BetaCitation Atlas

RESEARCH PAPER · The Journal of Chemical Physics · 2022

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Read original paper ↗

See where this research leads.

9 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernelsOriginalChemical design by artificial intelligence5A global biological conservation horizon scan of issues for 202336Advancing Computational Toxicology by Interpretable Machine Learning183Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules47Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives20Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction9BeeEcoTox: A Large Language Model-Empowered Graph-Learning Framework for Predicting Ecotoxicity of Agrochemicals to Bees0Machine learning prediction of soil adsorption coefficients for industry-related aromatic contaminants: Molecular drivers and global distribution insights0A SAR-Based Classification Model for Assessing Pesticide Toxicity to Apis mellifera0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Browse Matching Papers

Papers that cite this work

9 direct citing papers saved.

  1. Chemical design by artificial intelligence

    The Journal of Chemical Physics · 2022-09-28

    Daniel H. Ess, Kim E. Jelfs, Heather J. Kulik

    Read original paper ↗ ·
  2. A global biological conservation horizon scan of issues for 2023

    Trends in Ecology & Evolution · 2022-11-29

    William J. Sutherland, Craig Bennett, Peter N. M. Brotherton, Holly M. Butterworth, Mick N. Clout, Isabelle M. Côté, Jason Dinsdale, Nafeesa Esmail, Erica Fleishman, Kevin J. Gaston, James E. Herbert‐Read, Alice Catherine Hughes, Hermanni Kaartokallio, Xavier Le Roux, Fiona A. Lickorish, Wendy Matcham, Noor Noor, James E. Palardy, James W. Pearce‐Higgins, Lloyd Samuel Peck, Nathalie Pettorelli, Jules Pretty, Richard Scobey, Mark D. Spalding, Femke H. Tonneijck, Nicolas Tubbs, James E. M. Watson, Jonathan Wentworth, Jeremy D. Wilson, Ann Thornton

    Read original paper ↗ ·
  3. Advancing Computational Toxicology by Interpretable Machine Learning

    Environmental Science & Technology · 2023-05-24

    Xuelian Jia, Tong Wang, Hao Zhu

    Read original paper ↗ ·
  4. Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

    Chemical Science · 2023-12-05

    Zikai Xie, Xenophon Evangelopoulos, Ömer H. Omar, Alessandro Troisi, Andrew Ian Cooper, Linjiang Chen

    Read original paper ↗ ·
  5. Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

    Expert Opinion on Drug Metabolism & Toxicology · 2023-12-23

    Maria Vittoria Togo, Fabrizio Mastrolorito, Angelica Orfino, Elisabetta Anna Graps, Anna Rita Tondo, Cosimo Altomare, Fulvio Ciriaco, Daniela Trisciuzzi, Orazio Nicolotti, Nicola Amoroso

    Read original paper ↗ ·
  6. Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction

    Journal of Hazardous Materials · 2024-06-12

    Xinkang Li, Feng Zhang, Liangzhen Zheng, Jingjing Guo

    Read original paper ↗ ·
  7. BeeEcoTox: A Large Language Model-Empowered Graph-Learning Framework for Predicting Ecotoxicity of Agrochemicals to Bees

    Environmental Science & Technology · 2026-04-15

    Xuanlin Chen, Lilai Shen, Y N Huang, Yuchen Gao, Ke Chen, Dirong Zhang, Jianing Liu, Jinhui Yuan, Shulin Zhuang

    Read original paper ↗ ·
  8. Machine learning prediction of soil adsorption coefficients for industry-related aromatic contaminants: Molecular drivers and global distribution insights

    Journal of Hazardous Materials · 2026-06-02

    Liman Wei, Peiyu Cao, Siyuan Li, Xiang Zhang, Qiuyue Zhang, Huatai Song, Pan Liu, Xiangyu Chen, Hao Chen, Jiaping Xu, Yiming Yao, Yu Wang (12152), Cuiping Wang, Hongwen Sun

    Read original paper ↗ ·
  9. A SAR-Based Classification Model for Assessing Pesticide Toxicity to Apis mellifera

    Journal of Xenobiotics · 2026-07-11

    Nadia Iovine, Anna Lombardo, Alessandra Roncaglioni, Emilio Benfenati

    Read original paper ↗ ·