Abstract
In recently years, because the quantity of drug and human protein information increase quickly, the nova drug development has to face to large possible drug-target experiment pair. Therefore, the in silico prediction method become important step in drug development to cut down the cost and have raised much attention. Through those prediction methods that we can filter out the possible drug-target pair before actually conducting biological experiments or even human test. We proposed a new method based on network method and machine learning method to predict high probability interactional drug-target pair. Furthermore, we also want to find out the associations between the features of drug and features of target. We use the data integrated by Yoshihiro Yamanishi(et al.,2010) for comfortably to compare accuracy. In the result of experiment, we construct a bipartite network of both the chemical structure and protein domain association networks. The accuracy performance of our method approaches to that of the best method very closely. This result can provide confidence that our c association network actually helps in revealing the association between the two heterogeneous data features.