Background: Identification of drug-target interaction is essential in drug discovery. It is
beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several
computational methods have been proposed to predict drug-target interactions because they are
prompt and low-cost compared with traditional wet experiments.
Methods: In this study, we investigated this problem in a different way. According to KEGG,
drugs were classified into several groups based on their target proteins. A multi-label classification
model was presented to assign drugs into correct target groups. To make full use of the known drug
properties, five networks were constructed, each of which represented drug associations in one
property. A powerful network embedding method, Mashup, was adopted to extract drug features
from above-mentioned networks, based on which several machine learning algorithms, including
RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector
Machine (SVM), were used to build the classification model.
Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of
0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of
each network was also analyzed. Furthermore, the network model with multiple networks was
found to be superior to the one with a single network and classic model, indicating the superiority
of the proposed model.