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Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions

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Listed:
  • Lei Chen
  • Jing Lu
  • Jian Zhang
  • Kai-Rui Feng
  • Ming-Yue Zheng
  • Yu-Dong Cai

Abstract

Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1st order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.

Suggested Citation

  • Lei Chen & Jing Lu & Jian Zhang & Kai-Rui Feng & Ming-Yue Zheng & Yu-Dong Cai, 2013. "Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
  • Handle: RePEc:plo:pone00:0056517
    DOI: 10.1371/journal.pone.0056517
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    References listed on IDEAS

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    1. Lei Chen & Wei-Ming Zeng & Yu-Dong Cai & Kai-Yan Feng & Kuo-Chen Chou, 2012. "Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    2. Martín Gómez Ravetti & Pablo Moscato, 2008. "Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-12, September.
    3. Yiannis A I Kourmpetis & Aalt D J van Dijk & Marco C A M Bink & Roeland C H J van Ham & Cajo J F ter Braak, 2010. "Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
    4. Bi-Qing Li & Le-Le Hu & Lei Chen & Kai-Yan Feng & Yu-Dong Cai & Kuo-Chen Chou, 2012. "Prediction of Protein Domain with mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    5. Lele Hu & Tao Huang & Xiaohe Shi & Wen-Cong Lu & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    6. Bi-Qing Li & Yu-Dong Cai & Kai-Yan Feng & Gui-Jun Zhao, 2012. "Prediction of Protein Cleavage Site with Feature Selection by Random Forest," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
    7. Le-Le Hu & Chen Chen & Tao Huang & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-9, December.
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    Cited by:

    1. Lei Chen & Jing Lu & Tao Huang & Yu-Dong Cai, 2017. "A computational method for the identification of candidate drugs for non-small cell lung cancer," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    2. Somin Wadhwa & Aishwarya Gupta & Shubham Dokania & Rakesh Kanji & Ganesh Bagler, 2018. "A hierarchical anatomical classification schema for prediction of phenotypic side effects," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-15, March.

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