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Prediction of Cancer Drugs by Chemical-Chemical Interactions

Author

Listed:
  • Jing Lu
  • Guohua Huang
  • Hai-Peng Li
  • Kai-Yan Feng
  • Lei Chen
  • Ming-Yue Zheng
  • Yu-Dong Cai

Abstract

Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1st order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the ‘wrong’ predicted indications, indicating that some ‘wrong’ drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.

Suggested Citation

  • Jing Lu & Guohua Huang & Hai-Peng Li & Kai-Yan Feng & Lei Chen & Ming-Yue Zheng & Yu-Dong Cai, 2014. "Prediction of Cancer Drugs by Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0087791
    DOI: 10.1371/journal.pone.0087791
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