Using a Human Drug Network for generating novel hypotheses about drugs

Analyzing different drugs for various purposes is an important issue in the area of computational biology. We categorize the previous computational studies into Individual and Network approaches. While the Individual approach focuses on one specific drug without considering its relationship with other drugs, the Network approach considers also the drugs relationships. In this paper, we apply a Network approach, previously proposed for discovering the relationships among diseases, to drug data. We construct a Human Drug Network (HDN) for 200 different drugs based on functional and structural information available in the PPI network. For evaluating our proposed HDN, first, we analyzed the literature to prove that the proposed HDN is biologically meaningful. Second, we used the HDN to augment the initial prior knowledge of different drugs. As an example of prior knowledge, we considered the initial seed proteins (a set of proteins which are previously known to be drug targets) of each drug. We clustered the HDN nodes using the Markov CLustering Algorithm (MCL) and then, we augmented the seed proteins of each drug based on the cluster it belongs to. In the end, we concluded that our proposed HDN enables us to generate novel hypotheses (in terms of potential drug target proteins) and produce complementary results comparing to existing methods.