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Prediction of cancer drugs by chemical-chemical interactions.

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 1(st) 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.

SUBMITTER: Lu J

PROVIDER: S-EPMC3912061 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Prediction of cancer drugs by chemical-chemical interactions.

Lu Jing J Huang Guohua G Li Hai-Peng HP Feng Kai-Yan KY Chen Lei L Zheng Ming-Yue MY Cai Yu-Dong YD

PloS one 20140203 2

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 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.5 ...[more]

PMID: 24498372

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