4
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