Shinhyuk Kim, Daeyong Jin and Hyunju Lee (2013) Predicting drug-target interactions using drug-drug interactions. PLoS One. 8(11): e80129. (IF: 3.730).

Predicting drug-target interactions using drug-drug interactions.



Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side e ffects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse eff ects (DDIAE) and pharmacological information (DDIPharm), and investigate the relationship among chemical structures, side eff ects, and DDIs from several data sources. In this study, DDIPharm data from the STITCH database, DDIAE from, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side e ffects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.


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