Prediction of cancer drugs by chemical-chemical interactions.
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.
Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Yantai, Shandong, People's Republic of China.
Jing
J
Lu
Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China ; Department of Mathematics, Shaoyang University, Shaoyang, Hunan, People's Republic of China.
Guohua
G
Huang
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
Hai-Peng
HP
Li
Beijing Genomics Institute, Shenzhen Beishan Industrial zone, Shenzhen, People's Republic of China.
Kai-Yan
KY
Feng
College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
Lei
L
Chen
State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai, People's Republic of China.
Ming-Yue
MY
Zheng
Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China.
Yu-Dong
YD
Cai
D000970:Antineoplastic Agents / Q000494:pharmacology*; D004347:Drug Interactions; D006801:Humans; D048088:Informatics / Q000379:methods*; D008962:Models, Theoretical; D009369:Neoplasms / Q000188:drug therapy*
D000970:Antineoplastic Agents