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Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

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  • Lei Chen
  • Wei-Ming Zeng
  • Yu-Dong Cai
  • Kai-Yan Feng
  • Kuo-Chen Chou

Abstract

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0035254
    DOI: 10.1371/journal.pone.0035254
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Guohua Huang & Yuchao Zhang & Lei Chen & Ning Zhang & Tao Huang & Yu-Dong Cai, 2014. "Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    2. Lei Chen & Jing Yang & Zhihao Xing & Fei Yuan & Yang Shu & YunHua Zhang & XiangYin Kong & Tao Huang & HaiPeng Li & Yu-Dong Cai, 2017. "An integrated method for the identification of novel genes related to oral cancer," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-25, April.
    3. 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.
    4. 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.
    5. Yu-Fei Gao & Lei Chen & Yu-Dong Cai & Kai-Yan Feng & Tao Huang & Yang Jiang, 2012. "Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.

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